diff --git a/.travis.yml b/.travis.yml new file mode 100644 index 0000000..e6f1a0c --- /dev/null +++ b/.travis.yml @@ -0,0 +1,8 @@ +language: python +python: + - "2.7" +# command to install dependencies +install: "pip install -r requirements.txt" +# command to run tests +script: + - python stock_trader.py diff --git a/BP.py b/BP.py index 8f60e1a..61519fc 100644 --- a/BP.py +++ b/BP.py @@ -1,193 +1,193 @@ -# coding=utf-8 -# Back-Propagation Neural Networks -# http://blog.csdn.net/akunainiannian/article/details/40073903 - -import math -import random -import string - -random.seed(0) - -# calculate a random number where: a <= rand < b -def rand(a, b): - return (b-a)*random.random() + a - -# Make a matrix (we could use NumPy to speed this up) -def makeMatrix(I, J, fill=0.0): - m = [] - for i in range(I): - m.append([fill]*J) - return m - -# our sigmoid function, tanh is a little nicer than the standard 1/(1+e^-x) -#使用双正切函数代替logistic函数 -def sigmoid(x): - return math.tanh(x) - -# derivative of our sigmoid function, in terms of the output (i.e. y) -# 双正切函数的导数，在求取输出层和隐藏侧的误差项的时候会用到 -def dsigmoid(y): - return 1.0 - y**2 - -class NN: - def __init__(self, ni, nh, no): - # number of input, hidden, and output nodes - # 输入层，隐藏层，输出层的数量，三层网络 - self.ni = ni + 1 # +1 for bias node - self.nh = nh - self.no = no - - # activations for nodes - self.ai = [1.0]*self.ni - self.ah = [1.0]*self.nh - self.ao = [1.0]*self.no - - # create weights - #生成权重矩阵，每一个输入层节点和隐藏层节点都连接 - #每一个隐藏层节点和输出层节点链接 - #大小：self.ni*self.nh - self.wi = makeMatrix(self.ni, self.nh) - #大小：self.ni*self.nh - self.wo = makeMatrix(self.nh, self.no) - # set them to random vaules - #生成权重，在-0.2-0.2之间 - for i in range(self.ni): - for j in range(self.nh): - self.wi[i][j] = rand(-0.2, 0.2) - for j in range(self.nh): - for k in range(self.no): - self.wo[j][k] = rand(-2.0, 2.0) - - # last change in weights for momentum - #? - self.ci = makeMatrix(self.ni, self.nh) - self.co = makeMatrix(self.nh, self.no) - - def update(self, inputs): - if len(inputs) != self.ni-1: - raise ValueError('wrong number of inputs') - - # input activations - # 输入的激活函数，就是y=x; - for i in range(self.ni-1): - #self.ai[i] = sigmoid(inputs[i]) - self.ai[i] = inputs[i] - - # hidden activations - #隐藏层的激活函数,求和然后使用压缩函数 - for j in range(self.nh): - sum = 0.0 - for i in range(self.ni): - #sum就是《ml》书中的net - sum = sum + self.ai[i] * self.wi[i][j] - self.ah[j] = sigmoid(sum) - - # output activations - #输出的激活函数 - for k in range(self.no): - sum = 0.0 - for j in range(self.nh): - sum = sum + self.ah[j] * self.wo[j][k] - self.ao[k] = sigmoid(sum) - - return self.ao[:] - - #反向传播算法 targets是样本的正确的输出 - def backPropagate(self, targets, N, M): - if len(targets) != self.no: - raise ValueError('wrong number of target values') - - # calculate error terms for output - #计算输出层的误差项 - output_deltas = [0.0] * self.no - for k in range(self.no): - #计算k-o - error = targets[k]-self.ao[k] - #计算书中公式4.14 - output_deltas[k] = dsigmoid(self.ao[k]) * error - - # calculate error terms for hidden - #计算隐藏层的误差项，使用《ml》书中的公式4.15 - hidden_deltas = [0.0] * self.nh - for j in range(self.nh): - error = 0.0 - for k in range(self.no): - error = error + output_deltas[k]*self.wo[j][k] - hidden_deltas[j] = dsigmoid(self.ah[j]) * error - - # update output weights - # 更新输出层的权重参数 - # 这里可以看出，本例使用的是带有“增加冲量项”的BPANN - # 其中，N为学习速率 M为充量项的参数 self.co为冲量项 - # N: learning rate - # M: momentum factor - for j in range(self.nh): - for k in range(self.no): - change = output_deltas[k]*self.ah[j] - self.wo[j][k] = self.wo[j][k] + N*change + M*self.co[j][k] - self.co[j][k] = change - #print N*change, M*self.co[j][k] - - # update input weights - #更新输入项的权重参数 - for i in range(self.ni): - for j in range(self.nh): - change = hidden_deltas[j]*self.ai[i] - self.wi[i][j] = self.wi[i][j] + N*change + M*self.ci[i][j] - self.ci[i][j] = change - - # calculate error - #计算E(w) - error = 0.0 - for k in range(len(targets)): - error = error + 0.5*(targets[k]-self.ao[k])**2 - return error - - #测试函数，用于测试训练效果 - def test(self, patterns): - for p in patterns: - print(p[0], '->', self.update(p[0])) - - def weights(self): - print('Input weights:') - for i in range(self.ni): - print(self.wi[i]) - print() - print('Output weights:') - for j in range(self.nh): - print(self.wo[j]) - - def train(self, patterns, iterations=1000, N=0.5, M=0.1): - # N: learning rate - # M: momentum factor - for i in range(iterations): - error = 0.0 - for p in patterns: - inputs = p[0] - targets = p[1] - self.update(inputs) - error = error + self.backPropagate(targets, N, M) - if i % 100 == 0: - print('error %-.5f' % error) - - -def demo(): - # Teach network XOR function - pat = [ - [[0], [0]], - [[2], [1]], - [[3], [1]], - [[4], [5]] - ] - - # create a network with two input, two hidden, and one output nodes - n = NN(1, 2, 1) - # train it with some patterns - n.train(pat) - # test it - n.test(pat) - - - -if __name__ == '__main__': - demo() +# coding=utf-8 +# Back-Propagation Neural Networks +# http://blog.csdn.net/akunainiannian/article/details/40073903 + +import math +import random +import string + +random.seed(0) + +# calculate a random number where: a <= rand < b +def rand(a, b): + return (b-a)*random.random() + a + +# Make a matrix (we could use NumPy to speed this up) +def makeMatrix(I, J, fill=0.0): + m = [] + for i in range(I): + m.append([fill]*J) + return m + +# our sigmoid function, tanh is a little nicer than the standard 1/(1+e^-x) +#使用双正切函数代替logistic函数 +def sigmoid(x): + return math.tanh(x) + +# derivative of our sigmoid function, in terms of the output (i.e. y) +# 双正切函数的导数，在求取输出层和隐藏侧的误差项的时候会用到 +def dsigmoid(y): + return 1.0 - y**2 + +class NN: + def __init__(self, ni, nh, no): + # number of input, hidden, and output nodes + # 输入层，隐藏层，输出层的数量，三层网络 + self.ni = ni + 1 # +1 for bias node + self.nh = nh + self.no = no + + # activations for nodes + self.ai = [1.0]*self.ni + self.ah = [1.0]*self.nh + self.ao = [1.0]*self.no + + # create weights + #生成权重矩阵，每一个输入层节点和隐藏层节点都连接 + #每一个隐藏层节点和输出层节点链接 + #大小：self.ni*self.nh + self.wi = makeMatrix(self.ni, self.nh) + #大小：self.ni*self.nh + self.wo = makeMatrix(self.nh, self.no) + # set them to random vaules + #生成权重，在-0.2-0.2之间 + for i in range(self.ni): + for j in range(self.nh): + self.wi[i][j] = rand(-0.2, 0.2) + for j in range(self.nh): + for k in range(self.no): + self.wo[j][k] = rand(-2.0, 2.0) + + # last change in weights for momentum + #? + self.ci = makeMatrix(self.ni, self.nh) + self.co = makeMatrix(self.nh, self.no) + + def update(self, inputs): + if len(inputs) != self.ni-1: + raise ValueError('wrong number of inputs') + + # input activations + # 输入的激活函数，就是y=x; + for i in range(self.ni-1): + #self.ai[i] = sigmoid(inputs[i]) + self.ai[i] = inputs[i] + + # hidden activations + #隐藏层的激活函数,求和然后使用压缩函数 + for j in range(self.nh): + sum = 0.0 + for i in range(self.ni): + #sum就是《ml》书中的net + sum = sum + self.ai[i] * self.wi[i][j] + self.ah[j] = sigmoid(sum) + + # output activations + #输出的激活函数 + for k in range(self.no): + sum = 0.0 + for j in range(self.nh): + sum = sum + self.ah[j] * self.wo[j][k] + self.ao[k] = sigmoid(sum) + + return self.ao[:] + + #反向传播算法 targets是样本的正确的输出 + def backPropagate(self, targets, N, M): + if len(targets) != self.no: + raise ValueError('wrong number of target values') + + # calculate error terms for output + #计算输出层的误差项 + output_deltas = [0.0] * self.no + for k in range(self.no): + #计算k-o + error = targets[k]-self.ao[k] + #计算书中公式4.14 + output_deltas[k] = dsigmoid(self.ao[k]) * error + + # calculate error terms for hidden + #计算隐藏层的误差项，使用《ml》书中的公式4.15 + hidden_deltas = [0.0] * self.nh + for j in range(self.nh): + error = 0.0 + for k in range(self.no): + error = error + output_deltas[k]*self.wo[j][k] + hidden_deltas[j] = dsigmoid(self.ah[j]) * error + + # update output weights + # 更新输出层的权重参数 + # 这里可以看出，本例使用的是带有“增加冲量项”的BPANN + # 其中，N为学习速率 M为充量项的参数 self.co为冲量项 + # N: learning rate + # M: momentum factor + for j in range(self.nh): + for k in range(self.no): + change = output_deltas[k]*self.ah[j] + self.wo[j][k] = self.wo[j][k] + N*change + M*self.co[j][k] + self.co[j][k] = change + #print N*change, M*self.co[j][k] + + # update input weights + #更新输入项的权重参数 + for i in range(self.ni): + for j in range(self.nh): + change = hidden_deltas[j]*self.ai[i] + self.wi[i][j] = self.wi[i][j] + N*change + M*self.ci[i][j] + self.ci[i][j] = change + + # calculate error + #计算E(w) + error = 0.0 + for k in range(len(targets)): + error = error + 0.5*(targets[k]-self.ao[k])**2 + return error + + #测试函数，用于测试训练效果 + def test(self, patterns): + for p in patterns: + print(p[0], '->', self.update(p[0])) + + def weights(self): + print('Input weights:') + for i in range(self.ni): + print(self.wi[i]) + print() + print('Output weights:') + for j in range(self.nh): + print(self.wo[j]) + + def train(self, patterns, iterations=1000, N=0.5, M=0.1): + # N: learning rate + # M: momentum factor + for i in range(iterations): + error = 0.0 + for p in patterns: + inputs = p[0] + targets = p[1] + self.update(inputs) + error = error + self.backPropagate(targets, N, M) + if i % 100 == 0: + print('error %-.5f' % error) + + +def demo(): + # Teach network XOR function + pat = [ + [[0], [0]], + [[2], [1]], + [[3], [1]], + [[4], [5]] + ] + + # create a network with two input, two hidden, and one output nodes + n = NN(1, 2, 1) + # train it with some patterns + n.train(pat) + # test it + n.test(pat) + + + +if __name__ == '__main__': + demo() diff --git a/BP.pyc b/BP.pyc new file mode 100644 index 0000000..172f6b9 Binary files /dev/null and b/BP.pyc differ diff --git a/README.md b/README.md index 9d90360..0d6b02f 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,8 @@ -- 实时交易 -- 历史回撤 - +[![Build Status](https://www.travis-ci.org/lishulincug/stock-2.svg?branch=master)](https://www.travis-ci.org/lishulincug/stock-2) +- 实时交易 +- 历史回撤 +-均线策略 +-趋势策略 +计算策略收益以及超额收益 +根据经验判断基金买卖情况 +发送邮件 diff --git a/cleastsq.py b/cleastsq.py index d2890a2..9094f61 100644 --- a/cleastsq.py +++ b/cleastsq.py @@ -1,115 +1,115 @@ -# coding=utf-8 - -''' -作者:Jairus Chan -程序:多项式曲线拟合算法 -''' -import matplotlib.pyplot as plt -import math -import numpy -import random - -#阶数为9阶 -order=9 - -#进行曲线拟合 -def getMatA(xa): - matA=[] - for i in range(0,order+1): - matA1=[] - for j in range(0,order+1): - tx=0.0 - for k in range(0,len(xa)): - dx=1.0 - for l in range(0,j+i): - dx=dx*xa[k] - tx+=dx - matA1.append(tx) - matA.append(matA1) - - matA=numpy.array(matA) - return matA - -def getMatB(xa,ya): - matB=[] - for i in range(0,order+1): - ty=0.0 - for k in range(0,len(xa)): - dy=1.0 - for l in range(0,i): - dy=dy*xa[k] - ty+=ya[k]*dy - matB.append(ty) - - matB=numpy.array(matB) - return matB - -def getMatAA(xa, ya): - matAA=numpy.linalg.solve(getMatA(xa),getMatB(xa, ya)) - return matAA - -#设置阶数（默认为9 -def setOrder(newOrder): - order = newOrder - -#获取拟合后的新序列 -def getFitYValues(xValues, yValues, xNewValues): - matAA_get = getMatAA(xValues, yValues) - - yya=[] - - for i in range(0,len(xNewValues)): - yy=0.0 - for j in range(0,order+1): - dy=1.0 - for k in range(0,j): - dy*=xNewValues[i] - dy*=matAA_get[j] - yy+=dy - yya.append(yy) - - return yya -#画出拟合后的曲线 -#print(matAA) - -if __name__ == "__main__": - - fig = plt.figure() - ax = fig.add_subplot(111) - - #生成曲线上的各个点 - x = numpy.arange(-1,1,0.02) - y = [((a*a-1)*(a*a-1)*(a*a-1)+0.5)*numpy.sin(a*2) for a in x] - #ax.plot(x,y,color='r',linestyle='-',marker='') - #,label="(a*a-1)*(a*a-1)*(a*a-1)+0.5" - - #生成的曲线上的各个点偏移一下，并放入到xa,ya中去 - i=0 - xa=[] - ya=[] - for xx in x: - yy=y[i] - d=float(random.randint(60,140))/100 - #ax.plot([xx*d],[yy*d],color='m',linestyle='',marker='.') - i+=1 - xa.append(xx*d) - ya.append(yy*d) - - '''for i in range(0,5): - xx=float(random.randint(-100,100))/100 - yy=float(random.randint(-60,60))/100 - xa.append(xx) - ya.append(yy)''' - - ax.plot(xa,ya,color='m',linestyle='',marker='.') - - matAA_n = getMatAA(xa,ya) - print len(matAA_n) - - xxa= numpy.arange(-1,1.2,0.01) - yya= getFitYValues(xa, ya, xxa) - - ax.plot(xxa,yya,color='g',linestyle='-',marker='') - - ax.legend() +# coding=utf-8 + +''' +作者:Jairus Chan +程序:多项式曲线拟合算法 +''' +import matplotlib.pyplot as plt +import math +import numpy +import random + +#阶数为9阶 +order=9 + +#进行曲线拟合 +def getMatA(xa): + matA=[] + for i in range(0,order+1): + matA1=[] + for j in range(0,order+1): + tx=0.0 + for k in range(0,len(xa)): + dx=1.0 + for l in range(0,j+i): + dx=dx*xa[k] + tx+=dx + matA1.append(tx) + matA.append(matA1) + + matA=numpy.array(matA) + return matA + +def getMatB(xa,ya): + matB=[] + for i in range(0,order+1): + ty=0.0 + for k in range(0,len(xa)): + dy=1.0 + for l in range(0,i): + dy=dy*xa[k] + ty+=ya[k]*dy + matB.append(ty) + + matB=numpy.array(matB) + return matB + +def getMatAA(xa, ya): + matAA=numpy.linalg.solve(getMatA(xa),getMatB(xa, ya)) + return matAA + +#设置阶数（默认为9 +def setOrder(newOrder): + order = newOrder + +#获取拟合后的新序列 +def getFitYValues(xValues, yValues, xNewValues): + matAA_get = getMatAA(xValues, yValues) + + yya=[] + + for i in range(0,len(xNewValues)): + yy=0.0 + for j in range(0,order+1): + dy=1.0 + for k in range(0,j): + dy*=xNewValues[i] + dy*=matAA_get[j] + yy+=dy + yya.append(yy) + + return yya +#画出拟合后的曲线 +#print(matAA) + +if __name__ == "__main__": + + fig = plt.figure() + ax = fig.add_subplot(111) + + #生成曲线上的各个点 + x = numpy.arange(-1,1,0.02) + y = [((a*a-1)*(a*a-1)*(a*a-1)+0.5)*numpy.sin(a*2) for a in x] + #ax.plot(x,y,color='r',linestyle='-',marker='') + #,label="(a*a-1)*(a*a-1)*(a*a-1)+0.5" + + #生成的曲线上的各个点偏移一下，并放入到xa,ya中去 + i=0 + xa=[] + ya=[] + for xx in x: + yy=y[i] + d=float(random.randint(60,140))/100 + #ax.plot([xx*d],[yy*d],color='m',linestyle='',marker='.') + i+=1 + xa.append(xx*d) + ya.append(yy*d) + + '''for i in range(0,5): + xx=float(random.randint(-100,100))/100 + yy=float(random.randint(-60,60))/100 + xa.append(xx) + ya.append(yy)''' + + ax.plot(xa,ya,color='m',linestyle='',marker='.') + + matAA_n = getMatAA(xa,ya) + print len(matAA_n) + + xxa= numpy.arange(-1,1.2,0.01) + yya= getFitYValues(xa, ya, xxa) + + ax.plot(xxa,yya,color='g',linestyle='-',marker='') + + ax.legend() plt.show() \ No newline at end of file diff --git a/cleastsq.pyc b/cleastsq.pyc new file mode 100644 index 0000000..d93af08 Binary files /dev/null and b/cleastsq.pyc differ diff --git a/config.ini b/config.ini index c8b0736..1a29a50 100644 --- a/config.ini +++ b/config.ini @@ -1,11 +1,11 @@ - - -# 日志设置 -[log] -info_log_name=../Data/Log/info.log -error_log_name=../Data/Log/error.log - -#交易阈值 -[trade_threshold] -Threshold_Buy_Count = 3 + + +# 日志设置 +[log] +info_log_name=../Data/Log/info.log +error_log_name=../Data/Log/error.log + +#交易阈值 +[trade_threshold] +Threshold_Buy_Count = 3 Threshold_Sale_Count = 3 \ No newline at end of file diff --git a/cwavelet.py b/cwavelet.py index 2f6e6a8..3bf0db3 100644 --- a/cwavelet.py +++ b/cwavelet.py @@ -1,129 +1,129 @@ -#!/usr/local/bin/python -#coding=utf-8 - -import math -import numpy as np -import pywt -import pylab -import matplotlib.pyplot as plt -import cleastsq -import copy - -#固定阈值准则 (sqtwolog) -def threshold_sqtwolog(n): - return math.sqrt(2*math.log(n)) - -#软阈值处理函数 -def softThreshold(thr, values): - newValues = [] - for value in values: - if math.fabs(value) <= thr: - value = 0 - elif value > thr: - value -= thr - else: - value += thr - newValues.append(value) - return newValues - -def getWaveletData(values, waveletName, level, threadMethodName): - mode = 'sym' - #小波系数分解 - data = pywt.wavedec(values, waveletName, mode, level) - #cA4, cD4, cD3, cD2, cD1 = pywt.wavedec(values, waveletName, mode, level) - coeffs = [] #小波重构系数 - #阈值处理 - if threadMethodName == 'sqtwolog': - #print len(cA4), len(cD4), len(cD3), len(cD2), len(cD1),len(values) - for i in np.arange(0, len(data)): - if i > 0: - data[i] = softThreshold(threshold_sqtwolog(len(data[i])), data[i]) - coeffs.append(data[i]) - #小波重构 - zValues = pywt.waverec(coeffs, waveletName, mode) - -# if level == 4: -# #cA4, cD4, cD3, cD2, cD1 = pywt.wavedec(values, waveletName, mode, level) -# coeffs = [] -# #阈值处理 -# if threadMethodName == 'sqtwolog': -# #print len(cA4), len(cD4), len(cD3), len(cD2), len(cD1),len(values) -# # cD4 = softThreshold(threshold_sqtwolog(len(cD4)), cD4) -# # cD3 = softThreshold(threshold_sqtwolog(len(cD3)), cD3) -# # cD2 = softThreshold(threshold_sqtwolog(len(cD2)), cD2) -# # cD1 = softThreshold(threshold_sqtwolog(len(cD1)), cD1) -# #小波重构 -# #coeffs = [cA4, cD4, cD3, cD2, cD1] -# zValues = pywt.waverec(coeffs, waveletName, mode) -# elif level ==2: -# cA2, cD2, cD1 = pywt.wavedec(values, waveletName, mode, level) -# #阈值处理 -# if threadMethodName == 'sqtwolog': -# print len(cA2), len(cD2), len(cD1),len(values) -# cD2 = softThreshold(threshold_sqtwolog(len(cD2)), cD2) -# cD1 = softThreshold(threshold_sqtwolog(len(cD1)), cD1) -# #小波重构 -# coeffs = [cA2, cD2, cD1] -# zValues = pywt.waverec(coeffs, waveletName, mode) - return zValues - -#小波包分解 -# forecastCount:预测的点位数 -def getWavePacketData(values, waveletName, level, forecastCount): - wp = pywt.WaveletPacket(data=values, wavelet=waveletName, mode='sym', maxlevel=level) - print waveletName, ":", wp.maxlevel - #nodes = wp.get_level(level) - #labels = [n.path for n in nodes] - #values = pylab.array([n.data for n in nodes], 'd') - #print labels - #print [n.data for n in nodes] - #print [node.path for node in wp.get_leaf_nodes(decompose=False)] - #print [node.path for node in wp.get_leaf_nodes(decompose=True)] - coeffs = [(node.path, node.data) for node in wp.get_leaf_nodes(decompose=True)] - #print coeffs - for node in wp.get_leaf_nodes(decompose=True): - print node.path, len(node.data) - - - coeffsNew = [] - for path, data in coeffs: - data = cleastsq.getFitYValues(range(len(data)), data, range(len(data)+forecastCount)) - coeffsNew.append((path, data)) - - #print coeffsNew - - wp2 = pywt.WaveletPacket(None, waveletName, maxlevel=level) - for path, data in coeffsNew: - wp2[path] = data - - #print wp["a"] - #print [node.path for node in wp2.get_leaf_nodes(decompose=False)] - value2s= wp2.reconstruct(update=True) - newLen = len(values)+forecastCount - print newLen - return value2s[:newLen] - -# print len(value2s) -# value2s_n= wp2.reconstruct(update=False) -# plt.plot(range(18), value2s[0:18], color="b", linewidth=1) -# #plt.plot(range(len(value2s_n)), value2s_n, color="r", linewidth=1) -# plt.xlabel("Time") -# plt.ylabel("Price") -# plt.grid() -# plt.legend() -# plt.show() - -# print wp['a'].data -# print wp['d'].data -# print wp['add'].data -# print wp['ada'].data -# print [node.path for node in wp.get_level(3, 'freq')] - - -if __name__ == '__main__': - values = range(16) - plt.plot(values) - for i in np.arange(4,5): - name = "db"+str(i) - print getWavePacketData(values, name, 3, 3) +#!/usr/local/bin/python +#coding=utf-8 + +import math +import numpy as np +import pywt +import pylab +import matplotlib.pyplot as plt +import cleastsq +import copy + +#固定阈值准则 (sqtwolog) +def threshold_sqtwolog(n): + return math.sqrt(2*math.log(n)) + +#软阈值处理函数 +def softThreshold(thr, values): + newValues = [] + for value in values: + if math.fabs(value) <= thr: + value = 0 + elif value > thr: + value -= thr + else: + value += thr + newValues.append(value) + return newValues + +def getWaveletData(values, waveletName, level, threadMethodName): + mode = 'sym' + #小波系数分解 + data = pywt.wavedec(values, waveletName, mode, level) + #cA4, cD4, cD3, cD2, cD1 = pywt.wavedec(values, waveletName, mode, level) + coeffs = [] #小波重构系数 + #阈值处理 + if threadMethodName == 'sqtwolog': + #print len(cA4), len(cD4), len(cD3), len(cD2), len(cD1),len(values) + for i in np.arange(0, len(data)): + if i > 0: + data[i] = softThreshold(threshold_sqtwolog(len(data[i])), data[i]) + coeffs.append(data[i]) + #小波重构 + zValues = pywt.waverec(coeffs, waveletName, mode) + +# if level == 4: +# #cA4, cD4, cD3, cD2, cD1 = pywt.wavedec(values, waveletName, mode, level) +# coeffs = [] +# #阈值处理 +# if threadMethodName == 'sqtwolog': +# #print len(cA4), len(cD4), len(cD3), len(cD2), len(cD1),len(values) +# # cD4 = softThreshold(threshold_sqtwolog(len(cD4)), cD4) +# # cD3 = softThreshold(threshold_sqtwolog(len(cD3)), cD3) +# # cD2 = softThreshold(threshold_sqtwolog(len(cD2)), cD2) +# # cD1 = softThreshold(threshold_sqtwolog(len(cD1)), cD1) +# #小波重构 +# #coeffs = [cA4, cD4, cD3, cD2, cD1] +# zValues = pywt.waverec(coeffs, waveletName, mode) +# elif level ==2: +# cA2, cD2, cD1 = pywt.wavedec(values, waveletName, mode, level) +# #阈值处理 +# if threadMethodName == 'sqtwolog': +# print len(cA2), len(cD2), len(cD1),len(values) +# cD2 = softThreshold(threshold_sqtwolog(len(cD2)), cD2) +# cD1 = softThreshold(threshold_sqtwolog(len(cD1)), cD1) +# #小波重构 +# coeffs = [cA2, cD2, cD1] +# zValues = pywt.waverec(coeffs, waveletName, mode) + return zValues + +#小波包分解 +# forecastCount:预测的点位数 +def getWavePacketData(values, waveletName, level, forecastCount): + wp = pywt.WaveletPacket(data=values, wavelet=waveletName, mode='sym', maxlevel=level) + print waveletName, ":", wp.maxlevel + #nodes = wp.get_level(level) + #labels = [n.path for n in nodes] + #values = pylab.array([n.data for n in nodes], 'd') + #print labels + #print [n.data for n in nodes] + #print [node.path for node in wp.get_leaf_nodes(decompose=False)] + #print [node.path for node in wp.get_leaf_nodes(decompose=True)] + coeffs = [(node.path, node.data) for node in wp.get_leaf_nodes(decompose=True)] + #print coeffs + for node in wp.get_leaf_nodes(decompose=True): + print node.path, len(node.data) + + + coeffsNew = [] + for path, data in coeffs: + data = cleastsq.getFitYValues(range(len(data)), data, range(len(data)+forecastCount)) + coeffsNew.append((path, data)) + + #print coeffsNew + + wp2 = pywt.WaveletPacket(None, waveletName, maxlevel=level) + for path, data in coeffsNew: + wp2[path] = data + + #print wp["a"] + #print [node.path for node in wp2.get_leaf_nodes(decompose=False)] + value2s= wp2.reconstruct(update=True) + newLen = len(values)+forecastCount + print newLen + return value2s[:newLen] + +# print len(value2s) +# value2s_n= wp2.reconstruct(update=False) +# plt.plot(range(18), value2s[0:18], color="b", linewidth=1) +# #plt.plot(range(len(value2s_n)), value2s_n, color="r", linewidth=1) +# plt.xlabel("Time") +# plt.ylabel("Price") +# plt.grid() +# plt.legend() +# plt.show() + +# print wp['a'].data +# print wp['d'].data +# print wp['add'].data +# print wp['ada'].data +# print [node.path for node in wp.get_level(3, 'freq')] + + +if __name__ == '__main__': + values = range(16) + plt.plot(values) + for i in np.arange(4,5): + name = "db"+str(i) + print getWavePacketData(values, name, 3, 3) plt.plot(values) \ No newline at end of file diff --git a/cwavelet.pyc b/cwavelet.pyc new file mode 100644 index 0000000..db0000b Binary files /dev/null and b/cwavelet.pyc differ diff --git a/data_process/Stock.py b/data_process/Stock.py index b7118e3..5afdb39 100644 --- a/data_process/Stock.py +++ b/data_process/Stock.py @@ -1,89 +1,89 @@ -#!/usr/local/bin/python -#coding=utf-8 - -from datetime import date - -from util.codeConvert import * -from init import * - -stockNameList = [('600000', '浦发银行'), \ - ('600048', '保利地产'), \ - ('600011', '华能国际'),\ - ('002600', '江粉磁材'),\ - ('002505', '大康牧业'),\ - ('000725', '京东方A'),\ - ('000783', '长江证券'),\ - ('300315', '掌趣科技'),\ - ('002167', '东方锆业'),\ - ('601001', '大同煤业'),\ - ('150172', '证券B')] - -class Stock: - name = '' #股票名，中文 - code = '' #股票代码 - open = 0.0 #今日开盘价 - close = 0.0 #昨日收盘价 - current = 0.0 #当前价 - high = 0.0 #今日最高价 - low = 0.0 #今日最低价 - buyFiveInfo = [] #五档买入：[(买一报价，买一申请数)，买二...] - saleFiveInfo = [] #五档卖出：(卖一报价，卖一申请数)，卖二... - dealAmount = 0 #成交股数/100，N手 - dealTurnover = 0 #成交金额 - date = date.today() #日期 - time = time.localtime(time.time()) #时间 - - signal = 0 - - #构造函数 - def __init__(self, sinaStockInfo=''): - - if len(sinaStockInfo) >= 32: - self.name = sinaStockInfo[0] - self.code = self.find_in_list(self.name) - self.open = sinaStockInfo[1] - self.close = sinaStockInfo[2] - self.current = sinaStockInfo[3] - self.high = sinaStockInfo[4] - self.low = sinaStockInfo[5] - self.dealAmount = sinaStockInfo[8] - self.dealTurnover = sinaStockInfo[9] - self.date = sinaStockInfo[30] - self.time = sinaStockInfo[31] - self.buyFiveInfo = [(sinaStockInfo[11], sinaStockInfo[10]),\ - (sinaStockInfo[13], sinaStockInfo[12]),\ - (sinaStockInfo[15], sinaStockInfo[14]),\ - (sinaStockInfo[17], sinaStockInfo[16]),\ - (sinaStockInfo[19], sinaStockInfo[18])] - self.saleFiveInfo = [(sinaStockInfo[21], sinaStockInfo[20]),\ - (sinaStockInfo[23], sinaStockInfo[22]),\ - (sinaStockInfo[25], sinaStockInfo[24]),\ - (sinaStockInfo[27], sinaStockInfo[26]),\ - (sinaStockInfo[29], sinaStockInfo[28])] - - def find_in_list(self, name): - try: - for i in range(0, len(stockNameList)): - if name == stockNameList[i][1]: - return stockNameList[i][0] - except Exception as e: - print "find_in_list() Exception:", str(e) - finally: - None - return '' - - def __str__(self): - try: - s = '%s\t%s\t%s\t%0.3f' % (self.name, self.code, str(self.current), (float(self.current)-float(self.close))/float(self.close)*100) - s = s + '%' - #str = '%s %s %s ' % (self.name, self.code, str(self.current)) - #str = str + str((float(self.current)-float(self.close))/float(self.close)*100) + '%' - return s - except Exception,e: - errorLogger.logger.error(str(e)) - return '' - - def str_print(self): - return self.__str__() - +#!/usr/local/bin/python +#coding=utf-8 + +from datetime import date + +from util.codeConvert import * +from init import * + +stockNameList = [('600000', '浦发银行'), \ + ('600048', '保利地产'), \ + ('600011', '华能国际'),\ + ('002600', '江粉磁材'),\ + ('002505', '大康牧业'),\ + ('000725', '京东方A'),\ + ('000783', '长江证券'),\ + ('300315', '掌趣科技'),\ + ('002167', '东方锆业'),\ + ('601001', '大同煤业'),\ + ('150172', '证券B')] + +class Stock: + name = '' #股票名，中文 + code = '' #股票代码 + open = 0.0 #今日开盘价 + close = 0.0 #昨日收盘价 + current = 0.0 #当前价 + high = 0.0 #今日最高价 + low = 0.0 #今日最低价 + buyFiveInfo = [] #五档买入：[(买一报价，买一申请数)，买二...] + saleFiveInfo = [] #五档卖出：(卖一报价，卖一申请数)，卖二... + dealAmount = 0 #成交股数/100，N手 + dealTurnover = 0 #成交金额 + date = date.today() #日期 + time = time.localtime(time.time()) #时间 + + signal = 0 + + #构造函数 + def __init__(self, sinaStockInfo=''): + + if len(sinaStockInfo) >= 32: + self.name = sinaStockInfo[0] + self.code = self.find_in_list(self.name) + self.open = sinaStockInfo[1] + self.close = sinaStockInfo[2] + self.current = sinaStockInfo[3] + self.high = sinaStockInfo[4] + self.low = sinaStockInfo[5] + self.dealAmount = sinaStockInfo[8] + self.dealTurnover = sinaStockInfo[9] + self.date = sinaStockInfo[30] + self.time = sinaStockInfo[31] + self.buyFiveInfo = [(sinaStockInfo[11], sinaStockInfo[10]),\ + (sinaStockInfo[13], sinaStockInfo[12]),\ + (sinaStockInfo[15], sinaStockInfo[14]),\ + (sinaStockInfo[17], sinaStockInfo[16]),\ + (sinaStockInfo[19], sinaStockInfo[18])] + self.saleFiveInfo = [(sinaStockInfo[21], sinaStockInfo[20]),\ + (sinaStockInfo[23], sinaStockInfo[22]),\ + (sinaStockInfo[25], sinaStockInfo[24]),\ + (sinaStockInfo[27], sinaStockInfo[26]),\ + (sinaStockInfo[29], sinaStockInfo[28])] + + def find_in_list(self, name): + try: + for i in range(0, len(stockNameList)): + if name == stockNameList[i][1]: + return stockNameList[i][0] + except Exception as e: + print "find_in_list() Exception:", str(e) + finally: + None + return '' + + def __str__(self): + try: + s = '%s\t%s\t%s\t%0.3f' % (self.name, self.code, str(self.current), (float(self.current)-float(self.close))/float(self.close)*100) + s = s + '%' + #str = '%s %s %s ' % (self.name, self.code, str(self.current)) + #str = str + str((float(self.current)-float(self.close))/float(self.close)*100) + '%' + return s + except Exception,e: + errorLogger.logger.error(str(e)) + return '' + + def str_print(self): + return self.__str__() + \ No newline at end of file diff --git a/data_process/Stock.pyc b/data_process/Stock.pyc new file mode 100644 index 0000000..78d4204 Binary files /dev/null and b/data_process/Stock.pyc differ diff --git a/data_process/__init__.py b/data_process/__init__.py index 04acb8f..3f3d417 100644 --- a/data_process/__init__.py +++ b/data_process/__init__.py @@ -1,5 +1,5 @@ -from data_process.Stock import * -from data_process.online_data import * -from data_process.native_data import * -from data_process.download_stock import * - +# from data_process.Stock import * +# from data_process.online_data import * +# from data_process.native_data import * +# from data_process.download_stock import * + diff --git a/data_process/__init__.pyc b/data_process/__init__.pyc new file mode 100644 index 0000000..1503760 Binary files /dev/null and b/data_process/__init__.pyc differ diff --git a/data_process/dataDownloadByDongfangcf.py b/data_process/dataDownloadByDongfangcf.py new file mode 100644 index 0000000..12d93c6 --- /dev/null +++ b/data_process/dataDownloadByDongfangcf.py @@ -0,0 +1,178 @@ +#ecoding:utf-8 +# 导入需要使用到的模块 +import urllib +import re +import pandas as pd +import pymysql +import os,datetime,time + + +# 爬虫抓取网页函数 +def getHtml(url): + html = urllib.urlopen(url).read() + html = html.decode('gbk') + return html + + +# 抓取网页股票代码函数 +def getStackCode(html): + s = r'
  • ' + pat = re.compile(s) + code = pat.findall(html) + return code +def downlaodDataBycode(code,jj): + #########################开始干活############################ + Url = 'http://quote.eastmoney.com/stocklist.html' # 东方财富网股票数据连接地址 + filepath = './stockdata/dongfangcf/' # 定义数据文件保存路径 + # 实施抓取 + # code = getStackCode(getHtml(Url)) + # 获取所有股票代码（以6开头的，应该是沪市数据）集合 + # CodeList = [] + # for item in code: + # if item[0] == '6': + # CodeList.append(item) + file={'stockcodewangyiShangz.txt':0,'stockcodewangyiShenz.txt':1} #'stockcodewangyiqdii.txt':'','stockcodewangyidetail.txt':'' + # + # CodeList = [] + # stockTxtNewPath =os.path.pardir + "/stockdata/" + jj + # file_to_read = open(stockTxtNewPath, 'r') + # lines = file_to_read.readlines() # 整行读取数据 + # if not lines: + # pass + # else: + # for line in lines: + # if line != '\n'and line!=' ': + # if jj=='stockcodewangyidetail.txt': + # icode=line.split(' ') + # else: + # icode = line.split('\t') + # CodeList.append(icode[1]) + # 抓取数据并保存到本地csv文件 + # for code in CodeList: + if not ' 'in code: + print(u'正在获取股票%s数据' % code) + strtoday = datetime.datetime.strftime(datetime.datetime.now(), '%Y%m%d') + tdatetime = datetime.datetime.strptime(strtoday, '%Y%m%d') + # 前年今日 + sdatetime = tdatetime - datetime.timedelta(days=365) + strsdate = datetime.datetime.strftime(sdatetime, '%Y%m%d') + url = 'http://quotes.money.163.com/service/chddata.html?code='+str(file[jj]) + code + \ + '&start='+str(strsdate)+'&end='+str(strtoday)+'&fields=TCLOSE;HIGH;LOW;TOPEN;LCLOSE;CHG;PCHG;TURNOVER;VOTURNOVER;VATURNOVER;TCAP;MCAP' + print url + # if jj=='stockcodewangyiqdii.txt': + # print code + time.sleep(0.1) + urllib.urlretrieve(url, filepath + code + '.csv') + return filepath + code + '.csv' +def downlaodData(): + #########################开始干活############################ + Url = 'http://quote.eastmoney.com/stocklist.html' # 东方财富网股票数据连接地址 + filepath =os.path.pardir + '/stockdata/dongfangcf/' # 定义数据文件保存路径 + # 实施抓取 + # code = getStackCode(getHtml(Url)) + # 获取所有股票代码（以6开头的，应该是沪市数据）集合 + # CodeList = [] + # for item in code: + # if item[0] == '6': + # CodeList.append(item) + file={'stockcodewangyiShangz.txt':0,'stockcodewangyiShenz.txt':1} #'stockcodewangyiqdii.txt':'','stockcodewangyidetail.txt':'' + for jj in file: + CodeList = [] + stockTxtNewPath =os.path.pardir + "../stockdata/" + jj + file_to_read = open(stockTxtNewPath, 'r') + lines = file_to_read.readlines() # 整行读取数据 + if not lines: + pass + else: + for line in lines: + if line != '\n'and line!=' ': + if jj=='stockcodewangyidetail.txt': + icode=line.split(' ') + else: + icode = line.split('\t') + CodeList.append(icode[1]) + # 抓取数据并保存到本地csv文件 + for code in CodeList: + if not ' 'in code: + print(u'正在获取股票%s数据' % code) + strtoday = datetime.datetime.strftime(datetime.datetime.now(), '%Y%m%d') + tdatetime = datetime.datetime.strptime(strtoday, '%Y%m%d') + # 前年今日 + sdatetime = tdatetime - datetime.timedelta(days=365) + strsdate = datetime.datetime.strftime(sdatetime, '%Y%m%d') + url = 'http://quotes.money.163.com/service/chddata.html?code='+str(file[jj]) + code + \ + '&start='+str(strsdate)+'&end='+str(strtoday)+'&fields=TCLOSE;HIGH;LOW;TOPEN;LCLOSE;CHG;PCHG;TURNOVER;VOTURNOVER;VATURNOVER;TCAP;MCAP' + print url + if jj=='stockcodewangyiqdii.txt': + print code + time.sleep(0.1) + urllib.urlretrieve(url, filepath + code + '.csv') + # stockdata=urllib.urlopen(url) + # print (stockdata) +if __name__ == '__main__': + + downlaodData() + filepath = os.path.pardir + '/stockdata/dongfangcf/' + ##########################将股票数据存入数据库########################### + # 数据库名称和密码 + name = 'xxxx' + password = 'xxxx' # 替换为自己的账户名和密码 + # 建立本地数据库连接(需要先开启数据库服务) + db = pymysql.connect('localhost', name, password, charset='utf8') + cursor = db.cursor() + # 创建数据库stockDataBase + sqlSentence1 = "create database stockDataBase" + cursor.execute(sqlSentence1) # 选择使用当前数据库 + sqlSentence2 = "use stockDataBase;" + cursor.execute(sqlSentence2) + + # 获取本地文件列表 + fileList = os.listdir(filepath) + # 依次对每个数据文件进行存储 + for fileName in fileList: + data = pd.read_csv(filepath + fileName, encoding="gbk") + # 创建数据表，如果数据表已经存在，会跳过继续执行下面的步骤print('创建数据表stock_%s'% fileName[0:6]) + try: + sqlSentence3 = "create table stock_%s" % fileName[0:6] + "(日期 date, 股票代码 VARCHAR(10), 名称 VARCHAR(10),\ + 收盘价 float, 最高价 float, 最低价 float, 开盘价 float, 前收盘 float, 涨跌额 float, \ + 涨跌幅 float, 换手率 float, 成交量 bigint, 成交金额 bigint, 总市值 bigint, 流通市值 bigint)" + cursor.execute(sqlSentence3) + except: + print('数据表已存在！') + + # 迭代读取表中每行数据，依次存储（整表存储还没尝试过） + print('正在存储stock_%s' % fileName[0:6]) + length = len(data) + for i in range(0, length): + record = tuple(data.loc[i]) + # 插入数据语句 + try: + sqlSentence4 = "insert into stock_%s" % fileName[0:6] + "(日期, 股票代码, 名称, 收盘价, 最高价, 最低价, 开盘价,\ + 前收盘, 涨跌额, 涨跌幅, 换手率, 成交量, 成交金额, 总市值, 流通市值) \ + values ('%s',%s','%s',%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)" % record + # 获取的表中数据很乱，包含缺失值、Nnone、none等，插入数据库需要处理成空值 + sqlSentence4 = sqlSentence4.replace('nan', 'null').replace('None', 'null').replace('none', 'null') + cursor.execute(sqlSentence4) + except: + # 如果以上插入过程出错，跳过这条数据记录，继续往下进行 + break + + # 关闭游标，提交，关闭数据库连接 + cursor.close() + db.commit() + db.close() + + ###########################查询刚才操作的成果################################## + + # 重新建立数据库连接 + db = pymysql.connect('localhost', name, password, 'stockDataBase') + cursor = db.cursor() + # 查询数据库并打印内容 + cursor.execute('select * from stock_600000') + results = cursor.fetchall() + for row in results: + print(row) + # 关闭 + cursor.close() + db.commit() + db.close() \ No newline at end of file diff --git a/data_process/data_calcute.py b/data_process/data_calcute.py index dcdad5f..30d7fba 100644 --- a/data_process/data_calcute.py +++ b/data_process/data_calcute.py @@ -1,155 +1,157 @@ -#coding=utf-8 - -__author__ = 'cbb' - -import sys -sys.path.append('C:\Code\stock-master') -reload(sys) -sys.setdefaultencoding('utf-8') - -import MySQLdb -import numpy as np -import pandas as pd -import datetime -from tqdm import tqdm -from multiprocessing.dummy import Pool as ThreadPool -import wrapcache - -from data_get import * - - - - - - - -#计算均线 -def calcute_ma_all(): - codes = get_all_stock_codes() - # for code in tqdm(codes): - # _calcute_ma(code) - - pool = ThreadPool(processes=4) - pool.map(_calcute_ma, codes) - pool.close() - pool.join() - -# date_start:'2015-01-01' -# date_end: '2015-10-12' -def _calcute_ma(code, date_start='', date_end='', is_calcute_lastest=False): - try: - df = get_stock_k_line_if_ma_is_null(code) - if df is None: - return - close_prices = df['close'].get_values() - - print str(code) + ' ' + 'calcute ma' - ma_short = pd.rolling_mean(close_prices, AVR_SHORT) #12 - ma_long = pd.rolling_mean(close_prices, AVR_LONG) #40 - - df[ 'ma_' + str(AVR_SHORT)] = ma_short - df['ma_' + str(AVR_LONG)] = ma_long - - print str(code) + ' ' + 'calcute ema' - ema_short = pd.ewma(close_prices, span=AVR_SHORT) #12 - ema_long = pd.ewma(close_prices, span=AVR_LONG) #40 - - df['ema_'+str(AVR_SHORT)] = ema_short - df['ema_'+str(AVR_LONG)] = ema_long - - df = df.replace(np.nan, 0) - - #写数据库 - - # 增加列 - table_name =STOCK_KLINE_TABLE - ma_s = 'ma_' + str(AVR_SHORT) - ma_l = 'ma_' + str(AVR_LONG) - ema_s = 'ema_' + str(AVR_SHORT) - ema_l = 'ema_' + str(AVR_LONG) - # try: - # sql = "alter table %s add %s double" % (table_name, ma_s) - # engine.execute(sql) - # sql = "alter table %s add %s double" % (table_name, ma_l) - # engine.execute(sql) - # sql = "alter table %s add %s double" % (table_name, ema_s) - # engine.execute(sql) - # sql = "alter table %s add %s double" % (table_name, ema_l) - # engine.execute(sql) - # except Exception, e: - # str_error = 'column exists' - # print '' - - # 按由近到远的顺序排序 - df = df.sort_index(by='date', ascending=False) - - count = 0 - - #更新数据 - for ix, row in df.iterrows(): - print row - if row[ma_l] == 0 or row[ema_l] == 0: - continue - - sql_update = "update %s set %s=%f,%s=%f,%s=%f,%s=%f where date='%s' and %s='%s'" % \ - (table_name, ma_s, row[ma_s], ma_l, row[ma_l], - ema_s, row[ema_s], ema_l,row[ema_l], \ - row['date'], KEY_CODE, code) - print sql_update - engine.execute(sql_update) - print table_name + ' ' + str(row['date']) - - count = count+1 - #只计算最后1个收盘 - if is_calcute_lastest and count >= 7: - break - - except Exception, e: - print (str(code)+":"+date_start+" ~ "+date_end+str(e)) - -# 计算最近日期的均线 -def calcute_ma_lastest_all(): - codes = get_all_stock_codes() - - for code in codes: - _calcute_ma_lastest(code) - -# 计算最近日期的均线 (单个) -def _calcute_ma_lastest(code): - - d_avr_long = datetime.date.today() + datetime.timedelta(days=-180) - d_today = datetime.date.today() - date_start = d_avr_long.strftime('%Y-%m-%d') - date_end = d_today.strftime('%Y-%m-%d') - - _calcute_ma(code, date_start, date_end, True) - -@wrapcache.wrapcache(timeout=6*60*60) -def calcute_ma(df, avr_short=12, avr_long=40): - """ - 计算ma, ema - :param df: - :return: - """ - if len(df) == 0: - return - - # print "{} calcute ma".format(df.ix[0,'code']) - df['ma_' + str(avr_short)] = pd.rolling_mean(df['close'], avr_short) # 12 - df['ma_' + str(avr_long)] = pd.rolling_mean(df['close'], avr_long) # 40 - - - # print "{} calcute ema".format(df.ix[0, 'code']) - df['ema_' + str(avr_short)] = pd.ewma(df['close'], span=avr_short) # 12 - df['ema_' + str(avr_long)] = pd.ewma(df['close'], span=avr_long) # 40 - - df = df.replace(np.nan, 0) - return df - - - -if __name__ == "__main__": - #_calcute_ma('600000', '2015-01-01', '2015-10-14', True) - calcute_ma_all() - #calcute_ma_lastest_all() +#coding=utf-8 +__author__ = 'cbb' + +import sys +sys.path.append('C:\Code\stock-master') +reload(sys) +sys.setdefaultencoding('utf-8') + +# import MySQLdb +import pymysql +import numpy as np +import pandas as pd +import datetime +from tqdm import tqdm +from multiprocessing.dummy import Pool as ThreadPool +import wrapcache +from data_get import * + +ChinaStockIndexList = [ + "000001", # sh000001 上证指数 + "399001", # sz399001 深证成指 + "000300", # sh000300 沪深300 + "399005", # sz399005 中小板指 + "399006", # sz399006 创业板指 + "000003",# sh000003 B股指数 + "000016",#上证50 + "000012",#国债指数 +] + +#计算均线 +def calcute_ma_all(): + # codes = get_all_stock_codes() + # for code in tqdm(codes): + # _calcute_ma(code) + + pool = ThreadPool(processes=4) + pool.map(_calcute_ma, ChinaStockIndexList) + pool.close() + pool.join() + +# date_start:'2015-01-01' +# date_end: '2015-10-12' +def _calcute_ma(code, date_start='', date_end='', is_calcute_lastest=False): + try: + df = get_stock_k_line_if_ma_is_null(code) + if df is None: + return + close_prices = df['close'].get_values() + + print str(code) + ' ' + 'calcute ma' + ma_short = pd.rolling_mean(close_prices, AVR_SHORT) #12 + ma_long = pd.rolling_mean(close_prices, AVR_LONG) #40 + + df[ 'ma_' + str(AVR_SHORT)] = ma_short + df['ma_' + str(AVR_LONG)] = ma_long + + print str(code) + ' ' + 'calcute ema' + ema_short = pd.ewma(close_prices, span=AVR_SHORT) #12 + ema_long = pd.ewma(close_prices, span=AVR_LONG) #40 + + df['ema_'+str(AVR_SHORT)] = ema_short + df['ema_'+str(AVR_LONG)] = ema_long + + df = df.replace(np.nan, 0) + + #写数据库 + + # 增加列 + table_name =STOCK_KLINE_TABLE + ma_s = 'ma_' + str(AVR_SHORT) + ma_l = 'ma_' + str(AVR_LONG) + ema_s = 'ema_' + str(AVR_SHORT) + ema_l = 'ema_' + str(AVR_LONG) + # try: + # sql = "alter table %s add %s double" % (table_name, ma_s) + # engine.execute(sql) + # sql = "alter table %s add %s double" % (table_name, ma_l) + # engine.execute(sql) + # sql = "alter table %s add %s double" % (table_name, ema_s) + # engine.execute(sql) + # sql = "alter table %s add %s double" % (table_name, ema_l) + # engine.execute(sql) + # except Exception, e: + # str_error = 'column exists' + # print '' + + # 按由近到远的顺序排序 + df = df.sort_index(by='date', ascending=False) + + count = 0 + + #更新数据 + for ix, row in df.iterrows(): + print row + if row[ma_l] == 0 or row[ema_l] == 0: + continue + + sql_update = "update %s set %s=%f,%s=%f,%s=%f,%s=%f where date='%s' and %s='%s'" % \ + (table_name, ma_s, row[ma_s], ma_l, row[ma_l], + ema_s, row[ema_s], ema_l,row[ema_l], \ + row['date'], KEY_CODE, code) + print sql_update + engine.execute(sql_update) + print table_name + ' ' + str(row['date']) + + count = count+1 + #只计算最后1个收盘 + if is_calcute_lastest and count >= 7: + break + except Exception, e: + print (str(code)+":"+date_start+" ~ "+date_end+str(e)) + +# 计算最近日期的均线 +def calcute_ma_lastest_all(): + codes = get_all_stock_codes() + + for code in ChinaStockIndexList: + _calcute_ma_lastest(code) + +# 计算最近日期的均线 (单个) +def _calcute_ma_lastest(code): + + d_avr_long = datetime.date.today() + datetime.timedelta(days=-180) + d_today = datetime.date.today() + date_start = d_avr_long.strftime('%Y-%m-%d') + date_end = d_today.strftime('%Y-%m-%d') + _calcute_ma(code, date_start, date_end, True) + +@wrapcache.wrapcache(timeout=6*60*60) +def calcute_ma(df, avr_short=12, avr_long=40): + """ + 计算ma, ema + :param df: + :return: + """ + if len(df) == 0: + return + + # print "{} calcute ma".format(df.ix[0,'code']) + df['ma_' + str(avr_short)] = pd.rolling_mean(df['close'], avr_short) # 12 + df['ma_' + str(avr_long)] = pd.rolling_mean(df['close'], avr_long) # 40 + + + # print "{} calcute ema".format(df.ix[0, 'code']) + df['ema_' + str(avr_short)] = pd.ewma(df['close'], span=avr_short) # 12 + df['ema_' + str(avr_long)] = pd.ewma(df['close'], span=avr_long) # 40 + + df = df.replace(np.nan, 0) + return df + + + +if __name__ == "__main__": + #_calcute_ma('600000', '2015-01-01', '2015-10-14', True) + calcute_ma_all() + #calcute_ma_lastest_all() #_calcute_ma_lastest('000033') \ No newline at end of file diff --git a/data_process/data_calcute.pyc b/data_process/data_calcute.pyc new file mode 100644 index 0000000..acbde7f Binary files /dev/null and b/data_process/data_calcute.pyc differ diff --git a/data_process/data_download.py b/data_process/data_download.py index d492f4a..41313d1 100644 --- a/data_process/data_download.py +++ b/data_process/data_download.py @@ -1,295 +1,295 @@ -#coding=utf-8 -#!/usr/local/bin/python - -import sys -sys.path.append('../') - -import tushare as ts -import pandas as pd -import datetime -from multiprocessing.dummy import Pool as ThreadPool -from tushare.util import dateu as du -from tqdm import tqdm - -from util import stockutil as util - -from util.stockutil import fn_timer as fn_timer_ - -from init import * -from util.commons import * -from data_calcute import calcute_ma_all - - -################################### -#-- 获取股票基本信息 --# -############################ -def download_stock_basic_info(): - - try: - df = ts.get_stock_basics() - - print df.columns - df[KEY_CODE] = df.index - df = df[[KEY_CODE,KEY_NAME, KEY_INDUSTRY, KEY_AREA, KEY_TimeToMarket]] - - print df.columns - print df.head() - - sql = 'select code from {}'.format(STOCK_BASIC_TABLE) - df_code = pd.read_sql(sql, engine) - df = df[df['code'].apply(lambda x : not x in df_code['code'].get_values())] - print df.head() - print len(df) - if len(df): - df.to_sql(STOCK_BASIC_TABLE, engine, if_exists='append', index=False) - - # 添加指数 - # indexs = [('sh', '上证指数', '指数','全国','19910715'), - # ('sz', '深圳成指', '指数','全国','19940720'), - # ('hs300', '沪深300指数', '指数','全国','20050408'), - # ('sz50', '上证50指数', '指数','全国','20040102'), - # ('zxb', '中小板指数', '指数','全国','20050607'), - # ('cyb', '创业板指数', '指数','全国','20100531'),] - # df = pd.DataFrame(indexs, columns=[KEY_CODE,KEY_NAME, KEY_INDUSTRY, KEY_AREA, KEY_TimeToMarket]) - # print df - # df.to_sql(STOCK_BASIC_TABLE, engine, if_exists='append', index=False) - - - except Exception as e: - print str(e) - - -# 下载单只股票到数据库 -def download_stock_kline_by_code(code, date_start='', date_end=datetime.datetime.now()): - - try: - # 设置日期范围 - if date_start == '': - # 取数据库最近的时间 - sql = "select MAX({0}) as {0} from {1} where {2}='{3}'".format(KEY_DATE, STOCK_KLINE_TABLE, KEY_CODE, code) - df = pd.read_sql_query(sql, engine) - if df is not None and df.ix[0, KEY_DATE] is not None: - date_start = df.ix[0, KEY_DATE] - date_start = datetime.datetime.strptime(str(date_start), "%Y-%m-%d") + datetime.timedelta(1) - date_start = date_start.strftime('%Y-%m-%d') - else: - se = get_stock_info(code) - date_start = se[KEY_TimeToMarket] - date_start = datetime.datetime.strptime(str(date_start), "%Y%m%d") - date_start = date_start.strftime('%Y-%m-%d') - - if isinstance(date_end, datetime.date): - date_end = date_end.strftime('%Y-%m-%d') - - if date_start >= date_end: - print 'Code:{0} is updated to {1}'.format(code, date_start) - return - - # 开始下载 - # 日期分隔成一年以内 - dates = pd.date_range(date_start, date_end) - df = pd.DataFrame(dates, columns=['date']) - df['year'] = df['date'].apply(lambda x : x.year) - print df.head() - - years = list(set(df['year'].get_values())) - years.sort() - - for year in years: - df1 = df[df['year']==year] - if len(df) >= 2: - date_s = str(df1['date'].get_values()[0]) - date_e = str(df1['date'].get_values()[-1]) - date_s = date_s[:10] - date_e = date_e[:10] - print 'download ' + str(code) + ' k-line >>>begin (', date_s + u' 到 ' + date_e + ')' - df_qfq = download_kline_source_select(code, date_s, date_e) - if len(df_qfq): - df_qfq.to_sql(STOCK_KLINE_TABLE, engine, if_exists='append', index=False) - print '\ndownload {} k-line to mysql finish ({} to {})'.format(code, date_s, date_e) - else: - return 'fail... , and try again' - - except Exception as e: - print str(e) - - - # return None - -# 下载源选择 -def download_kline_source_select(code, date_start, date_end): - try: - if len(code)==6: - df_qfq = ts.get_h_data(str(code), start=date_start, end=date_end) # 前复权 - df_hfq = ts.get_h_data(str(code), start=date_start, end=date_end, autype='hfq') # 后复权 - else: - # import pandas.io.data as web - # price = web.get_data_yahoo('000001.SS', '1991-07-15') - df_qfq = ts.get_hist_data(str(code), start=date_start, end=date_end) - if len(df_qfq)==0 or (len(code)==6 and len(df_hfq)==0): - return pd.DataFrame() - #if df_qfq is None: - #df_qfq = ts.get_hist_data(code, start=date_start, end=date_end) - # df_qfq = df_qfq[::-1] - df_qfq[KEY_CODE] = code - df_qfq[KEY_DATE] = df_qfq.index - - - columns = [KEY_CODE, KEY_DATE, KEY_OPEN, KEY_HIGH, KEY_CLOSE, KEY_LOW, KEY_VOLUME] - df_qfq = df_qfq[columns] - - if len(code)==6: - df_qfq['close_hfq'] = df_hfq['close'] - else: - df_qfq['close_hfq'] = df_qfq['close'] - - - print df_qfq.head() - - return df_qfq - except Exception as e: - print str(e) - return pd.DataFrame() - -# 下载股票的历史分笔数据 -# code:股票代码 -# 默认为最近3年的分笔数据 -def download_stock_quotes(code, date_start='', date_end=str(datetime.date.today())): - code = util.getSixDigitalStockCode(code) - try: - if date_start == '': - date = datetime.datetime.today().date() + datetime.timedelta(-365*3) - date_start = str(date) - - dateStart = datetime.datetime.strptime(str(date_start), "%Y-%m-%d") - - for i in range(du.diff_day(date_start, date_end)): - date = dateStart + datetime.timedelta(i) - strDate = date.strftime("%Y-%m-%d") - df = ts.get_tick_data(code, strDate) - print df - except Exception as e: - print str(e) - -####################### -## private methods ## -####################### - -# 获取个股的基本信息：股票名称，行业，地域，PE等，详细如下 -# code,代码 -# name,名称 -# industry,所属行业 -# area,地区 -# pe,市盈率 -# outstanding,流通股本 -# totals,总股本(万) -# totalAssets,总资产(万) -# liquidAssets,流动资产 -# fixedAssets,固定资产 -# reserved,公积金 -# reservedPerShare,每股公积金 -# eps,每股收益 -# bvps,每股净资 -# pb,市净率 -# timeToMarket,上市日期 -# 返回值类型：Series -def get_stock_info(code): - try: - sql = "select * from %s where %s='%s'" % (STOCK_BASIC_TABLE, KEY_CODE, code) - df = pd.read_sql_query(sql, engine) - se = df.ix[0] - except Exception as e: - print str(e) - return se - -# 获取所有股票的历史K线 -@fn_timer_ -def download_all_stock_history_k_line(): - print 'download all stock k-line start' - - try: - - df = pd.read_sql_table(STOCK_BASIC_TABLE, engine) - codes = list(df[KEY_CODE].get_values()) - print 'total stocks:{0}'.format(len(codes)) - # for code in codes: - # download_stock_kline_by_code(code) - codes = codes[::-1] - - #codes = r.lrange(INDEX_STOCK_BASIC, 0, -1) - pool = ThreadPool(processes=10) - pool.map(download_stock_kline_by_code, codes) - pool.close() - pool.join() - - except Exception as e: - print str(e) - print 'download all stock k-line finish' - - -def check_unnormal_stock_price(): - """ - 异常股价检测 - :return: - """ - sql = "select code,name from stock_basic_all" - df_code = pd.read_sql(sql, engine) - - unnormal_codes = [] - for code,name in df_code[['code','name']].get_values(): - sql = "select code, date, close, close_hfq from hq_db.stock_kline_fq where code='{}' and date >'2002-12-06' order by date asc ".format(code) - df = pd.read_sql(sql, engine) - if len(df) < 2: - continue - # print code, len(df) - df.index = df['date'] - returns_close = df['close'].pct_change() - # returns_close_hfq = df['close_hfq'].pct_change() - returns_close[0]= 0 - # returns_close_hfq[0] = 0 - - # print returns_close_hfq[:10] - - df['returns_close'] = returns_close - # df['returns_close_hfq'] = returns_close_hfq - - df1 = df[df['returns_close']==df['returns_close'].min()] - # print df1 - - #print '前复权', min(returns_close), '后复权',min(returns_close_hfq) - if returns_close.min() < -0.2: - print df1 - #先删除code对应的行情 - sql = "delete from stock_kline_fq where code='{}'".format(code) - print sql - engine.execute(sql) - #再重新取 - download_stock_kline_by_code(code) - - # print ">"*10, name, code, '前复权', returns_close.min() - unnormal_codes.append((name, code, '前复权', returns_close.min())) - # if returns_close_hfq.min() < -0.15: - # print df1 - # # print ">"*10, name, code, '后复权', returns_close_hfq.min() - # unnormal_codes.append((name, code, '后复权', returns_close_hfq.min())) - - print "\n"*5 - for un_code in unnormal_codes: - print "{}:{}\t{}\t{}".format(un_code[0], un_code[1], un_code[2], un_code[3]) - - print "total count", len(unnormal_codes) - -if __name__ == '__main__': - - download_stock_basic_info() - download_all_stock_history_k_line() - - check_unnormal_stock_price() - #calcute_ma_all() - #download_stock_kline_to_sql('000002', date_start='1991-01-29',date_end='2012-12-16') - - #convertRedisToSqlite() - - - - +#coding=utf-8 +#!/usr/local/bin/python + +import sys +sys.path.append('../') + +import tushare as ts +import pandas as pd +import datetime +from multiprocessing.dummy import Pool as ThreadPool +from tushare.util import dateu as du +from tqdm import tqdm + +from util import stockutil as util + +from util.stockutil import fn_timer as fn_timer_ + +from init import * +from util.commons import * +from data_calcute import calcute_ma_all + + +################################### +#-- 获取股票基本信息 --# +############################ +def download_stock_basic_info(): + + try: + df = ts.get_stock_basics() + + print df.columns + df[KEY_CODE] = df.index + df = df[[KEY_CODE,KEY_NAME, KEY_INDUSTRY, KEY_AREA, KEY_TimeToMarket]] + + print df.columns + print df.head() + + sql = 'select code from {}'.format(STOCK_BASIC_TABLE) + df_code = pd.read_sql(sql, engine) + df = df[df['code'].apply(lambda x : not x in df_code['code'].get_values())] + print df.head() + print len(df) + if len(df): + df.to_sql(STOCK_BASIC_TABLE, engine, if_exists='append', index=False) + + # 添加指数 + # indexs = [('sh', '上证指数', '指数','全国','19910715'), + # ('sz', '深圳成指', '指数','全国','19940720'), + # ('hs300', '沪深300指数', '指数','全国','20050408'), + # ('sz50', '上证50指数', '指数','全国','20040102'), + # ('zxb', '中小板指数', '指数','全国','20050607'), + # ('cyb', '创业板指数', '指数','全国','20100531'),] + # df = pd.DataFrame(indexs, columns=[KEY_CODE,KEY_NAME, KEY_INDUSTRY, KEY_AREA, KEY_TimeToMarket]) + # print df + # df.to_sql(STOCK_BASIC_TABLE, engine, if_exists='append', index=False) + + + except Exception as e: + print str(e) + + +# 下载单只股票到数据库 +def download_stock_kline_by_code(code, date_start='', date_end=datetime.datetime.now()): + + try: + # 设置日期范围 + if date_start == '': + # 取数据库最近的时间 + sql = "select MAX({0}) as {0} from {1} where {2}='{3}'".format(KEY_DATE, STOCK_KLINE_TABLE, KEY_CODE, code) + df = pd.read_sql_query(sql, engine) + if df is not None and df.ix[0, KEY_DATE] is not None: + date_start = df.ix[0, KEY_DATE] + date_start = datetime.datetime.strptime(str(date_start), "%Y-%m-%d") + datetime.timedelta(1) + date_start = date_start.strftime('%Y-%m-%d') + else: + se = get_stock_info(code) + date_start = se[KEY_TimeToMarket] + date_start = datetime.datetime.strptime(str(date_start), "%Y%m%d") + date_start = date_start.strftime('%Y-%m-%d') + + if isinstance(date_end, datetime.date): + date_end = date_end.strftime('%Y-%m-%d') + + if date_start >= date_end: + print 'Code:{0} is updated to {1}'.format(code, date_start) + return + + # 开始下载 + # 日期分隔成一年以内 + dates = pd.date_range(date_start, date_end) + df = pd.DataFrame(dates, columns=['date']) + df['year'] = df['date'].apply(lambda x : x.year) + print df.head() + + years = list(set(df['year'].get_values())) + years.sort() + + for year in years: + df1 = df[df['year']==year] + if len(df) >= 2: + date_s = str(df1['date'].get_values()[0]) + date_e = str(df1['date'].get_values()[-1]) + date_s = date_s[:10] + date_e = date_e[:10] + print 'download ' + str(code) + ' k-line >>>begin (', date_s + u' 到 ' + date_e + ')' + df_qfq = download_kline_source_select(code, date_s, date_e) + if len(df_qfq): + df_qfq.to_sql(STOCK_KLINE_TABLE, engine, if_exists='append', index=False) + print '\ndownload {} k-line to mysql finish ({} to {})'.format(code, date_s, date_e) + else: + return 'fail... , and try again' + + except Exception as e: + print str(e) + + + # return None + +# 下载源选择 +def download_kline_source_select(code, date_start, date_end): + try: + if len(code)==6: + df_qfq = ts.get_h_data(str(code), start=date_start, end=date_end) # 前复权 + df_hfq = ts.get_h_data(str(code), start=date_start, end=date_end, autype='hfq') # 后复权 + else: + # import pandas.io.data as web + # price = web.get_data_yahoo('000001.SS', '1991-07-15') + df_qfq = ts.get_hist_data(str(code), start=date_start, end=date_end) + if len(df_qfq)==0 or (len(code)==6 and len(df_hfq)==0): + return pd.DataFrame() + #if df_qfq is None: + #df_qfq = ts.get_hist_data(code, start=date_start, end=date_end) + # df_qfq = df_qfq[::-1] + df_qfq[KEY_CODE] = code + df_qfq[KEY_DATE] = df_qfq.index + + + columns = [KEY_CODE, KEY_DATE, KEY_OPEN, KEY_HIGH, KEY_CLOSE, KEY_LOW, KEY_VOLUME] + df_qfq = df_qfq[columns] + + if len(code)==6: + df_qfq['close_hfq'] = df_hfq['close'] + else: + df_qfq['close_hfq'] = df_qfq['close'] + + + print df_qfq.head() + + return df_qfq + except Exception as e: + print str(e) + return pd.DataFrame() + +# 下载股票的历史分笔数据 +# code:股票代码 +# 默认为最近3年的分笔数据 +def download_stock_quotes(code, date_start='', date_end=str(datetime.date.today())): + code = util.getSixDigitalStockCode(code) + try: + if date_start == '': + date = datetime.datetime.today().date() + datetime.timedelta(-365*3) + date_start = str(date) + + dateStart = datetime.datetime.strptime(str(date_start), "%Y-%m-%d") + + for i in range(du.diff_day(date_start, date_end)): + date = dateStart + datetime.timedelta(i) + strDate = date.strftime("%Y-%m-%d") + df = ts.get_tick_data(code, strDate) + print df + except Exception as e: + print str(e) + +####################### +## private methods ## +####################### + +# 获取个股的基本信息：股票名称，行业，地域，PE等，详细如下 +# code,代码 +# name,名称 +# industry,所属行业 +# area,地区 +# pe,市盈率 +# outstanding,流通股本 +# totals,总股本(万) +# totalAssets,总资产(万) +# liquidAssets,流动资产 +# fixedAssets,固定资产 +# reserved,公积金 +# reservedPerShare,每股公积金 +# eps,每股收益 +# bvps,每股净资 +# pb,市净率 +# timeToMarket,上市日期 +# 返回值类型：Series +def get_stock_info(code): + try: + sql = "select * from %s where %s='%s'" % (STOCK_BASIC_TABLE, KEY_CODE, code) + df = pd.read_sql_query(sql, engine) + se = df.ix[0] + except Exception as e: + print str(e) + return se + +# 获取所有股票的历史K线 +@fn_timer_ +def download_all_stock_history_k_line(): + print 'download all stock k-line start' + + try: + + df = pd.read_sql_table(STOCK_BASIC_TABLE, engine) + codes = list(df[KEY_CODE].get_values()) + print 'total stocks:{0}'.format(len(codes)) + # for code in codes: + # download_stock_kline_by_code(code) + codes = codes[::-1] + + #codes = r.lrange(INDEX_STOCK_BASIC, 0, -1) + pool = ThreadPool(processes=10) + pool.map(download_stock_kline_by_code, codes) + pool.close() + pool.join() + + except Exception as e: + print str(e) + print 'download all stock k-line finish' + + +def check_unnormal_stock_price(): + """ + 异常股价检测 + :return: + """ + sql = "select code,name from stock_basic_all" + df_code = pd.read_sql(sql, engine) + + unnormal_codes = [] + for code,name in df_code[['code','name']].get_values(): + sql = "select code, date, close, close_hfq from hq_db.stock_kline_fq where code='{}' and date >'2002-12-06' order by date asc ".format(code) + df = pd.read_sql(sql, engine) + if len(df) < 2: + continue + # print code, len(df) + df.index = df['date'] + returns_close = df['close'].pct_change() + # returns_close_hfq = df['close_hfq'].pct_change() + returns_close[0]= 0 + # returns_close_hfq[0] = 0 + + # print returns_close_hfq[:10] + + df['returns_close'] = returns_close + # df['returns_close_hfq'] = returns_close_hfq + + df1 = df[df['returns_close']==df['returns_close'].min()] + # print df1 + + #print '前复权', min(returns_close), '后复权',min(returns_close_hfq) + if returns_close.min() < -0.2: + print df1 + #先删除code对应的行情 + sql = "delete from stock_kline_fq where code='{}'".format(code) + print sql + engine.execute(sql) + #再重新取 + download_stock_kline_by_code(code) + + # print ">"*10, name, code, '前复权', returns_close.min() + unnormal_codes.append((name, code, '前复权', returns_close.min())) + # if returns_close_hfq.min() < -0.15: + # print df1 + # # print ">"*10, name, code, '后复权', returns_close_hfq.min() + # unnormal_codes.append((name, code, '后复权', returns_close_hfq.min())) + + print "\n"*5 + for un_code in unnormal_codes: + print "{}:{}\t{}\t{}".format(un_code[0], un_code[1], un_code[2], un_code[3]) + + print "total count", len(unnormal_codes) + +if __name__ == '__main__': + + download_stock_basic_info() + download_all_stock_history_k_line() + + check_unnormal_stock_price() + #calcute_ma_all() + #download_stock_kline_to_sql('000002', date_start='1991-01-29',date_end='2012-12-16') + + #convertRedisToSqlite() + + + + diff --git a/data_process/data_get.py b/data_process/data_get.py index 99a206b..045eeb3 100644 --- a/data_process/data_get.py +++ b/data_process/data_get.py @@ -1,119 +1,119 @@ -#!/usr/local/bin/python -#coding=utf-8 - -import datetime - -import pandas as pd -import tushare as ts - -from util.stockutil import getSixDigitalStockCode -from init import * -from util.commons import * -from util.codeConvert import * -from util.helper import fn_timer -import wrapcache - - -# 获取所有股票代码 -@wrapcache.wrapcache(timeout=8*60*60) -def get_all_stock_codes(): - if DB_WAY == 'mysql': - sql = 'select %s from %s' % (KEY_CODE, STOCK_BASIC_TABLE) - df = pd.read_sql_query(sql, engine) - codes = df[KEY_CODE].get_values() - return codes - else: - return [] - - -# 获取个股K线数据 -# input: -# ->code: 股票代码 -# output: -# -> DataFrame -@wrapcache.wrapcache(timeout=8*60*60) -def get_stock_k_line(code, date_start='', date_end='', all_columns = False): - - if len(date_end) == 0: - date_end=datetime.date.today().strftime("%Y-%m-%d") - - try: - if len(date_start) == 0: - sql = "select * from {0} where {1}='{2}' and {3} <= '{4}' order by {3} asc".format( - STOCK_KLINE_TABLE, KEY_CODE, code, KEY_DATE, date_end) - else: - sql = "select * from {0} where {1}='{2}' and {3} > '{4}' and {3} <= '{5}' order by {3} asc".format( - STOCK_KLINE_TABLE, KEY_CODE, code, KEY_DATE, date_start, date_end) - - df = pd.read_sql_query(sql, engine) - return df - except Exception as e: - print str(e) - return None - -@fn_timer -def get_stock_k_line_if_ma_is_null(code): - - sql = 'SELECT min(date) as date FROM {table} where code={code} and ma_12 is NULL'.format(table=STOCK_KLINE_TABLE, code=code) - df = pd.read_sql_query(sql, engine) - - d_end=datetime.datetime.today() - #date_end =d_end.strftime('%Y-%m-%d') - if len(df) > 0: - date_start = df.ix[0, 'date'] - if date_start is None: - return None - - date_start = str(date_start)[:10] - d_start = str_to_datatime(date_start, '%Y-%m-%d') - delta = d_end - d_start - days = delta.days + AVR_LONG + 1 - - try: - sql = "select * from {table} where code='{code}' order by date desc limit {count}".format( - table=STOCK_KLINE_TABLE, code=code, count=days) - - df = pd.read_sql_query(sql, engine) - df = df.sort_index(by='date', ascending=True) - return df - except Exception as e: - print str(e) - return None - -# 获取个股的基本信息：股票名称，行业，地域，PE等，详细如下 -# code,代码 -# name,名称 -# industry,所属行业 -# area,地区 -# pe,市盈率 -# outstanding,流通股本 -# totals,总股本(万) -# totalAssets,总资产(万) -# liquidAssets,流动资产 -# fixedAssets,固定资产 -# reserved,公积金 -# reservedPerShare,每股公积金 -# eps,每股收益 -# bvps,每股净资 -# pb,市净率 -# timeToMarket,上市日期 -# 返回值类型：Series -def get_stock_info(code): - try: - #DB_WAY == 'mysql': - sql = "select * from %s where %s='%s'" % (INDEX_STOCK_BASIC,KEY_CODE, code) - df = pd.read_sql_query(sql, engine) - se = df.ix[0] - - except Exception as e: - print str(e) - se = pd.Series() - return se - -# 获取终止上市股票列表 -def get_stock_terminated(): - return ts.get_terminated() - -if __name__ == "__main__": - print get_stock_k_line('000001') +#!/usr/local/bin/python +#coding=utf-8 + +import datetime + +import pandas as pd +import tushare as ts + +from util.stockutil import getSixDigitalStockCode +from init import * +from util.commons import * +from util.codeConvert import * +from util.helper import fn_timer +import wrapcache + + +# 获取所有股票代码 +@wrapcache.wrapcache(timeout=8*60*60) +def get_all_stock_codes(): + if DB_WAY == 'mysql': + sql = 'select %s from %s' % (KEY_CODE, STOCK_BASIC_TABLE) + df = pd.read_sql_query(sql, engine) + codes = df[KEY_CODE].get_values() + return codes + else: + return [] + + +# 获取个股K线数据 +# input: +# ->code: 股票代码 +# output: +# -> DataFrame +@wrapcache.wrapcache(timeout=8*60*60) +def get_stock_k_line(code, date_start='', date_end='', all_columns = False): + + if len(date_end) == 0: + date_end=datetime.date.today().strftime("%Y-%m-%d") + + try: + if len(date_start) == 0: + sql = "select * from {0} where {1}='{2}' and {3} <= '{4}' order by {3} asc".format( + STOCK_KLINE_TABLE, KEY_CODE, code, KEY_DATE, date_end) + else: + sql = "select * from {0} where {1}='{2}' and {3} > '{4}' and {3} <= '{5}' order by {3} asc".format( + STOCK_KLINE_TABLE, KEY_CODE, code, KEY_DATE, date_start, date_end) + + df = pd.read_sql_query(sql, engine) + return df + except Exception as e: + print str(e) + return None + +@fn_timer +def get_stock_k_line_if_ma_is_null(code): + + sql = 'SELECT min(date) as date FROM {table} where code={code} and ma_12 is NULL'.format(table=STOCK_KLINE_TABLE, code=code) + df = pd.read_sql_query(sql, engine) + + d_end=datetime.datetime.today() + #date_end =d_end.strftime('%Y-%m-%d') + if len(df) > 0: + date_start = df.ix[0, 'date'] + if date_start is None: + return None + + date_start = str(date_start)[:10] + d_start = str_to_datatime(date_start, '%Y-%m-%d') + delta = d_end - d_start + days = delta.days + AVR_LONG + 1 + + try: + sql = "select * from {table} where code='{code}' order by date desc limit {count}".format( + table=STOCK_KLINE_TABLE, code=code, count=days) + + df = pd.read_sql_query(sql, engine) + df = df.sort_index(by='date', ascending=True) + return df + except Exception as e: + print str(e) + return None + +# 获取个股的基本信息：股票名称，行业，地域，PE等，详细如下 +# code,代码 +# name,名称 +# industry,所属行业 +# area,地区 +# pe,市盈率 +# outstanding,流通股本 +# totals,总股本(万) +# totalAssets,总资产(万) +# liquidAssets,流动资产 +# fixedAssets,固定资产 +# reserved,公积金 +# reservedPerShare,每股公积金 +# eps,每股收益 +# bvps,每股净资 +# pb,市净率 +# timeToMarket,上市日期 +# 返回值类型：Series +def get_stock_info(code): + try: + #DB_WAY == 'mysql': + sql = "select * from %s where %s='%s'" % (INDEX_STOCK_BASIC,KEY_CODE, code) + df = pd.read_sql_query(sql, engine) + se = df.ix[0] + + except Exception as e: + print str(e) + se = pd.Series() + return se + +# 获取终止上市股票列表 +def get_stock_terminated(): + return ts.get_terminated() + +if __name__ == "__main__": + print get_stock_k_line('000001') #get_stock_info('000001') \ No newline at end of file diff --git a/data_process/data_get.pyc b/data_process/data_get.pyc new file mode 100644 index 0000000..c38557d Binary files /dev/null and b/data_process/data_get.pyc differ diff --git a/data_process/download_stock.py b/data_process/download_stock.py index 5107ff4..12a26c7 100644 --- a/data_process/download_stock.py +++ b/data_process/download_stock.py @@ -1,203 +1,204 @@ -#!/usr/local/bin/python -#coding=utf-8 -import os -import urllib2 -import datetime -from datetime import date -import time -from matplotlib.cbook import iterable -from multiprocessing import Pool -from multiprocessing.dummy import Pool as ThreadPool -import copy_reg -import urllib -import re - -# def queryAllStock(): -# url = 'http://quote.eastmoney.com/stock_list.html' -# u = urllib.urlopen(url) -# htmlstr = u.read() -# group = re.match('(.*)', htmlstr).group() -# print group - -# 下载所有A股数据 -def downloadAllHistoryAShareData(): - # os.path.pardir: 上级目录 - downloadDir = os.path.pardir + '\stockdata' - stockDownload = StockDownload(downloadDir, date(2014,1,1), date.today()) - -# listSS = range(600000, 604000) -# listSZ = range(000000, 004060) -# listAll = listSS + listSZ - listAll = ['000725','000783','002167','002505','002600','300315','600000','600011','600048','601001'] - - - print time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) - startTime = time.clock() - stockDownload.download_mutli(listAll, 'yahoo') - - endTime = time.clock() - print '多线程执行时间:', (endTime-startTime) - print time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) - -# print '单线程执行开始:' -# startTime = time.clock() -# for i in listAll: -# stockDownload.download(str(i)) -# -# endTime = time.clock() -# print '单线程执行时间:', (endTime-startTime) - - - -# 股票下载类，单只股票 -class StockDownload: - - # 构造 - def __init__(self, downloadDir, startDate=date(2014,1,1), endDate=date.today()): - #self.stockCode = stockCode - self.downloadDir = downloadDir - self.startDate = startDate - self.endDate = endDate - self.ignorelist_file = 'IgnoreListStock.txt' - - #增加忽略列表 - if os.path.exists(self.downloadDir) == False: - os.makedirs(self.downloadDir) - try: - f = open(self.downloadDir + '\\' + self.ignorelist_file) - data = f.read() - self.ignoreList = data.split('\n') - f.close() - except Exception as e: - print str(e) - self.ignoreList = [] - - - - # 多线程下载 - def download_mutli(self, listSockeCode, source='yahoo'): - pool = ThreadPool(processes=5) - if source == 'yahoo': - pool.map(self.downloadFromYahoo, listSockeCode) - elif source == '163': - pool.map(self.downloadFrom163, listSockeCode) - pool.close() - pool.join() - - # 从雅虎API下载 - def downloadFromYahoo(self, stockCode): - stockCode = str(stockCode).zfill(6) #不足6位，前面补零 - -# if stockCode in self.ignoreList: -# print 'ignore:', stockCode -# return; - - if os.path.exists(self.downloadDir) == False: - os.makedirs(self.downloadDir) - stock_file = self.downloadDir + '/' + stockCode + '.csv' - #print stockCode - -# if os.path.exists(stock_file): -# print (">>exist:" + stockCode) -# return - - print (">>download begin:" + stockCode) - - exchange = "SS" if (int(stockCode) // 100000 == 6) else "SZ" - - if iterable(self.endDate): - d1 = (self.startDate[1]-1, self.startDate[2], self.startDate[0]) - else: - d1 = (self.startDate.month-1, self.startDate.day, self.startDate.year) - if iterable(self.endDate): - d2 = (self.endDate[1]-1, self.endDate[2], self.endDate[0]) - else: - d2 = (self.endDate.month-1, self.endDate.day, self.endDate.year) - - g='d' - - ticker = stockCode + '.' + exchange - urlFmt = 'http://ichart.yahoo.com/table.csv?a=%d&b=%d&c=%d&d=%d&e=%d&f=%d&s=%s&y=0&g=%s&ignore=.csv' - - url = urlFmt % (d1[0], d1[1], d1[2], - d2[0], d2[1], d2[2], ticker, g) - - self.down_file(url, stock_file) - - print (">>download finish：" + stockCode) - - # 从网易API下载 - def downloadFrom163(self, stockCode): - stockCode = str(stockCode).zfill(6) #不足6位，前面补零 - - stockCode163 = stockCode - if int(stockCode) // 100000 == 0: - stockCode163 = '1'+stockCode - else: - stockCode163 = '0'+stockCode - -# if stockCode in self.ignoreList: -# print 'ignore:', stockCode -# return; - - if os.path.exists(self.downloadDir) == False: - os.makedirs(self.downloadDir) - stock_file = self.downloadDir + '/' + stockCode + '.csv' - #print stockCode - -# if os.path.exists(stock_file): -# print (">>exist:" + stockCode) -# return - - print (">>download begin:" + stockCode) - - strDateStart = self.startDate.strftime('%Y%m%d') - strDateEnd = self.endDate.strftime('%Y%m%d') - - urlFmt = 'http://quotes.money.163.com/service/chddata.html?code=%s&start=%s&end=%s' - - url = urlFmt % (stockCode163,strDateStart, strDateEnd) - - self.down_file(url, stock_file) - - print (">>download finish：" + stockCode) - - # url:下载路径 - # file_name:保存到本地的文件名 - # 找不到股票代码则放入忽略列表 - def down_file(self,url, file_name): - - try: - u = urllib2.urlopen(url) - f = open(file_name, 'wb') - - file_size_dl = 0 - block_sz = 8192 - while True: - buffer = u.read(block_sz) - if not buffer: - break - - file_size_dl += len(buffer) - f.write(buffer) - f.close() - except Exception as e: - print str(e) - - basename = os.path.basename(os.path.splitext(file_name)[0]) #股票代码 - - f = open(self.downloadDir + '/' + self.ignorelist_file, 'a') # a，文件尾部插入，不覆盖原数据 - f.write('\n' + basename) - f.close() - finally: - None - -# def deleteFile(self,fileName): -# try: -# os.remove(self.stockCode + ".csv") -# except Exception as e: -# pass - - -if __name__ == "__main__": - downloadAllHistoryAShareData() \ No newline at end of file +#!/usr/local/bin/python +#coding=utf-8 +#下载股票历史数据 +import os +import urllib2 +import datetime +from datetime import date +import time +from matplotlib.cbook import iterable +from multiprocessing import Pool +from multiprocessing.dummy import Pool as ThreadPool +import copy_reg +import urllib +import re + +# def queryAllStock(): +# url = 'http://quote.eastmoney.com/stock_list.html' +# u = urllib.urlopen(url) +# htmlstr = u.read() +# group = re.match('(.*)', htmlstr).group() +# print group + +# 下载所有A股数据 +# listAll ：股票代码 +def downloadAllHistoryAShareData(listAll): + # os.path.pardir: 上级目录 + downloadDir = os.path.pardir + '\stockdata' + stockDownload = StockDownload(downloadDir, date(2017,1,1), date.today()) + +# listSS = range(600000, 604000) +# listSZ = range(000000, 004060) +# listAll = listSS + listSZ +# listAll = ['000725','000783','002167','002505','002600','300315','600000','600011','600048','601001'] + + print time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) + startTime = time.clock() + stockDownload.download_mutli(listAll, 'yahoo') + + endTime = time.clock() + print '多线程执行时间:', (endTime-startTime) + print time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) + +# print '单线程执行开始:' +# startTime = time.clock() +# for i in listAll: +# stockDownload.download(str(i)) +# +# endTime = time.clock() +# print '单线程执行时间:', (endTime-startTime) + + + +# 股票下载类，单只股票 +class StockDownload: + + # 构造 + def __init__(self, downloadDir, startDate=date(2016,1,1), endDate=date.today()): + #self.stockCode = stockCode + self.downloadDir = downloadDir + self.startDate = startDate + self.endDate = endDate + self.ignorelist_file = 'IgnoreListStock.txt' + + #增加忽略列表 + if os.path.exists(self.downloadDir) == False: + os.makedirs(self.downloadDir) + try: + f = open(self.downloadDir + '\\' + self.ignorelist_file) + data = f.read() + self.ignoreList = data.split('\n') + f.close() + except Exception as e: + print str(e) + self.ignoreList = [] + + # 多线程下载 + def download_mutli(self, listSockeCode, source='yahoo'): + pool = ThreadPool(processes=5) + if source == 'yahoo': + pool.map(self.downloadFromYahoo, listSockeCode) + + elif source == '163': + pool.map(self.downloadFrom163, listSockeCode) + pool.close() + pool.join() + + # 从雅虎API下载 + def downloadFromYahoo(self, stockCode): + stockCode = str(stockCode).zfill(6) #不足6位，前面补零 + +# if stockCode in self.ignoreList: +# print 'ignore:', stockCode +# return; + + if os.path.exists(self.downloadDir) == False: + os.makedirs(self.downloadDir) + stock_file = self.downloadDir + '/' + stockCode + '.csv' + #print stockCode + +# if os.path.exists(stock_file): +# print (">>exist:" + stockCode) +# return + + print (">>download begin:" + stockCode) + + exchange = "SS" if (int(stockCode) // 100000 == 6) else "SZ" + + if iterable(self.endDate): + d1 = (self.startDate[1]-1, self.startDate[2], self.startDate[0]) + else: + d1 = (self.startDate.month-1, self.startDate.day, self.startDate.year) + if iterable(self.endDate): + d2 = (self.endDate[1]-1, self.endDate[2], self.endDate[0]) + else: + d2 = (self.endDate.month-1, self.endDate.day, self.endDate.year) + + g='d' + + ticker = stockCode + '.' + exchange + urlFmt = 'http://ichart.yahoo.com/table.csv?a=%d&b=%d&c=%d&d=%d&e=%d&f=%d&s=%s&y=0&g=%s&ignore=.csv' + + url = urlFmt % (d1[0], d1[1], d1[2], + d2[0], d2[1], d2[2], ticker, g) + + if(not self.down_file(url, stock_file)): + self.downloadFrom163(stockCode) + print (">>download finish：" + stockCode) + + # 从网易API下载 + def downloadFrom163(self, stockCode): + stockCode = str(stockCode).zfill(6) #不足6位，前面补零 + + stockCode163 = stockCode + if int(stockCode) // 100000 == 0: + stockCode163 = '1'+stockCode + else: + stockCode163 = '0'+stockCode + +# if stockCode in self.ignoreList: +# print 'ignore:', stockCode +# return; + + if os.path.exists(self.downloadDir) == False: + os.makedirs(self.downloadDir) + stock_file = self.downloadDir + '/' + stockCode + '.csv' + #print stockCode + +# if os.path.exists(stock_file): +# print (">>exist:" + stockCode) +# return + + print (">>download begin:" + stockCode) + + strDateStart = self.startDate.strftime('%Y%m%d') + strDateEnd = self.endDate.strftime('%Y%m%d') + + urlFmt = 'http://quotes.money.163.com/service/chddata.html?code=%s&start=%s&end=%s' + + url = urlFmt % (stockCode163,strDateStart, strDateEnd) + + self.down_file(url, stock_file) + + print (">>download finish：" + stockCode) + + # url:下载路径 + # file_name:保存到本地的文件名 + # 找不到股票代码则放入忽略列表 + def down_file(self,url, file_name): + + try: + u = urllib2.urlopen(url) + f = open(file_name, 'wb') + + file_size_dl = 0 + block_sz = 8192 + while True: + buffer = u.read(block_sz) + if not buffer: + break + + file_size_dl += len(buffer) + f.write(buffer) + f.close() + except Exception as e: + print str(e) + + # basename = os.path.basename(os.path.splitext(file_name)[0]) #股票代码 + # + # f = open(self.downloadDir + '/' + self.ignorelist_file, 'a') # a，文件尾部插入，不覆盖原数据 + # f.write('\n' + basename) + # f.close() + finally: + None + +# def deleteFile(self,fileName): +# try: +# os.remove(self.stockCode + ".csv") +# except Exception as e: +# pass + + +if __name__ == "__main__": + listAll = ['000725', '000783', '002167', '002505', '002600', '300315', '600000', '600011', '600048', '601001'] + downloadAllHistoryAShareData(listAll) \ No newline at end of file diff --git a/data_process/download_stock.pyc b/data_process/download_stock.pyc new file mode 100644 index 0000000..e2bf55a Binary files /dev/null and b/data_process/download_stock.pyc differ diff --git a/data_process/get_all_china_stock_code.py b/data_process/get_all_china_stock_code.py index 44a032e..4ae41aa 100644 --- a/data_process/get_all_china_stock_code.py +++ b/data_process/get_all_china_stock_code.py @@ -1,198 +1,198 @@ -#!/usr/local/bin/python -#coding=utf-8 - -import urllib2 -import BeautifulSoup -import re -import sys -import chardet - -import logging -import scrapy -from scrapy.spiders import Spider -from scrapy.selector import Selector -#from scrapy import log - -from scrapy.downloadermiddlewares.useragent import UserAgentMiddleware -import random as rd -import codecs -import json - -# http://app.finance.ifeng.com/list/stock.php?t=ha&f=symbol&o=asc&p=1 - -#url = 'http://quote.eastmoney.com/stock_list.html' -# url = 'http://app.finance.ifeng.com/list/stock.php?t=ha&f=symbol&o=asc&p=1' -# -# #设置头文件，模拟浏览器访问，防止封IP -# req_header = {'User-Agent':'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11', -# 'Accept':'text/html;q=0.9,*/*;q=0.8', -# 'Accept-Charset':'ISO-8859-1,utf-8;q=0.7,*;q=0.3', -# 'Accept-Encoding':'gb2312', -# 'Connection':'close', -# 'Referer':None #注意如果依然不能抓取的话，这里可以设置抓取网站的host -# } -# -# #建立连接请求，这时返回页面信息给con这个变量，con是一个对象 -# req = urllib2.Request(url, headers=req_header) -# con = urllib2.urlopen(req) -# -# #对con对象调用read()方法，返回的就是html页面，也就是有html标签的纯文本 -# doc = con.read() -# typeEncode = sys.getfilesystemencoding()##系统默认编码 -# infoencode = chardet.detect(doc).get('encoding','utf-8') #通过第3方模块来自动提取网页的编码 -# html = doc.decode(infoencode, 'ignore').encode(typeEncode)##先转换成unicode编码，然后转换系统编码输出 ---------->方式一 -# #html = doc.decode('gb2312')#.encode('utf-8') #先转成gb2312编码，然后转换unicode编码输出 ---------->方式二 -# #html = unicode(doc,'GBK').encode('UTF-8') ----------->方式三 -# print html -# -# # 生成一个soup对象 -# # soup = BeautifulSoup.BeautifulSoup(html) -# # for link in soup.findAll('a'): -# # if link.get('target') == "_blank": -# # print (link.get('href')) -# -# # paper_desc = soup.html.body.find('div', {'class' : 'quotebody'}).text -# # #stock_list = re.findall(r'\.html\"\>.*\<\/a\>\<\/li\>', html) -# # stock_list = re.findall(r'\>.*\(\d{6}\)\<\/a\>', html) -# # print paper_desc -# -# #关闭连接 -# con.close() - -# Define here the models for your scraped items -# -# See documentation in: -# http://doc.scrapy.org/en/latest/topics/items.html - -class TTjjItem(scrapy.Item): - stockCode = scrapy.Field() - stockName = scrapy.Field() - -class TTJJi(Spider): - - name = "TTJJ" - allowed_domains=['eastmoney.com'] - start_urls = ["http://quote.eastmoney.com/stocklist.html#sh"] - - def parse(self, response): - - sel = Selector(response) - cont=sel.xpath('//div[@class="qox"]/div[@class="quotebody"]/div/ul')[0].extract() - item = TTjjItem() - - for ii in re.findall(r'
  • .*?(.*?)',cont): - item["stockName"]=ii.split("(")[0].encode('utf-8') - item["stockCode"]=("sh"+ii.split("(")[1][:-1]).encode('utf-8') - #log.msg(ii.encode('utf-8'),level="INFO") - yield item - - #item["stockCode"]="+------------------------------------------------------------------+" - #yield item - cont1=sel.xpath('//div[@class="qox"]/div[@class="quotebody"]/div/ul')[1].extract() - - for iii in re.findall(r'
  • .*?(.*?)',cont1): - item["stockName"]=iii.split("(")[0].encode('utf-8') - item["stockCode"]=("sz"+iii.split("(")[1][:-1]).encode('utf-8') - #log.msg(iii.encode('utf-8'),level="INFO") - yield item - -class UserAgentMiddle(UserAgentMiddleware): - - def __init__(self, user_agent=''): - self.user_agent = user_agent - - def process_request(self, request, spider): - ua = rd.choice(self.user_agent_list) - if ua: - #显示当前使用的useragent - print "********Current UserAgent:%s************" %ua - - #记录 - #log.msg('Current UserAgent: '+ua, level='INFO') - request.headers.setdefault('User-Agent', ua) - - #the default user_agent_list composes chrome,I E,firefox,Mozilla,opera,netscape - #for more user agent strings,you can find it in http://www.useragentstring.com/pages/useragentstring.php - user_agent_list = [\ - "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 " - "(KHTML, like Gecko) Chrome/22.0.1207.1 Safari/537.1", - "Mozilla/5.0 (X11; CrOS i686 2268.111.0) AppleWebKit/536.11 " - "(KHTML, like Gecko) Chrome/20.0.1132.57 Safari/536.11", - "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.6 " - "(KHTML, like Gecko) Chrome/20.0.1092.0 Safari/536.6", - "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.6 " - "(KHTML, like Gecko) Chrome/20.0.1090.0 Safari/536.6", - "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.1 " - "(KHTML, like Gecko) Chrome/19.77.34.5 Safari/537.1", - "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/536.5 " - "(KHTML, like Gecko) Chrome/19.0.1084.9 Safari/536.5", - "Mozilla/5.0 (Windows NT 6.0) AppleWebKit/536.5 " - "(KHTML, like Gecko) Chrome/19.0.1084.36 Safari/536.5", - "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 " - "(KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3", - "Mozilla/5.0 (Windows NT 5.1) AppleWebKit/536.3 " - "(KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3", - "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0) AppleWebKit/536.3 " - "(KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3", - "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 " - "(KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3", - "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 " - "(KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3", - "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 " - "(KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3", - "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 " - "(KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3", - "Mozilla/5.0 (Windows NT 6.1) AppleWebKit/536.3 " - "(KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3", - "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 " - "(KHTML, like Gecko) Chrome/19.0.1061.0 Safari/536.3", - "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/535.24 " - "(KHTML, like Gecko) Chrome/19.0.1055.1 Safari/535.24", - "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/535.24 " - "(KHTML, like Gecko) Chrome/19.0.1055.1 Safari/535.24" - ] - - -# Define your item pipelines here -# -# Don't forget to add your pipeline to the ITEM_PIPELINES setting -# See: http://doc.scrapy.org/en/latest/topics/item-pipeline.html - -class TtjjPipeline(object): - - def __init__(self): - - self.file=codecs.open("TTJJ.json",mode="wb",encoding='utf-8') - self.file.write('{"hah"'+':[') - - - def process_item(self, item, spider): - line = json.dumps(dict(item))+"," - self.file.write(line.decode("unicode_escape")) - - return item - - - -# Scrapy settings for TTJJ project -# -# For simplicity, this file contains only the most important settings by -# default. All the other settings are documented here: -# -# http://doc.scrapy.org/en/latest/topics/settings.html -# - -BOT_NAME = 'TTJJ' - -SPIDER_MODULES = ['TTJJ.spiders'] -NEWSPIDER_MODULE = 'TTJJ.spiders' -download_delay=1 -ITEM_PIPELINES={'TTJJ.pipelines.TtjjPipeline':300} -COOKIES_ENABLED=False -# Crawl responsibly by identifying yourself (and your website) on the user-agent -#USER_AGENT = 'TTJJ (+http://www.yourdomain.com)' -#取消默认的useragent,使用新的useragent -DOWNLOADER_MIDDLEWARES = { - 'scrapy.contrib.downloadermiddleware.useragent.UserAgentMiddleware' : None, - 'TTJJ.spiders.UserAgentMiddle.UserAgentMiddle':400 +#!/usr/local/bin/python +#coding=utf-8 + +import urllib2 +import BeautifulSoup +import re +import sys +import chardet + +import logging +import scrapy +from scrapy.spiders import Spider +from scrapy.selector import Selector +#from scrapy import log + +from scrapy.downloadermiddlewares.useragent import UserAgentMiddleware +import random as rd +import codecs +import json + +# http://app.finance.ifeng.com/list/stock.php?t=ha&f=symbol&o=asc&p=1 + +#url = 'http://quote.eastmoney.com/stock_list.html' +# url = 'http://app.finance.ifeng.com/list/stock.php?t=ha&f=symbol&o=asc&p=1' +# +# #设置头文件，模拟浏览器访问，防止封IP +# req_header = {'User-Agent':'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11', +# 'Accept':'text/html;q=0.9,*/*;q=0.8', +# 'Accept-Charset':'ISO-8859-1,utf-8;q=0.7,*;q=0.3', +# 'Accept-Encoding':'gb2312', +# 'Connection':'close', +# 'Referer':None #注意如果依然不能抓取的话，这里可以设置抓取网站的host +# } +# +# #建立连接请求，这时返回页面信息给con这个变量，con是一个对象 +# req = urllib2.Request(url, headers=req_header) +# con = urllib2.urlopen(req) +# +# #对con对象调用read()方法，返回的就是html页面，也就是有html标签的纯文本 +# doc = con.read() +# typeEncode = sys.getfilesystemencoding()##系统默认编码 +# infoencode = chardet.detect(doc).get('encoding','utf-8') #通过第3方模块来自动提取网页的编码 +# html = doc.decode(infoencode, 'ignore').encode(typeEncode)##先转换成unicode编码，然后转换系统编码输出 ---------->方式一 +# #html = doc.decode('gb2312')#.encode('utf-8') #先转成gb2312编码，然后转换unicode编码输出 ---------->方式二 +# #html = unicode(doc,'GBK').encode('UTF-8') ----------->方式三 +# print html +# +# # 生成一个soup对象 +# # soup = BeautifulSoup.BeautifulSoup(html) +# # for link in soup.findAll('a'): +# # if link.get('target') == "_blank": +# # print (link.get('href')) +# +# # paper_desc = soup.html.body.find('div', {'class' : 'quotebody'}).text +# # #stock_list = re.findall(r'\.html\"\>.*\<\/a\>\<\/li\>', html) +# # stock_list = re.findall(r'\>.*\(\d{6}\)\<\/a\>', html) +# # print paper_desc +# +# #关闭连接 +# con.close() + +# Define here the models for your scraped items +# +# See documentation in: +# http://doc.scrapy.org/en/latest/topics/items.html + +class TTjjItem(scrapy.Item): + stockCode = scrapy.Field() + stockName = scrapy.Field() + +class TTJJi(Spider): + + name = "TTJJ" + allowed_domains=['eastmoney.com'] + start_urls = ["http://quote.eastmoney.com/stocklist.html#sh"] + + def parse(self, response): + + sel = Selector(response) + cont=sel.xpath('//div[@class="qox"]/div[@class="quotebody"]/div/ul')[0].extract() + item = TTjjItem() + + for ii in re.findall(r'
  • .*?(.*?)',cont): + item["stockName"]=ii.split("(")[0].encode('utf-8') + item["stockCode"]=("sh"+ii.split("(")[1][:-1]).encode('utf-8') + #log.msg(ii.encode('utf-8'),level="INFO") + yield item + + #item["stockCode"]="+------------------------------------------------------------------+" + #yield item + cont1=sel.xpath('//div[@class="qox"]/div[@class="quotebody"]/div/ul')[1].extract() + + for iii in re.findall(r'
  • .*?(.*?)',cont1): + item["stockName"]=iii.split("(")[0].encode('utf-8') + item["stockCode"]=("sz"+iii.split("(")[1][:-1]).encode('utf-8') + #log.msg(iii.encode('utf-8'),level="INFO") + yield item + +class UserAgentMiddle(UserAgentMiddleware): + + def __init__(self, user_agent=''): + self.user_agent = user_agent + + def process_request(self, request, spider): + ua = rd.choice(self.user_agent_list) + if ua: + #显示当前使用的useragent + print "********Current UserAgent:%s************" %ua + + #记录 + #log.msg('Current UserAgent: '+ua, level='INFO') + request.headers.setdefault('User-Agent', ua) + + #the default user_agent_list composes chrome,I E,firefox,Mozilla,opera,netscape + #for more user agent strings,you can find it in http://www.useragentstring.com/pages/useragentstring.php + user_agent_list = [\ + "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 " + "(KHTML, like Gecko) Chrome/22.0.1207.1 Safari/537.1", + "Mozilla/5.0 (X11; CrOS i686 2268.111.0) AppleWebKit/536.11 " + "(KHTML, like Gecko) Chrome/20.0.1132.57 Safari/536.11", + "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.6 " + "(KHTML, like Gecko) Chrome/20.0.1092.0 Safari/536.6", + "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.6 " + "(KHTML, like Gecko) Chrome/20.0.1090.0 Safari/536.6", + "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.1 " + "(KHTML, like Gecko) Chrome/19.77.34.5 Safari/537.1", + "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/536.5 " + "(KHTML, like Gecko) Chrome/19.0.1084.9 Safari/536.5", + "Mozilla/5.0 (Windows NT 6.0) AppleWebKit/536.5 " + "(KHTML, like Gecko) Chrome/19.0.1084.36 Safari/536.5", + "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 " + "(KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3", + "Mozilla/5.0 (Windows NT 5.1) AppleWebKit/536.3 " + "(KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3", + "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0) AppleWebKit/536.3 " + "(KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3", + "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 " + "(KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3", + "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 " + "(KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3", + "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 " + "(KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3", + "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 " + "(KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3", + "Mozilla/5.0 (Windows NT 6.1) AppleWebKit/536.3 " + "(KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3", + "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 " + "(KHTML, like Gecko) Chrome/19.0.1061.0 Safari/536.3", + "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/535.24 " + "(KHTML, like Gecko) Chrome/19.0.1055.1 Safari/535.24", + "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/535.24 " + "(KHTML, like Gecko) Chrome/19.0.1055.1 Safari/535.24" + ] + + +# Define your item pipelines here +# +# Don't forget to add your pipeline to the ITEM_PIPELINES setting +# See: http://doc.scrapy.org/en/latest/topics/item-pipeline.html + +class TtjjPipeline(object): + + def __init__(self): + + self.file=codecs.open("TTJJ.json",mode="wb",encoding='utf-8') + self.file.write('{"hah"'+':[') + + + def process_item(self, item, spider): + line = json.dumps(dict(item))+"," + self.file.write(line.decode("unicode_escape")) + + return item + + + +# Scrapy settings for TTJJ project +# +# For simplicity, this file contains only the most important settings by +# default. All the other settings are documented here: +# +# http://doc.scrapy.org/en/latest/topics/settings.html +# + +BOT_NAME = 'TTJJ' + +SPIDER_MODULES = ['TTJJ.spiders'] +NEWSPIDER_MODULE = 'TTJJ.spiders' +download_delay=1 +ITEM_PIPELINES={'TTJJ.pipelines.TtjjPipeline':300} +COOKIES_ENABLED=False +# Crawl responsibly by identifying yourself (and your website) on the user-agent +#USER_AGENT = 'TTJJ (+http://www.yourdomain.com)' +#取消默认的useragent,使用新的useragent +DOWNLOADER_MIDDLEWARES = { + 'scrapy.contrib.downloadermiddleware.useragent.UserAgentMiddleware' : None, + 'TTJJ.spiders.UserAgentMiddle.UserAgentMiddle':400 } \ No newline at end of file diff --git a/data_process/native_data.py b/data_process/native_data.py index 899d6f1..c2dad1a 100644 --- a/data_process/native_data.py +++ b/data_process/native_data.py @@ -1,34 +1,34 @@ -#!/usr/local/bin/python -#coding=utf-8 -import os -#import matplotlib.mlab as mlab -import sys -#import chardet - -# 获取csv文件序列 -def getCsvDataBySplitPath(stock_code, dataPath): - fh = open(dataPath + '\\' + stock_code +".csv", 'r') - r = mlab.csv2rec(fh); - fh.close() - r.sort() #按时间由远及近排序 - - #比如读取收盘价 prices = r.adj_price - return r - -# 获取csv文件序列 -def getCsvDataByFullPath(csvName): - fh = open(csvName, 'r') - -# typeEncode = sys.getfilesystemencoding()##系统默认编码 -# infoencode = chardet.detect(fh).get('encoding','utf-8') #通过第3方模块来自动提取网页的编码 -# fh = fh.decode(infoencode, 'ignore').encode(typeEncode)##先转换成unicode编码，然后转换系统编码输出 ---------->方式一 - - r = mlab.csv2rec(fh); - fh.close() - r.sort() #按时间由远及近排序 - - #比如读取收盘价 prices = r.adj_price - return r - -if __name__ == "__main__": +#!/usr/local/bin/python +#coding=utf-8 +import os +#import matplotlib.mlab as mlab +import sys +#import chardet + +# 获取csv文件序列 +def getCsvDataBySplitPath(stock_code, dataPath): + fh = open(dataPath + '\\' + stock_code +".csv", 'r') + r = mlab.csv2rec(fh); + fh.close() + r.sort() #按时间由远及近排序 + + #比如读取收盘价 prices = r.adj_price + return r + +# 获取csv文件序列 +def getCsvDataByFullPath(csvName): + fh = open(csvName, 'r') + +# typeEncode = sys.getfilesystemencoding()##系统默认编码 +# infoencode = chardet.detect(fh).get('encoding','utf-8') #通过第3方模块来自动提取网页的编码 +# fh = fh.decode(infoencode, 'ignore').encode(typeEncode)##先转换成unicode编码，然后转换系统编码输出 ---------->方式一 + + r = mlab.csv2rec(fh); + fh.close() + r.sort() #按时间由远及近排序 + + #比如读取收盘价 prices = r.adj_price + return r + +if __name__ == "__main__": print 'data center' \ No newline at end of file diff --git a/data_process/native_data.pyc b/data_process/native_data.pyc new file mode 100644 index 0000000..7e1acec Binary files /dev/null and b/data_process/native_data.pyc differ diff --git a/data_process/online_data.py b/data_process/online_data.py index fe9c198..f89d906 100644 --- a/data_process/online_data.py +++ b/data_process/online_data.py @@ -1,152 +1,147 @@ -#!/usr/local/bin/python -#coding=utf-8 - -import urllib2 -import re -import Stock as st -import tushare as ts -import pandas as pd -import time -import wrapcache -from retrying import retry - -from util.stockutil import fn_timer as fn_timer_ -import util.commons as cm -import util.stockutil as util - -#获取实时股价(同时获取多只股票) -def getLiveMutliChinaStockPrice(stockCodeList): - - - - try: - - stockCodeListEx = [] - for stockCode in stockCodeList: - if len(str(stockCode)) == 6: - exchange = "sh" if (int(stockCode) // 100000 == 6) else "sz" - stockCode = exchange + str(stockCode) - stockCodeListEx.append(stockCode) - strStockCode = ','.join(stockCodeListEx) - dataUrl = "http://hq.sinajs.cn/list=%s" % (strStockCode) - stdout = urllib2.urlopen(dataUrl) - stdoutInfo = stdout.read().decode('gb2312').encode('utf-8') - stdoutInfoList = stdoutInfo.splitlines() - - stockClassList = [] - - index = 0 - for eachLine in stdoutInfoList: - - # 正则表达式说明 - # 搜索 “ ”双引号内的字符串，包含换行符，将匹配的字符串分为三组：用（）表示 - # group(2)：取第二组数据 - tempData = re.search('''(")(.+)(")''', eachLine).group(2) - stockInfo = tempData.split(",") - stock = st.Stock(stockInfo) - stock.code = stockCodeList[index] - index += 1 - if stock.current > 0.0: - stockClassList.append(stock) - else: - print stock.code , 'stop' - return stockClassList - except Exception as e: - #print str(e) - return [] - -#获取实时股价 -def getLiveChinaStockPrice(stockCode): - - try: - exchange = "sh" if (int(stockCode) // 100000 == 6) else "sz" - dataUrl = "http://hq.sinajs.cn/list=" + exchange + stockCode - stdout = urllib2.urlopen(dataUrl) - stdoutInfo = stdout.read().decode('gb2312').encode('utf-8') - - # 正则表达式说明 - # 搜索 “ ”双引号内的字符串，包含换行符，将匹配的字符串分为三组：用（）表示 - # group(2)：取第二组数据 - tempData = re.search('''(")(.+)(")''', stdoutInfo).group(2) - stockInfo = tempData.split(",") - - #bb[0]:股票名 bb[1]:今日开盘价 bb[2]：昨日收盘价 bb[3]:当前价格 bb[4]:今日最高价 bb[5]:今日最低价 - #bb[6]:买一报价 bb[7]:卖一报价 bb[8]:成交股票数/100 bb[9]:成交金额/w bb[10]:买一申请股数 bb[11]:买一报价 - #bb[12]:买二股数 bb[13]:买二报价 bb[14]:买三股数 bb[15]:买三报价 bb[16]:买四申请股数 bb[17]:买四报价 - #bb[18]:买五股数 bb[19]:买五报价 bb[20]:卖一股数 bb[21]:卖一报价 bb[22]:卖二申请股数 bb[23]:卖二报价 - #bb[24]:卖三股数 bb[25]:卖三报价 bb[26]:卖四股数 bb[27]:卖四报价 bb[28]:卖五股数 bb[29]:卖五报价 - #bb[30]:日期 bb[31]:时间 bb[8]:不知道 - - return st.Stock(stockInfo) - - except Exception as e: - print(">>>>>> Exception: " + str(e)) - finally: - None - -# 获取A股所有股票的实时股价 -# 通过 ts.get_today_all 获取 -@wrapcache.wrapcache(timeout=5*60) -@retry(stop_max_attempt_number=10) -def get_real_price_dataframe(): - df = ts.get_today_all() - return df - -@fn_timer_ -def getAllChinaStock(): - - stockList = [] - try: - df = get_real_price_dataframe() - for se in df.get_values(): - stock = st.Stock('') - stock.code = se[0] - stock.name = se[1] - stock.current = se[3] - stock.open = se[4] - stock.high = se[5] - stock.low = se[6] - stock.close = se[7] - stock.dealAmount = se[8]/100 - stock.time = time.localtime(time.time()) #时间 - #print stock - - stockList.append(stock) - except: - print 'get real price timeout' - - return stockList - -# 获取A股所有股票的实时股价 -# 通过get_realtime_quotes接口获取 -def getAllChinaStock2(): - df_list = pd.read_csv(cm.DownloadDir + cm.TABLE_STOCKS_BASIC + '.csv') - stockList = df_list['code'].values; - stockList_group = util.group_list(stockList, 20) - print len(stockList_group) - print stockList_group[1] - stockList = [] - for group in stockList_group: - df = ts.get_realtime_quotes(group) - - for se in df.get_values(): - stock = st.Stock('') - stock.code = se[0] - stock.name = se[1] - stock.current = se[3] - stock.open = se[4] - stock.high = se[5] - stock.low = se[6] - stock.close = se[7] - stock.dealAmount = se[8]/100 - stock.time = time.localtime(time.time()) #时间 - #print stock - stockList.append(stock) - return stockList - -if __name__ == "__main__": - getAllChinaStock() - - -#df = ts.get_realtime_quotes('002600') +#!/usr/local/bin/python +#coding=utf-8 + +import urllib2 +import re +import Stock as st +import tushare as ts +import pandas as pd +import time +import wrapcache +from retrying import retry + +from util.stockutil import fn_timer as fn_timer_ +import util.commons as cm +import util.stockutil as util + +#获取实时股价(同时获取多只股票) +def getLiveMutliChinaStockPrice(stockCodeList): + try: + stockCodeListEx = [] + for stockCode in stockCodeList: + if len(str(stockCode)) == 6: + exchange = "sh" if (int(stockCode) // 100000 == 6) else "sz" + stockCode = exchange + str(stockCode) + stockCodeListEx.append(stockCode) + strStockCode = ','.join(stockCodeListEx) + dataUrl = "http://hq.sinajs.cn/list=%s" % (strStockCode) + stdout = urllib2.urlopen(dataUrl) + stdoutInfo = stdout.read().decode('gb2312').encode('utf-8') + stdoutInfoList = stdoutInfo.splitlines() + + stockClassList = [] + + index = 0 + for eachLine in stdoutInfoList: + + # 正则表达式说明 + # 搜索 “ ”双引号内的字符串，包含换行符，将匹配的字符串分为三组：用（）表示 + # group(2)：取第二组数据 + tempData = re.search('''(")(.+)(")''', eachLine).group(2) + stockInfo = tempData.split(",") + stock = st.Stock(stockInfo) + stock.code = stockCodeList[index] + index += 1 + if stock.current > 0.0: + stockClassList.append(stock) + else: + print stock.code , 'stop' + return stockClassList + except Exception as e: + #print str(e) + return [] + +#获取实时股价 +def getLiveChinaStockPrice(stockCode): + try: + exchange = "sh" if (int(stockCode) // 100000 == 6) else "sz" + dataUrl = "http://hq.sinajs.cn/list=" + exchange + stockCode + stdout = urllib2.urlopen(dataUrl) + stdoutInfo = stdout.read().decode('gb2312').encode('utf-8') + + # 正则表达式说明 + # 搜索 “ ”双引号内的字符串，包含换行符，将匹配的字符串分为三组：用（）表示 + # group(2)：取第二组数据 + tempData = re.search('''(")(.+)(")''', stdoutInfo).group(2) + stockInfo = tempData.split(",") + + #bb[0]:股票名 bb[1]:今日开盘价 bb[2]：昨日收盘价 bb[3]:当前价格 bb[4]:今日最高价 bb[5]:今日最低价 + #bb[6]:买一报价 bb[7]:卖一报价 bb[8]:成交股票数/100 bb[9]:成交金额/w bb[10]:买一申请股数 bb[11]:买一报价 + #bb[12]:买二股数 bb[13]:买二报价 bb[14]:买三股数 bb[15]:买三报价 bb[16]:买四申请股数 bb[17]:买四报价 + #bb[18]:买五股数 bb[19]:买五报价 bb[20]:卖一股数 bb[21]:卖一报价 bb[22]:卖二申请股数 bb[23]:卖二报价 + #bb[24]:卖三股数 bb[25]:卖三报价 bb[26]:卖四股数 bb[27]:卖四报价 bb[28]:卖五股数 bb[29]:卖五报价 + #bb[30]:日期 bb[31]:时间 bb[8]:不知道 + + return st.Stock(stockInfo) + + except Exception as e: + print(">>>>>> Exception: " + str(e)) + finally: + None + +# 获取A股所有股票的实时股价 +# 通过 ts.get_today_all 获取 +@wrapcache.wrapcache(timeout=5*60) +@retry(stop_max_attempt_number=10) +def get_real_price_dataframe(): + df = ts.get_today_all() + return df + +@fn_timer_ +def getAllChinaStock(): + + stockList = [] + try: + df = get_real_price_dataframe() + for se in df.get_values(): + stock = st.Stock('') + stock.code = se[0] + stock.name = se[1] + stock.current = se[3] + stock.open = se[4] + stock.high = se[5] + stock.low = se[6] + stock.close = se[7] + stock.dealAmount = se[8]/100 + stock.time = time.localtime(time.time()) #时间 + #print stock + + stockList.append(stock) + except: + print 'get real price timeout' + + return stockList + +# 获取A股所有股票的实时股价 +# 通过get_realtime_quotes接口获取 +def getAllChinaStock2(): + df_list = pd.read_csv(cm.DownloadDir + cm.TABLE_STOCKS_BASIC + '.csv') + stockList = df_list['code'].values; + stockList_group = util.group_list(stockList, 20) + print len(stockList_group) + print stockList_group[1] + stockList = [] + for group in stockList_group: + df = ts.get_realtime_quotes(group) + + for se in df.get_values(): + stock = st.Stock('') + stock.code = se[0] + stock.name = se[1] + stock.current = se[3] + stock.open = se[4] + stock.high = se[5] + stock.low = se[6] + stock.close = se[7] + stock.dealAmount = se[8]/100 + stock.time = time.localtime(time.time()) #时间 + #print stock + stockList.append(stock) + return stockList + +if __name__ == "__main__": + getAllChinaStock() + + +#df = ts.get_realtime_quotes('002600') #print df[['code', 'name', 'price', 'bid', 'ask', 'volume', 'amount', 'time']] \ No newline at end of file diff --git a/data_process/online_data.pyc b/data_process/online_data.pyc new file mode 100644 index 0000000..fa060ae Binary files /dev/null and b/data_process/online_data.pyc differ diff --git a/init.py b/init.py index 8d916a9..58d8f4e 100644 --- a/init.py +++ b/init.py @@ -1,47 +1,114 @@ -#coding:utf-8 -__author__ = 'cbb' - -import platform, os -from sqlalchemy import create_engine -from util.MyLogger import Logger - -#DB_WAY:数据存储方式 'csv' # or 'mysql' or 'redis' or 'sqlite' -DB_WAY = 'mysql' -DB_USER = 'root' -DB_PWD = 'root' # or '123456' in win7 -DB_NAME = 'stock' -DownloadDir = os.path.pardir + '/stockdata/' # os.path.pardir: 上级目录 - -# mysql Host -# if platform.system() == 'Windows': -# host_mysql = 'localhost' -# else: -# host_mysql = '101.200.183.216' -host_mysql = '127.0.0.1' -user_mysql = 'admin' -pwd_mysql = '1234' -db_name_mysql = 'wealth_db' - -engine = create_engine('mysql+mysqldb://%s:%s@%s/%s' % (user_mysql, pwd_mysql, host_mysql, db_name_mysql), connect_args={'charset':'utf8'}) - - -# 短均线， 长均线 -AVR_SHORT = 12 -AVR_LONG = 40 - -#买卖标记 -SIGNAL_BUY = 1 #买 -SIGNAL_SALE = -1 #卖 -SIGNAL_DEFAULT = 0 - -#阈值 -Threshold_Buy_Count = 3 -Threshold_Sale_Count = 2 - -#日志设置 -from util.MyLogger import Logger -#infoLogger = Logger(logname='../Log/info.log', logger='I') -#errorLogger = Logger(logname='../Log/error.log', logger='E') - -#配置文件 位置 -config_file_path = '../config.ini' \ No newline at end of file +#coding:utf-8 +__author__ = 'cbb' + +import platform, os +from sqlalchemy import create_engine +from util.MyLogger import Logger +import pymysql +import datetime +import sys +reload(sys) +sys.setdefaultencoding("utf-8") + +#DB_WAY:数据存储方式 'csv' # or 'mysql' or 'redis' or 'sqlite' +DB_WAY = 'mysql' +DB_USER = 'root' +DB_PWD = 'root' # or '123456' in win7 +DB_NAME = 'stock' +DownloadDir = os.path.pardir + '/stockdata/' # os.path.pardir: 上级目录 + +# mysql Host +# if platform.system() == 'Windows': +# host_mysql = 'localhost' +# else: +# host_mysql = '101.200.183.216' +host_mysql = 'localhost' +user_mysql = 'root' +pwd_mysql = '133499' +db_name_mysql = 'wealth_db' + +#engine = create_engine('mysql+mysqldb://%s:%s@%s/%s' % (user_mysql, pwd_mysql, host_mysql, db_name_mysql), connect_args={'charset':'utf8'}) +class get_mysql(object): + '''链接数据库，并根据提供的数据库名称和关键词信息创建一个表格，表格存在就不创建''' + def __init__(self,dbname,key,citys): + self.T = datetime.datetime.strftime(datetime.datetime.now(), "%Y%m%d%H%M") + self.dbname = dbname + self.key = key + if len(citys) == 1: + self.city = citys[0] + elif len(citys) > 1: + self.city = "&".join(citys) + else: + self.city = "" + self.table_name = "{}_{}_{}".format(self.T,self.key,self.city) + self.conn = pymysql.Connect( + host="localhost", + port=3306, + user='root', + password='133499', + db=self.dbname, + charset='utf8' + ) + self.cursor = self.conn.cursor() + # 直接创建一个表格 + self.create_table() + + # 创建表格的函数，表格名称按照时间和关键词命名 + def create_table(self): + sql = '''CREATE TABLE `{tbname}`( + {job_name} varchar(100) not null, + {gs_name} varchar(100), + {salary} char(20), + {job_site} char(20), + {create_date} char(20), + {job_link} varchar(100), + {gs_link} varchar(100) + )''' + try: + self.cursor.execute(sql.format(tbname=self.table_name,job_name="职位名称",gs_name="公司名称",salary="薪资", + job_site="工作地点",create_date="发布时间",job_link="招聘链接",gs_link="公司链接")) + except Exception as e: + print("创建表格失败，表格可能已经存在！",e) + else: + self.conn.commit() + print("成功创建一个表格，名称是{}".format(self.table_name)) + + # 插入信息函数，每次插入一条信息，插入信息失败会回滚 + def insert_data(self,data): + '''插入数据，不成功就回滚操作''' + sql = '''INSERT INTO `{}` VALUES('{}','{}','{}','{}','{}','{}','{}')''' + try: + self.cursor.execute(sql.format(self.table_name,data["job_name"],data["gs_name"],data["salary"],data["job_site"], + data["create_date"],data["job_link"],data["gs_link"])) + except Exception as e: + self.conn.rollback() + print("插入信息失败，原因：",e) + else: + self.conn.commit() + print("成功插入一条信息") + + def close_mytable(self): + '''关闭游标和断开链接，数据全部插入后必须执行这个操作''' + self.cursor.close() + self.conn.close() + +# 短均线， 长均线 +AVR_SHORT = 12 +AVR_LONG = 40 + +#买卖标记 +SIGNAL_BUY = 1 #买 +SIGNAL_SALE = -1 #卖 +SIGNAL_DEFAULT = 0 + +#阈值 +Threshold_Buy_Count = 3 +Threshold_Sale_Count = 2 + +#日志设置 +from util.MyLogger import Logger +infoLogger = Logger(logname='../Log/info.log', logger='I') +errorLogger = Logger(logname='../Log/error.log', logger='E') + +#配置文件 位置 +config_file_path = '../config.ini' diff --git a/init.pyc b/init.pyc new file mode 100644 index 0000000..5a1f056 Binary files /dev/null and b/init.pyc differ diff --git a/k-line.py b/k-line.py index 2439b1b..875012d 100644 --- a/k-line.py +++ b/k-line.py @@ -1,37 +1,37 @@ -#!/usr/bin/env python -#coding=utf-8 -import matplotlib.pyplot as plt -from matplotlib.dates import DateFormatter, WeekdayLocator,\ - DayLocator, MONDAY -from matplotlib.finance import quotes_historical_yahoo_ohlc, candlestick_ohlc - - -# (Year, month, day) tuples suffice as args for quotes_historical_yahoo -date1 = (2015, 2, 1) -date2 = (2015, 5, 22) - - -mondays = WeekdayLocator(MONDAY) # major ticks on the mondays -alldays = DayLocator() # minor ticks on the days -weekFormatter = DateFormatter('%b %d') # e.g., Jan 12 -dayFormatter = DateFormatter('%d') # e.g., 12 - -quotes = quotes_historical_yahoo_ohlc('600000.SS', date1, date2) -if len(quotes) == 0: - raise SystemExit - -fig, ax = plt.subplots() -fig.subplots_adjust(bottom=0.2) -ax.xaxis.set_major_locator(mondays) -ax.xaxis.set_minor_locator(alldays) -ax.xaxis.set_major_formatter(weekFormatter) -#ax.xaxis.set_minor_formatter(dayFormatter) - -#plot_day_summary(ax, quotes, ticksize=3) -candlestick_ohlc(ax, quotes, width=0.6) - -ax.xaxis_date() -ax.autoscale_view() -plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right') - +#!/usr/bin/env python +#coding=utf-8 +import matplotlib.pyplot as plt +from matplotlib.dates import DateFormatter, WeekdayLocator,\ + DayLocator, MONDAY +from matplotlib.finance import quotes_historical_yahoo_ohlc, candlestick_ohlc + + +# (Year, month, day) tuples suffice as args for quotes_historical_yahoo +date1 = (2015, 2, 1) +date2 = (2015, 5, 22) + + +mondays = WeekdayLocator(MONDAY) # major ticks on the mondays +alldays = DayLocator() # minor ticks on the days +weekFormatter = DateFormatter('%b %d') # e.g., Jan 12 +dayFormatter = DateFormatter('%d') # e.g., 12 + +quotes = quotes_historical_yahoo_ohlc('600000.SS', date1, date2) +if len(quotes) == 0: + raise SystemExit + +fig, ax = plt.subplots() +fig.subplots_adjust(bottom=0.2) +ax.xaxis.set_major_locator(mondays) +ax.xaxis.set_minor_locator(alldays) +ax.xaxis.set_major_formatter(weekFormatter) +#ax.xaxis.set_minor_formatter(dayFormatter) + +#plot_day_summary(ax, quotes, ticksize=3) +candlestick_ohlc(ax, quotes, width=0.6) + +ax.xaxis_date() +ax.autoscale_view() +plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right') + plt.show() \ No newline at end of file diff --git a/livedata.py b/livedata.py index b1e8b77..b766202 100644 --- a/livedata.py +++ b/livedata.py @@ -1,208 +1,208 @@ -#coding=utf-8 -import datetime -import numpy as np -import matplotlib.colors as colors -import matplotlib.finance as finance -import matplotlib.dates as mdates -import matplotlib.ticker as mticker -import matplotlib.mlab as mlab -import matplotlib.pyplot as plt -import matplotlib.font_manager as font_manager - - -startdate = datetime.date(2014,1,1) -today = enddate = datetime.date.today() -ticker = '000048.SZ' - - -fh = finance.fetch_historical_yahoo(ticker, startdate, enddate) -# a numpy record array with fields: date, open, high, low, close, volume, adj_close) - -r = mlab.csv2rec(fh); fh.close() -r.sort() - - -def moving_average(x, n, type='simple'): - """ - compute an n period moving average. - - type is 'simple' | 'exponential' - - """ - x = np.asarray(x) - if type=='simple': - weights = np.ones(n) - else: - weights = np.exp(np.linspace(-1., 0., n)) - - weights /= weights.sum() - - - a = np.convolve(x, weights, mode='full')[:len(x)] - a[:n] = a[n] - return a - -def relative_strength(prices, n=14): - """ - compute the n period relative strength indicator - http://stockcharts.com/school/doku.php?id=chart_school:glossary_r#relativestrengthindex - http://www.investopedia.com/terms/r/rsi.asp - """ - - deltas = np.diff(prices) - seed = deltas[:n+1] - up = seed[seed>=0].sum()/n - down = -seed[seed<0].sum()/n - rs = up/down - rsi = np.zeros_like(prices) - rsi[:n] = 100. - 100./(1.+rs) - - for i in range(n, len(prices)): - delta = deltas[i-1] # cause the diff is 1 shorter - - if delta>0: - upval = delta - downval = 0. - else: - upval = 0. - downval = -delta - - up = (up*(n-1) + upval)/n - down = (down*(n-1) + downval)/n - - rs = up/down - rsi[i] = 100. - 100./(1.+rs) - - return rsi - -def moving_average_convergence(x, nslow=26, nfast=12): - """ - compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg' - return value is emaslow, emafast, macd which are len(x) arrays - """ - emaslow = moving_average(x, nslow, type='exponential') - emafast = moving_average(x, nfast, type='exponential') - return emaslow, emafast, emafast - emaslow - - -plt.rc('axes', grid=True) -plt.rc('grid', color='0.75', linestyle='-', linewidth=0.5) - -textsize = 9 -left, width = 0.1, 0.8 -rect1 = [left, 0.7, width, 0.2] -rect2 = [left, 0.3, width, 0.4] -rect3 = [left, 0.1, width, 0.2] - - -fig = plt.figure(facecolor='white') -axescolor = '#f6f6f6' # the axes background color - -ax1 = fig.add_axes(rect1, axisbg=axescolor) #left, bottom, width, height -ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1) -ax2t = ax2.twinx() -ax3 = fig.add_axes(rect3, axisbg=axescolor, sharex=ax1) - - - -### plot the relative strength indicator -prices = r.adj_close -rsi = relative_strength(prices) -fillcolor = 'darkgoldenrod' - -ax1.plot(r.date, rsi, color=fillcolor) -ax1.axhline(70, color=fillcolor) -ax1.axhline(30, color=fillcolor) -ax1.fill_between(r.date, rsi, 70, where=(rsi>=70), facecolor=fillcolor, edgecolor=fillcolor) -ax1.fill_between(r.date, rsi, 30, where=(rsi<=30), facecolor=fillcolor, edgecolor=fillcolor) -ax1.text(0.6, 0.9, '>70 = overbought', va='top', transform=ax1.transAxes, fontsize=textsize) -ax1.text(0.6, 0.1, '<30 = oversold', transform=ax1.transAxes, fontsize=textsize) -ax1.set_ylim(0, 100) -ax1.set_yticks([30,70]) -ax1.text(0.025, 0.95, 'RSI (14)', va='top', transform=ax1.transAxes, fontsize=textsize) -ax1.set_title('%s daily'%ticker) - -### plot the price and volume data -dx = r.adj_close - r.close -low = r.low + dx -high = r.high + dx - -deltas = np.zeros_like(prices) -deltas[1:] = np.diff(prices) -up = deltas>0 -ax2.vlines(r.date[up], low[up], high[up], color='black', label='_nolegend_') -ax2.vlines(r.date[~up], low[~up], high[~up], color='black', label='_nolegend_') -ma20 = moving_average(prices, 20, type='simple') -ma200 = moving_average(prices, 200, type='simple') - -linema20, = ax2.plot(r.date, ma20, color='blue', lw=2, label='MA (20)') -linema200, = ax2.plot(r.date, ma200, color='red', lw=2, label='MA (200)') - - -last = r[-1] -s = '%s O:%1.2f H:%1.2f L:%1.2f C:%1.2f, V:%1.1fM Chg:%+1.2f' % ( - today.strftime('%d-%b-%Y'), - last.open, last.high, - last.low, last.close, - last.volume*1e-6, - last.close-last.open ) -t4 = ax2.text(0.3, 0.9, s, transform=ax2.transAxes, fontsize=textsize) - -props = font_manager.FontProperties(size=10) -leg = ax2.legend(loc='center left', shadow=True, fancybox=True, prop=props) -leg.get_frame().set_alpha(0.5) - - -volume = (r.close*r.volume)/1e6 # dollar volume in millions -vmax = volume.max() -poly = ax2t.fill_between(r.date, volume, 0, label='Volume', facecolor=fillcolor, edgecolor=fillcolor) -ax2t.set_ylim(0, 5*vmax) -ax2t.set_yticks([]) - - -### compute the MACD indicator -fillcolor = 'darkslategrey' -nslow = 26 -nfast = 12 -nema = 9 -emaslow, emafast, macd = moving_average_convergence(prices, nslow=nslow, nfast=nfast) -ema9 = moving_average(macd, nema, type='exponential') -ax3.plot(r.date, macd, color='black', lw=2) -ax3.plot(r.date, ema9, color='blue', lw=1) -ax3.fill_between(r.date, macd-ema9, 0, alpha=0.5, facecolor=fillcolor, edgecolor=fillcolor) - - -ax3.text(0.025, 0.95, 'MACD (%d, %d, %d)'%(nfast, nslow, nema), va='top', - transform=ax3.transAxes, fontsize=textsize) - -#ax3.set_yticks([]) -# turn off upper axis tick labels, rotate the lower ones, etc -for ax in ax1, ax2, ax2t, ax3: - if ax!=ax3: - for label in ax.get_xticklabels(): - label.set_visible(False) - else: - for label in ax.get_xticklabels(): - label.set_rotation(30) - label.set_horizontalalignment('right') - - ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d') - - - -class MyLocator(mticker.MaxNLocator): - def __init__(self, *args, **kwargs): - mticker.MaxNLocator.__init__(self, *args, **kwargs) - - def __call__(self, *args, **kwargs): - return mticker.MaxNLocator.__call__(self, *args, **kwargs) - -# at most 5 ticks, pruning the upper and lower so they don't overlap -# with other ticks -#ax2.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both')) -#ax3.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both')) - -ax2.yaxis.set_major_locator(MyLocator(5, prune='both')) -ax3.yaxis.set_major_locator(MyLocator(5, prune='both')) - +#coding=utf-8 +import datetime +import numpy as np +import matplotlib.colors as colors +import matplotlib.finance as finance +import matplotlib.dates as mdates +import matplotlib.ticker as mticker +import matplotlib.mlab as mlab +import matplotlib.pyplot as plt +import matplotlib.font_manager as font_manager + + +startdate = datetime.date(2014,1,1) +today = enddate = datetime.date.today() +ticker = '000048.SZ' + + +fh = finance.fetch_historical_yahoo(ticker, startdate, enddate) +# a numpy record array with fields: date, open, high, low, close, volume, adj_close) + +r = mlab.csv2rec(fh); fh.close() +r.sort() + + +def moving_average(x, n, type='simple'): + """ + compute an n period moving average. + + type is 'simple' | 'exponential' + + """ + x = np.asarray(x) + if type=='simple': + weights = np.ones(n) + else: + weights = np.exp(np.linspace(-1., 0., n)) + + weights /= weights.sum() + + + a = np.convolve(x, weights, mode='full')[:len(x)] + a[:n] = a[n] + return a + +def relative_strength(prices, n=14): + """ + compute the n period relative strength indicator + http://stockcharts.com/school/doku.php?id=chart_school:glossary_r#relativestrengthindex + http://www.investopedia.com/terms/r/rsi.asp + """ + + deltas = np.diff(prices) + seed = deltas[:n+1] + up = seed[seed>=0].sum()/n + down = -seed[seed<0].sum()/n + rs = up/down + rsi = np.zeros_like(prices) + rsi[:n] = 100. - 100./(1.+rs) + + for i in range(n, len(prices)): + delta = deltas[i-1] # cause the diff is 1 shorter + + if delta>0: + upval = delta + downval = 0. + else: + upval = 0. + downval = -delta + + up = (up*(n-1) + upval)/n + down = (down*(n-1) + downval)/n + + rs = up/down + rsi[i] = 100. - 100./(1.+rs) + + return rsi + +def moving_average_convergence(x, nslow=26, nfast=12): + """ + compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg' + return value is emaslow, emafast, macd which are len(x) arrays + """ + emaslow = moving_average(x, nslow, type='exponential') + emafast = moving_average(x, nfast, type='exponential') + return emaslow, emafast, emafast - emaslow + + +plt.rc('axes', grid=True) +plt.rc('grid', color='0.75', linestyle='-', linewidth=0.5) + +textsize = 9 +left, width = 0.1, 0.8 +rect1 = [left, 0.7, width, 0.2] +rect2 = [left, 0.3, width, 0.4] +rect3 = [left, 0.1, width, 0.2] + + +fig = plt.figure(facecolor='white') +axescolor = '#f6f6f6' # the axes background color + +ax1 = fig.add_axes(rect1, axisbg=axescolor) #left, bottom, width, height +ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1) +ax2t = ax2.twinx() +ax3 = fig.add_axes(rect3, axisbg=axescolor, sharex=ax1) + + + +### plot the relative strength indicator +prices = r.adj_close +rsi = relative_strength(prices) +fillcolor = 'darkgoldenrod' + +ax1.plot(r.date, rsi, color=fillcolor) +ax1.axhline(70, color=fillcolor) +ax1.axhline(30, color=fillcolor) +ax1.fill_between(r.date, rsi, 70, where=(rsi>=70), facecolor=fillcolor, edgecolor=fillcolor) +ax1.fill_between(r.date, rsi, 30, where=(rsi<=30), facecolor=fillcolor, edgecolor=fillcolor) +ax1.text(0.6, 0.9, '>70 = overbought', va='top', transform=ax1.transAxes, fontsize=textsize) +ax1.text(0.6, 0.1, '<30 = oversold', transform=ax1.transAxes, fontsize=textsize) +ax1.set_ylim(0, 100) +ax1.set_yticks([30,70]) +ax1.text(0.025, 0.95, 'RSI (14)', va='top', transform=ax1.transAxes, fontsize=textsize) +ax1.set_title('%s daily'%ticker) + +### plot the price and volume data +dx = r.adj_close - r.close +low = r.low + dx +high = r.high + dx + +deltas = np.zeros_like(prices) +deltas[1:] = np.diff(prices) +up = deltas>0 +ax2.vlines(r.date[up], low[up], high[up], color='black', label='_nolegend_') +ax2.vlines(r.date[~up], low[~up], high[~up], color='black', label='_nolegend_') +ma20 = moving_average(prices, 20, type='simple') +ma200 = moving_average(prices, 200, type='simple') + +linema20, = ax2.plot(r.date, ma20, color='blue', lw=2, label='MA (20)') +linema200, = ax2.plot(r.date, ma200, color='red', lw=2, label='MA (200)') + + +last = r[-1] +s = '%s O:%1.2f H:%1.2f L:%1.2f C:%1.2f, V:%1.1fM Chg:%+1.2f' % ( + today.strftime('%d-%b-%Y'), + last.open, last.high, + last.low, last.close, + last.volume*1e-6, + last.close-last.open ) +t4 = ax2.text(0.3, 0.9, s, transform=ax2.transAxes, fontsize=textsize) + +props = font_manager.FontProperties(size=10) +leg = ax2.legend(loc='center left', shadow=True, fancybox=True, prop=props) +leg.get_frame().set_alpha(0.5) + + +volume = (r.close*r.volume)/1e6 # dollar volume in millions +vmax = volume.max() +poly = ax2t.fill_between(r.date, volume, 0, label='Volume', facecolor=fillcolor, edgecolor=fillcolor) +ax2t.set_ylim(0, 5*vmax) +ax2t.set_yticks([]) + + +### compute the MACD indicator +fillcolor = 'darkslategrey' +nslow = 26 +nfast = 12 +nema = 9 +emaslow, emafast, macd = moving_average_convergence(prices, nslow=nslow, nfast=nfast) +ema9 = moving_average(macd, nema, type='exponential') +ax3.plot(r.date, macd, color='black', lw=2) +ax3.plot(r.date, ema9, color='blue', lw=1) +ax3.fill_between(r.date, macd-ema9, 0, alpha=0.5, facecolor=fillcolor, edgecolor=fillcolor) + + +ax3.text(0.025, 0.95, 'MACD (%d, %d, %d)'%(nfast, nslow, nema), va='top', + transform=ax3.transAxes, fontsize=textsize) + +#ax3.set_yticks([]) +# turn off upper axis tick labels, rotate the lower ones, etc +for ax in ax1, ax2, ax2t, ax3: + if ax!=ax3: + for label in ax.get_xticklabels(): + label.set_visible(False) + else: + for label in ax.get_xticklabels(): + label.set_rotation(30) + label.set_horizontalalignment('right') + + ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d') + + + +class MyLocator(mticker.MaxNLocator): + def __init__(self, *args, **kwargs): + mticker.MaxNLocator.__init__(self, *args, **kwargs) + + def __call__(self, *args, **kwargs): + return mticker.MaxNLocator.__call__(self, *args, **kwargs) + +# at most 5 ticks, pruning the upper and lower so they don't overlap +# with other ticks +#ax2.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both')) +#ax3.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both')) + +ax2.yaxis.set_major_locator(MyLocator(5, prune='both')) +ax3.yaxis.set_major_locator(MyLocator(5, prune='both')) + plt.show() \ No newline at end of file diff --git a/new_trade/main.py b/new_trade/main.py index 216bb14..df74b82 100644 --- a/new_trade/main.py +++ b/new_trade/main.py @@ -1,52 +1,52 @@ -#coding: utf-8 - -import tushare as ts -import pandas as pd - -from util.codeConvert import GetNowDate - -def diagnosis_one_stock(code): - """ - 个股诊断 - :return: - """ - - # 获取股价 - df = get_stock_price(code, True) - - # 均线指标 - - - # K线提示 - -def get_stock_price(code, include_realtime_price): - """ - 获取个股股价 - :param code: 股票代码 - :param include_realtime_price: 是否含实时股价 - :return: - """ - - # 获取历史股价 - df = ts.get_hist_data(code) - df = df[['close']] - df['date'] = df.index - - if include_realtime_price: - df_today = ts.get_today_all() - df_code = df_today[df_today['code']==code] - df_code = df_code[['trade']] - df_code['date'] = GetNowDate() - df_code.rename(columns={'trade': 'close'}, inplace=True) - df = pd.concat([df, df_code], ignore_index=True) - - df.sort(columns='date', inplace=True) - df = df.drop_duplicates(['date']) - df.index = range(len(df)) - print '\n' - # print df.head() - print df.tail() - return df - -if __name__ == "__main__": - get_stock_price('600000', True) +#coding: utf-8 + +import tushare as ts +import pandas as pd + +from util.codeConvert import GetNowDate + +def diagnosis_one_stock(code): + """ + 个股诊断 + :return: + """ + + # 获取股价 + df = get_stock_price(code, True) + + # 均线指标 + + + # K线提示 + +def get_stock_price(code, include_realtime_price): + """ + 获取个股股价 + :param code: 股票代码 + :param include_realtime_price: 是否含实时股价 + :return: + """ + + # 获取历史股价 + df = ts.get_hist_data(code) + df = df[['close']] + df['date'] = df.index + + if include_realtime_price: + df_today = ts.get_today_all() + df_code = df_today[df_today['code']==code] + df_code = df_code[['trade']] + df_code['date'] = GetNowDate() + df_code.rename(columns={'trade': 'close'}, inplace=True) + df = pd.concat([df, df_code], ignore_index=True) + + # df.sort(columns='date', inplace=True) + df = df.drop_duplicates(['date']) + df.index = range(len(df)) + print '\n' + # print df.head() + print df.tail() + return df + +if __name__ == "__main__": + get_stock_price('600000', True) diff --git a/new_trade/strategy_ma.py b/new_trade/strategy_ma.py index 4697b78..2886bb9 100644 --- a/new_trade/strategy_ma.py +++ b/new_trade/strategy_ma.py @@ -1,37 +1,36 @@ -#coding: utf-8 - -""" -均线策略 -""" - -import pandas as pd - -# MA指标择时 -def select_time_ma(df_closeprice, ma_short=12, ma_long=40): - """ - MA指标择时(简单均线SMA) - :param df_closeprice: DataFrame, 收盘价 - :param ma_short: - :param ma_long: - :return: - """ - - #SMA - df_closeprice['ma_close_short'] = pd.rolling_mean(df_closeprice['close_price'], ma_short) - df_closeprice['ma_close_long'] = pd.rolling_mean(df_closeprice['close_price'], ma_long) - - - df_closeprice['signal'] = 0 - - for ix, row in df_closeprice.iterrows(): - pass - - - # if ema_close_short[-1] > ema_close_short[-2] and ema_close_short[-1] > ema_close_long[-1] \ - # and ema_close_short[-2] < ema_close_long[-2]: - # signal = SIGNAL_BUY - # elif ema_close_long[-1] < ema_close_long[-2] and ema_close_short[-1] < ema_close_long[-1] \ - # and ema_close_short[-2] > ema_close_long[-2]: - # signal = SIGNAL_SALE - +#coding: utf-8 + +""" +均线策略 +""" + +import pandas as pd + +# MA指标择时 +def select_time_ma(df_closeprice, ma_short=12, ma_long=40): + """ + MA指标择时(简单均线SMA) + :param df_closeprice: DataFrame, 收盘价 + :param ma_short: + :param ma_long: + :return: + """ + + #SMA + df_closeprice['ma_close_short'] = pd.rolling_mean(df_closeprice['close_price'], ma_short) + df_closeprice['ma_close_long'] = pd.rolling_mean(df_closeprice['close_price'], ma_long) + + + df_closeprice['signal'] = 0 + + for ix, row in df_closeprice.iterrows(): + pass + + # if ema_close_short[-1] > ema_close_short[-2] and ema_close_short[-1] > ema_close_long[-1] \ + # and ema_close_short[-2] < ema_close_long[-2]: + # signal = SIGNAL_BUY + # elif ema_close_long[-1] < ema_close_long[-2] and ema_close_short[-1] < ema_close_long[-1] \ + # and ema_close_short[-2] > ema_close_long[-2]: + # signal = SIGNAL_SALE + # # return signal \ No newline at end of file diff --git a/plot.py b/plot.py index 8e14f69..e78b8b3 100644 --- a/plot.py +++ b/plot.py @@ -1,207 +1,207 @@ -#!/usr/local/bin/python -#coding=utf-8 -import os -import matplotlib.pyplot as plt -import matplotlib.mlab as mlab -import numpy as np -import math -import csv -import urllib -import urllib2 -import pywt -from numpy import log -import cwavelet -import copy -import cleastsq -import BP - -date_begin = [1, 1, 2014] -date_end = [5, 27, 2015] - - -def plotClosePrice(plt, stock_name, stock_code, dataPath): - fh = open(dataPath + '\\' + stock_code +".csv", 'r') - r = mlab.csv2rec(fh); fh.close() - r.sort() - - prices = r.adj_close - - - plt.plot(r.date, prices) - - - -#收盘价走势及小波处理 -def plotData(stock_code, stock_name): - try: - xValues=[] - yValues=[] - - xLabels=[] - - dataPath = os.getcwd() + '\\stockdata\\'; - - i=-1 - for line in open(dataPath + stock_code +".csv"): - f_date, f_open, f_high, f_low, f_close, f_volume, f_adjclose = line.split(",") - i += 1 - if i == 0 or i > 1000: - continue - xValues.append(i) - yValues.append(float(f_adjclose)) - xLabels.append(f_date) - yValues.reverse() - - - zValues = cwavelet.getWaveletData(yValues, 'db2', 4, 'sqtwolog') - zxValue = np.arange(0,len(zValues),1) - print len(zxValue), len(zValues) - - plt.figure(figsize=(16,8)) - plt.plot(xValues, yValues, label=stock_code, color="b", linewidth=1) - plt.plot(zxValue, zValues, color="r", linewidth=2) - plt.xlabel("Time") - plt.ylabel("Price") - plt.title( stock_name) - #plt.xticks(range(min(xLabels), max(xLabels)+1, 10)) - plt.grid() - #plt.legend() - plt.show() - - except Exception as e: - print ("Exception:>>>" + str(e)) - finally: - None - -def plotRateOfReturn(stock_code): - try: - xValues = [] - yValues = [] - - i=-1 - for line in open(stock_code + ".csv"): - i += 1 - if i == 0 or i > 1000: - continue - f_date, f_open, f_high, f_low, f_close, f_volume, f_adjclose = line.split(",") - yValues.append(float(f_adjclose)) - - #yValues删除最后一个元素，zValues删除第一个元素 - zValues = copy.deepcopy(yValues) #深拷贝 - yValues.reverse() - yValues.pop() - zValues.pop() - zValues.reverse() - if len(yValues) != len(zValues): - return - - rateValues = [] - for i in range(0, len(yValues)): - print float(zValues[i])/yValues[i] - rateValues.append(math.log(float(zValues[i])/yValues[i])) - xValues.append(i) - - rateValues = cwavelet.getWaveletData(yValues, 'db4', 2, 'sqtwolog') - - # BP神经网络 -# patStock = [] -# for i in range(0, len(yValues)): -# each = [[i], [yValues[i]]] -# patStock.append(each) -# patStockPre = copy.deepcopy(patStock) -# for i in range(len(yValues), len(yValues)+10): -# each = [[i], [0]] -# patStockPre.append(each) -# pat = [ -# [[0], [0]], -# [[2], [1]], -# [[3], [1]], -# [[4], [5]] -# ] -# -# # create a network with two input, two hidden, and one output nodes -# n = BP.NN(1, 2, 1) -# # train it with some patterns -# n.train(patStock) -# # test it -# n.test(patStock) - - #最小二乘法 - print "原始长度:", len(rateValues) - catRateValues = rateValues[:-7] - print "原始长度:", len(catRateValues) - leastsqValues = cwavelet.getWavePacketData(catRateValues, 'haar', 4, 3) - #leastsqValues = cleastsq.getFitYValues(range(len(catRateValues)), catRateValues, range(len(catRateValues)+3)) - print "变换后长度:", len(leastsqValues) - newLeastsqValues = np.concatenate((rateValues[:-3], leastsqValues[-3:])) - - newLeastsqValues2 = [] - for data in newLeastsqValues: - data -= 0.2 - newLeastsqValues2.append(data) - newLeastsqValues = newLeastsqValues2 - - print "变换后长度:", len(newLeastsqValues) - plt.figure(figsize=(16,8)) - plt.legend() - plt.plot(range(len(rateValues)), rateValues,'b-', linewidth=1) - plt.plot(range(len(newLeastsqValues)), newLeastsqValues, 'r-', linewidth=1) - plt.xlabel('Time') - plt.ylabel('Price') - plt.title(stock_code) - plt.grid() - - plt.show() - - - except Exception as e: - print ("Exception:>>>"+str(e)) - finally: - None - -def down_file(url, file_name): - - u = urllib2.urlopen(url) - f = open(file_name, 'wb') - - file_size_dl = 0 - block_sz = 8192 - while True: - buffer = u.read(block_sz) - if not buffer: - break - - file_size_dl += len(buffer) - f.write(buffer) - f.close() - -ChinaStockNumIndividualList = [ - {'code':"600011", 'num':1100, 'name':u"华能国际"}, # - {'code':"600000", 'num':1900, 'name':u"浦发银行"}, # - {'code':"002600", 'num':1000, 'name':u"江粉磁材"}, # - {'code':"002505", 'num':1000, 'name':u"大康牧业"}, # - {'code':"000725", 'num':2000, 'name':u"京东方A"}, # - {'code':"000783", 'num':600, 'name':u"长江证券"}, # - {'code':"600048", 'num':2000, 'name':u"保利地产"}, # - {'code':"300315", 'num':200, 'name':u"掌趣科技"}, # - {'code':"002167", 'num':600, 'name':u"东方锆业"}, # - {'code':"601001", 'num':1000, 'name':u"大同煤业"}, # - #{'code':"150172", 'num':5000, 'name':"证券B"}, # -] - -if __name__ == '__main__': - - for stockCodeDict in ChinaStockNumIndividualList: - print stockCodeDict['name'] - #plotRateOfReturn(stockCodeDict['code']) - plotData(stockCodeDict['code'], stockCodeDict['name']) - break - #上证指数 - #downloadData("000001") - #plotRateOfReturn("000001") - - - - - - +#!/usr/local/bin/python +#coding=utf-8 +import os +import matplotlib.pyplot as plt +import matplotlib.mlab as mlab +import numpy as np +import math +import csv +import urllib +import urllib2 +import pywt +from numpy import log +import cwavelet +import copy +import cleastsq +import BP + +date_begin = [1, 1, 2014] +date_end = [5, 27, 2015] + + +def plotClosePrice(plt, stock_name, stock_code, dataPath): + fh = open(dataPath + '\\' + stock_code +".csv", 'r') + r = mlab.csv2rec(fh); fh.close() + r.sort() + + prices = r.adj_close + + + plt.plot(r.date, prices) + + + +#收盘价走势及小波处理 +def plotData(stock_code, stock_name): + try: + xValues=[] + yValues=[] + + xLabels=[] + + dataPath = os.getcwd() + '\\stockdata\\'; + + i=-1 + for line in open(dataPath + stock_code +".csv"): + f_date, f_open, f_high, f_low, f_close, f_volume, f_adjclose = line.split(",") + i += 1 + if i == 0 or i > 1000: + continue + xValues.append(i) + yValues.append(float(f_adjclose)) + xLabels.append(f_date) + yValues.reverse() + + + zValues = cwavelet.getWaveletData(yValues, 'db2', 4, 'sqtwolog') + zxValue = np.arange(0,len(zValues),1) + print len(zxValue), len(zValues) + + plt.figure(figsize=(16,8)) + plt.plot(xValues, yValues, label=stock_code, color="b", linewidth=1) + plt.plot(zxValue, zValues, color="r", linewidth=2) + plt.xlabel("Time") + plt.ylabel("Price") + plt.title( stock_name) + #plt.xticks(range(min(xLabels), max(xLabels)+1, 10)) + plt.grid() + #plt.legend() + plt.show() + + except Exception as e: + print ("Exception:>>>" + str(e)) + finally: + None + +def plotRateOfReturn(stock_code): + try: + xValues = [] + yValues = [] + + i=-1 + for line in open(stock_code + ".csv"): + i += 1 + if i == 0 or i > 1000: + continue + f_date, f_open, f_high, f_low, f_close, f_volume, f_adjclose = line.split(",") + yValues.append(float(f_adjclose)) + + #yValues删除最后一个元素，zValues删除第一个元素 + zValues = copy.deepcopy(yValues) #深拷贝 + yValues.reverse() + yValues.pop() + zValues.pop() + zValues.reverse() + if len(yValues) != len(zValues): + return + + rateValues = [] + for i in range(0, len(yValues)): + print float(zValues[i])/yValues[i] + rateValues.append(math.log(float(zValues[i])/yValues[i])) + xValues.append(i) + + rateValues = cwavelet.getWaveletData(yValues, 'db4', 2, 'sqtwolog') + + # BP神经网络 +# patStock = [] +# for i in range(0, len(yValues)): +# each = [[i], [yValues[i]]] +# patStock.append(each) +# patStockPre = copy.deepcopy(patStock) +# for i in range(len(yValues), len(yValues)+10): +# each = [[i], [0]] +# patStockPre.append(each) +# pat = [ +# [[0], [0]], +# [[2], [1]], +# [[3], [1]], +# [[4], [5]] +# ] +# +# # create a network with two input, two hidden, and one output nodes +# n = BP.NN(1, 2, 1) +# # train it with some patterns +# n.train(patStock) +# # test it +# n.test(patStock) + + #最小二乘法 + print "原始长度:", len(rateValues) + catRateValues = rateValues[:-7] + print "原始长度:", len(catRateValues) + leastsqValues = cwavelet.getWavePacketData(catRateValues, 'haar', 4, 3) + #leastsqValues = cleastsq.getFitYValues(range(len(catRateValues)), catRateValues, range(len(catRateValues)+3)) + print "变换后长度:", len(leastsqValues) + newLeastsqValues = np.concatenate((rateValues[:-3], leastsqValues[-3:])) + + newLeastsqValues2 = [] + for data in newLeastsqValues: + data -= 0.2 + newLeastsqValues2.append(data) + newLeastsqValues = newLeastsqValues2 + + print "变换后长度:", len(newLeastsqValues) + plt.figure(figsize=(16,8)) + plt.legend() + plt.plot(range(len(rateValues)), rateValues,'b-', linewidth=1) + plt.plot(range(len(newLeastsqValues)), newLeastsqValues, 'r-', linewidth=1) + plt.xlabel('Time') + plt.ylabel('Price') + plt.title(stock_code) + plt.grid() + + plt.show() + + + except Exception as e: + print ("Exception:>>>"+str(e)) + finally: + None + +def down_file(url, file_name): + + u = urllib2.urlopen(url) + f = open(file_name, 'wb') + + file_size_dl = 0 + block_sz = 8192 + while True: + buffer = u.read(block_sz) + if not buffer: + break + + file_size_dl += len(buffer) + f.write(buffer) + f.close() + +ChinaStockNumIndividualList = [ + {'code':"600011", 'num':1100, 'name':u"华能国际"}, # + {'code':"600000", 'num':1900, 'name':u"浦发银行"}, # + {'code':"002600", 'num':1000, 'name':u"江粉磁材"}, # + {'code':"002505", 'num':1000, 'name':u"大康牧业"}, # + {'code':"000725", 'num':2000, 'name':u"京东方A"}, # + {'code':"000783", 'num':600, 'name':u"长江证券"}, # + {'code':"600048", 'num':2000, 'name':u"保利地产"}, # + {'code':"300315", 'num':200, 'name':u"掌趣科技"}, # + {'code':"002167", 'num':600, 'name':u"东方锆业"}, # + {'code':"601001", 'num':1000, 'name':u"大同煤业"}, # + #{'code':"150172", 'num':5000, 'name':"证券B"}, # +] + +if __name__ == '__main__': + + for stockCodeDict in ChinaStockNumIndividualList: + print stockCodeDict['name'] + #plotRateOfReturn(stockCodeDict['code']) + plotData(stockCodeDict['code'], stockCodeDict['name']) + break + #上证指数 + #downloadData("000001") + #plotRateOfReturn("000001") + + + + + + diff --git a/plot.pyc b/plot.pyc new file mode 100644 index 0000000..a48fe3e Binary files /dev/null and b/plot.pyc differ diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..d3c468d --- /dev/null +++ b/requirements.txt @@ -0,0 +1,17 @@ +requests==2.14.2 +SQLAlchemy==1.1.11 +pandas==0.19.2 +retrying==1.3.3 +tqdm==4.19.5 +matplotlib==2.1.1 +APScheduler==3.3.1 +tushare==1.0.7 +chardet==3.0.4 +PyMySQL==0.7.11 +PyWavelets==0.5.2 +wrapcache==1.0.8 +numpy==1.13.3 +BeautifulSoup==3.2.1 +beautifulsoup4==4.6.0 +scrapy==1.5.0 +Pyevolve==0.6rc1 diff --git a/shedule/trade_daily.bat b/shedule/trade_daily.bat index 0a29564..5ba2328 100644 --- a/shedule/trade_daily.bat +++ b/shedule/trade_daily.bat @@ -1,2 +1,2 @@ -cd E:\Code\stock\trade_process +cd E:\Code\stock\trade_process python live_trade.py \ No newline at end of file diff --git a/stock.py b/stock.py index d66ac09..2bf81d5 100644 --- a/stock.py +++ b/stock.py @@ -1,237 +1,234 @@ -#!/usr/local/bin/python3 -#coding=utf-8 -#source http://www.cnblogs.com/txw1958/ - -import os, io, sys, re, time, json, base64 -import webbrowser, decimal -import urllib2 -from time import sleep -from plot import plotData - - -period_All_List = [ - "min", #分时线 - "daily", #日K线 - "weekly", #周K线 - "monthly" #月K线 - ] -period_min = period_All_List[0] -period_daily = period_All_List[1] - -ChinaStockIndexList = [ - "000001", # sh000001 上证指数 - "399001", # sz399001 深证成指 -# "000300", # sh000300 沪深300 -# "399005", # sz399005 中小板指 -# "399006", # sz399006 创业板指 -# "000003", # sh000003 B股指数 -] -ChinaStockIndividualList = [ - "600011", # - "600000", # - "002600", # - "002505", # - "000725", # - "000783", # - "600048", # - "300315", # - "002167", # - "601001", # - "150172", # -] - -ChinaStockNumIndividualList = [ - {'code':"600011", 'num':1100}, # 华能国际 - {'code':"600000", 'num':1900}, # 浦发银行 - {'code':"002600", 'num':1000}, # 江粉磁材 - {'code':"002505", 'num':1000}, # 大康牧业 - {'code':"000725", 'num':2000}, # 京东方A - {'code':"000783", 'num':600}, # 长江证券 - {'code':"600048", 'num':2000}, # 保利地产 - {'code':"300315", 'num':200}, # 掌趣科技 - {'code':"002167", 'num':600}, # 东方锆业 - {'code':"601001", 'num':1000}, # 大同煤业 - {'code':"150172", 'num':5000}, # 证券B -] - -WorldStockIndexList = [ - {'code':"000001", 'yahoo':"000001.SS",'name':{'chinese':"中国上证指数", 'english':"CHINA SHANGHAI COMPOSITE INDEX"}}, - {'code':"399001", 'yahoo':"399001.SZ",'name':{'chinese':"中国深证成指", 'english':"SZSE COMPONENT INDEX"}}, - {'code':"DJI", 'yahoo':"^DJI",'name':{'chinese':"美国道琼斯工业平均指数", 'english':"Dow Jones Industrial Average"}}, - {'code':"IXIC", 'yahoo':"^IXIC",'name':{'chinese':"美国纳斯达克综合指数", 'english':"NASDAQ Composite"},}, - {'code':"GSPC", 'yahoo':"^GSPC",'name':{'chinese':"美国标准普尔500指数", 'english':"S&P 500"}}, - {'code':"N225", 'yahoo':"^N225",'name':{'chinese':"日本日经225指数", 'english':"NIKKEI 225"}}, - {'code':"TWII", 'yahoo':"^TWII",'name':{'chinese':"台湾台北加权指数", 'english':"TSEC weighted index"}}, - {'code':"HSI", 'yahoo':"^HSI",'name':{'chinese':"香港恒生指数", 'english':"HANG SENG INDEX"}}, - {'code':"FCHI", 'yahoo':"^FCHI",'name':{'chinese':"法国CAC40指数", 'english':"CAC 40"}}, - {'code':"FTSE", 'yahoo':"^FTSE",'name':{'chinese':"英国富时100指数", 'english':"FTSE 100"}}, - {'code':"GDAXI", 'yahoo':"^GDAXI",'name':{'chinese':"德国法兰克福DAX指数", 'english':"DAX"} - } -] -WorldStockIndexList_SP500 = WorldStockIndexList[7] - -#国内股票数据：指数 -def getChinaStockIndexInfo(stockCode, period): - try: - exchange = "sz" if (int(stockCode) // 100000 == 3) else "sh" - #http://hq.sinajs.cn/list=s_sh000001 - dataUrl = "http://hq.sinajs.cn/list=s_" + exchange + stockCode - stdout = urllib2.urlopen(dataUrl) - stdoutInfo = stdout.read().decode('gb2312').encode('utf-8') - tempData = re.search('''(")(.+)(")''', stdoutInfo).group(2) - stockInfo = tempData.split(",") - #stockCode = stockCode, - stockName = stockInfo[0] - stockEnd = stockInfo[1] #当前价，15点后为收盘价 - stockZD = stockInfo[2] #涨跌 - stockLastEnd= str(float(stockEnd) - float(stockZD)) #开盘价 - stockFD = stockInfo[3] #幅度 - stockZS = stockInfo[4] #总手 - stockZS_W = str(int(stockZS) / 100) - stockJE = stockInfo[5] #金额 - stockJE_Y = str(int(stockJE) / 10000) - content = "#" + stockName + "#" + "(" + str(stockCode) + ")" + " 收盘：" \ - + stockEnd + "，涨跌：" + stockZD + "，幅度：" + stockFD + "%" \ - + "，总手：" + stockZS_W + "万" + "，金额：" + stockJE_Y + "亿" + " " - - imgPath = "http://image.sinajs.cn/newchart/" + period + "/n/" + exchange + str(stockCode) + ".gif" - twitter = {'message': content, 'image': ''} - - except Exception as e: - print(">>>>>> Exception: " + str(e)) - else: - return twitter - finally: - None - -#国内股票数据：个股 -def getChinaStockIndividualInfo(stockCode, period): - try: - exchange = "sh" if (int(stockCode) // 100000 == 6) else "sz" - dataUrl = "http://hq.sinajs.cn/list=" + exchange + stockCode - stdout = urllib2.urlopen(dataUrl) - stdoutInfo = stdout.read().decode('gb2312').encode('utf-8') - tempData = re.search('''(")(.+)(")''', stdoutInfo).group(2) - stockInfo = tempData.split(",") - #stockCode = stockCode, - stockName = stockInfo[0] #名称 - stockStart = stockInfo[1] #开盘 - stockLastEnd= stockInfo[2] #昨收盘 - stockCur = stockInfo[3] #当前 - stockMax = stockInfo[4] #最高 - stockMin = stockInfo[5] #最低 - stockUp = round(float(stockCur) - float(stockLastEnd), 2) - if (float(stockCur) == 0.0): - stockUp = 0 - stockRange = round(float(stockUp) / float(stockLastEnd), 4) * 100 - if (stockRange == 0): - stockRange = 0 - stockVolume = round(float(stockInfo[8]) / (100 * 10000), 2) - stockMoney = round(float(stockInfo[9]) / (100000000), 2) - stockTime = stockInfo[31] - -# content = "#" + stockName + "#(" + stockCode + ")" + " 开盘:" + stockStart \ -# + ",最新:" + stockCur + ",最高:" + stockMax + ",最低:" + stockMin \ -# + ",涨跌:" + str(stockUp) + ",幅度:" + str(stockRange) + "%" \ -# + ",总手:" + str(stockVolume) + "万" + ",金额:" + str(stockMoney) \ -# + "亿" + ",更新时间:" + stockTime + " " - - content = "#" + stockName + "#(" + stockCode + ")" + " 幅度:" + str(stockRange) + "%"\ - + ",最新:" + stockCur + ",最高/最低:" + stockMax + "/" + stockMin \ - + ",总手:" + str(stockVolume) + "万" + ",金额:" + str(stockMoney) \ - + "亿" + ",更新时间:" + stockTime + " " - - #imgUrl = "http://image.sinajs.cn/newchart/" + period + "/n/" + exchange + str(stockCode) + ".gif" - #twitter = {'message': content, 'image': imgUrl} - - twitter = {'message': content, 'image':'', 'stockUp':stockUp} - - except Exception as e: - print(">>>>>> Exception: " + str(e)) - else: - return twitter - finally: - None - -#全球股票指数 -def getWorldStockIndexInfo(stockDict): - try: - #http://download.finance.yahoo.com/d/quotes.csv?s=^IXIC&f=sl1c1p2l - yahooCode = stockDict['yahoo'] - dataUrl = "http://download.finance.yahoo.com/d/quotes.csv?s=" + yahooCode + "&f=sl1c1p2l" - - stdout = urllib2.urlopen(dataUrl) - stdoutInfo = stdout.read().decode('gb2312') - tempData = stdoutInfo.replace('"', '') - stockInfo = tempData.split(",") - stockNameCn = stockDict['name']['chinese'] - stockNameEn = stockDict['name']['english'] - stockCode = stockDict['code'] - stockEnd = stockInfo[1] #当前价，5点后为收盘价 - stockZD = stockInfo[2] #涨跌 - stockLastEnd= str(float(stockEnd) - float(stockZD)) #开盘价 - stockFD = stockInfo[3] #幅度 - percent = float(stockFD.replace("%", "")) - matchResult = re.search("([\w?\s?:]*)(\-)", stockInfo[4]) #日期和最新值 - stockDate = matchResult.group(1) - - content = "#" + stockNameCn + "# " + stockNameEn + "(" + stockCode + ")" \ - + " 当前：" + stockEnd + ", 涨跌：" + stockZD + ", 幅度：" + stockFD \ - + ", 最后交易时间：" + stockDate - - twitter = content - - except Exception as err: - print(">>>>>> Exception: " + yahooCode + " " + str(err)) - else: - return twitter - finally: - None - -def test_china_index_data(): - for stockCode in ChinaStockIndexList: - twitter = getChinaStockIndexInfo(stockCode, period_daily) - #print twitter['message'] + twitter['image'] - -def test_china_individual_data(): - -# for stockCode in ChinaStockIndividualList: -# twitter = getChinaStockIndividualInfo(stockCode, period_min) -# print(twitter['message'] + twitter['image']) - total = 0.0 - for stockCodeNumDict in ChinaStockNumIndividualList: - twitter = getChinaStockIndividualInfo(stockCodeNumDict['code'], period_min) - print twitter - if twitter != None: - total += twitter['stockUp'] * stockCodeNumDict['num'] - print twitter['message'] + twitter['image'] - print '当日盈亏:' + str(total) - - -def test_global_index_data(): - for stockDict in WorldStockIndexList: - print getWorldStockIndexInfo(stockDict) - - -def main(): - "main function" - print base64.b64decode(b'Q29weXJpZ2h0IChjKSAyMDEyIERvdWN1YmUgSW5jLiBBbGwgcmlnaHRzIHJlc2VydmVkLg==').decode() - -# dataUrl = "http://ichart.yahoo.com/table.csv?s=600000.SS&a=08&b=25&c=2010&d=09&e=8&f=2010&g=d" -# stdout = urllib.request.urlopen(dataUrl) -# stdoutInfo = stdout.read().decode('utf-8') - - ISOTIMEFORMAT='%Y-%m-%d %X' - while 1: - str = time.strftime(ISOTIMEFORMAT, time.localtime()) - print str - test_china_index_data() - test_china_individual_data() - #test_global_index_data() - print '\n' - sleep(180) #180s - -if __name__ == '__main__': - #plotData() - main() - +#!/usr/local/bin/python3 +#coding=utf-8 +#source http://www.cnblogs.com/txw1958/ + +import os, io, sys, re, time, json, base64 +import webbrowser, decimal +import urllib2 +from time import sleep +from plot import plotData + + +period_All_List = [ + "min", #分时线 + "daily", #日K线 + "weekly", #周K线 + "monthly" #月K线 + ] +period_min = period_All_List[0] +period_daily = period_All_List[1] + +ChinaStockIndexList = [ + "000001", # sh000001 上证指数 + "399001", # sz399001 深证成指 + "000300", # sh000300 沪深300 + "399005", # sz399005 中小板指 + "399006", # sz399006 创业板指 + "000003",# sh000003 B股指数 + "000016",#上证50 + "000012",#国债指数 +] +ChinaStockIndividualList = [ + "600011", # + "600000", # + "002600", # + "002505", # + "000725", # + "000783", # + "600048", # + "300315", # + "002167", # + "601001", # + "150172", # +] + +ChinaStockNumIndividualList = [ + {'code':"600011", 'num':1100}, # 华能国际 + {'code':"600000", 'num':1900}, # 浦发银行 + {'code':"002600", 'num':1000}, # 江粉磁材 + {'code':"002505", 'num':1000}, # 大康牧业 + {'code':"000725", 'num':2000}, # 京东方A + {'code':"000783", 'num':600}, # 长江证券 + {'code':"600048", 'num':2000}, # 保利地产 + {'code':"300315", 'num':200}, # 掌趣科技 + {'code':"002167", 'num':600}, # 东方锆业 + {'code':"601001", 'num':1000}, # 大同煤业 + {'code':"150172", 'num':5000}, # 证券B +] + +WorldStockIndexList = [ + {'code':"000001", 'yahoo':"000001.SS",'name':{'chinese':"中国上证指数", 'english':"CHINA SHANGHAI COMPOSITE INDEX"}}, + {'code':"399001", 'yahoo':"399001.SZ",'name':{'chinese':"中国深证成指", 'english':"SZSE COMPONENT INDEX"}}, + {'code':"DJI", 'yahoo':"^DJI",'name':{'chinese':"美国道琼斯工业平均指数", 'english':"Dow Jones Industrial Average"}}, + {'code':"IXIC", 'yahoo':"^IXIC",'name':{'chinese':"美国纳斯达克综合指数", 'english':"NASDAQ Composite"},}, + {'code':"GSPC", 'yahoo':"^GSPC",'name':{'chinese':"美国标准普尔500指数", 'english':"S&P 500"}}, + {'code':"N225", 'yahoo':"^N225",'name':{'chinese':"日本日经225指数", 'english':"NIKKEI 225"}}, + {'code':"TWII", 'yahoo':"^TWII",'name':{'chinese':"台湾台北加权指数", 'english':"TSEC weighted index"}}, + {'code':"HSI", 'yahoo':"^HSI",'name':{'chinese':"香港恒生指数", 'english':"HANG SENG INDEX"}}, + {'code':"FCHI", 'yahoo':"^FCHI",'name':{'chinese':"法国CAC40指数", 'english':"CAC 40"}}, + {'code':"FTSE", 'yahoo':"^FTSE",'name':{'chinese':"英国富时100指数", 'english':"FTSE 100"}}, + {'code':"GDAXI", 'yahoo':"^GDAXI",'name':{'chinese':"德国法兰克福DAX指数", 'english':"DAX"} + } +] +WorldStockIndexList_SP500 = WorldStockIndexList[7] + +#国内股票数据：指数 +def getChinaStockIndexInfo(stockCode, period): + try: + exchange = "sz" if (int(stockCode) // 100000 == 3) else "sh" + #http://hq.sinajs.cn/list=s_sh000001 + dataUrl = "http://hq.sinajs.cn/list=s_" + exchange + stockCode + stdout = urllib2.urlopen(dataUrl) + stdoutInfo = stdout.read().decode('gb2312').encode('utf-8') + tempData = re.search('''(")(.+)(")''', stdoutInfo).group(2) + stockInfo = tempData.split(",") + #stockCode = stockCode, + stockName = stockInfo[0] + stockEnd = stockInfo[1] #当前价，15点后为收盘价 + stockZD = stockInfo[2] #涨跌 + stockLastEnd= str(float(stockEnd) - float(stockZD)) #开盘价 + stockFD = stockInfo[3] #幅度 + stockZS = stockInfo[4] #总手 + stockZS_W = str(int(stockZS) / 100) + stockJE = stockInfo[5] #金额 + stockJE_Y = str(int(stockJE) / 10000) + content = "#" + stockName + "#" + "(" + str(stockCode) + ")" + " 收盘：" \ + + stockEnd + "，涨跌：" + stockZD + "，幅度：" + stockFD + "%" \ + + "，总手：" + stockZS_W + "万" + "，金额：" + stockJE_Y + "亿" + " " + + imgPath = "http://image.sinajs.cn/newchart/" + period + "/n/" + exchange + str(stockCode) + ".gif" + twitter = {'message': content, 'image': ''} + + except Exception as e: + print(">>>>>> Exception: " + str(e)) + else: + return twitter + finally: + None + +#国内股票数据：个股 +def getChinaStockIndividualInfo(stockCode, period): + try: + exchange = "sh" if (int(stockCode) // 100000 == 6) else "sz" + dataUrl = "http://hq.sinajs.cn/list=" + exchange + stockCode + stdout = urllib2.urlopen(dataUrl) + stdoutInfo = stdout.read().decode('gb2312').encode('utf-8') + tempData = re.search('''(")(.+)(")''', stdoutInfo).group(2) + stockInfo = tempData.split(",") + #stockCode = stockCode, + stockName = stockInfo[0] #名称 + stockStart = stockInfo[1] #开盘 + stockLastEnd= stockInfo[2] #昨收盘 + stockCur = stockInfo[3] #当前 + stockMax = stockInfo[4] #最高 + stockMin = stockInfo[5] #最低 + stockUp = round(float(stockCur) - float(stockLastEnd), 2) + if (float(stockCur) == 0.0): + stockUp = 0 + stockRange = round(float(stockUp) / float(stockLastEnd), 4) * 100 + if (stockRange == 0): + stockRange = 0 + stockVolume = round(float(stockInfo[8]) / (100 * 10000), 2) + stockMoney = round(float(stockInfo[9]) / (100000000), 2) + stockTime = stockInfo[31] + +# content = "#" + stockName + "#(" + stockCode + ")" + " 开盘:" + stockStart \ +# + ",最新:" + stockCur + ",最高:" + stockMax + ",最低:" + stockMin \ +# + ",涨跌:" + str(stockUp) + ",幅度:" + str(stockRange) + "%" \ +# + ",总手:" + str(stockVolume) + "万" + ",金额:" + str(stockMoney) \ +# + "亿" + ",更新时间:" + stockTime + " " + + content = "#" + stockName + "#(" + stockCode + ")" + " 幅度:" + str(stockRange) + "%"\ + + ",最新:" + stockCur + ",最高/最低:" + stockMax + "/" + stockMin \ + + ",总手:" + str(stockVolume) + "万" + ",金额:" + str(stockMoney) \ + + "亿" + ",更新时间:" + stockTime + " " + + #imgUrl = "http://image.sinajs.cn/newchart/" + period + "/n/" + exchange + str(stockCode) + ".gif" + #twitter = {'message': content, 'image': imgUrl} + + twitter = {'message': content, 'image':'', 'stockUp':stockUp} + + except Exception as e: + print(">>>>>> Exception: " + str(e)) + else: + return twitter + finally: + None + +#全球股票指数 +def getWorldStockIndexInfo(stockDict): + try: + #http://download.finance.yahoo.com/d/quotes.csv?s=^IXIC&f=sl1c1p2l + yahooCode = stockDict['yahoo'] + dataUrl = "http://download.finance.yahoo.com/d/quotes.csv?s=" + yahooCode + "&f=sl1c1p2l" + + stdout = urllib2.urlopen(dataUrl) + stdoutInfo = stdout.read().decode('gb2312') + tempData = stdoutInfo.replace('"', '') + stockInfo = tempData.split(",") + stockNameCn = stockDict['name']['chinese'] + stockNameEn = stockDict['name']['english'] + stockCode = stockDict['code'] + stockEnd = stockInfo[1] #当前价，5点后为收盘价 + stockZD = stockInfo[2] #涨跌 + stockLastEnd= str(float(stockEnd) - float(stockZD)) #开盘价 + stockFD = stockInfo[3] #幅度 + percent = float(stockFD.replace("%", "")) + matchResult = re.search("([\w?\s?:]*)(\-)", stockInfo[4]) #日期和最新值 + stockDate = matchResult.group(1) + + content = "#" + stockNameCn + "# " + stockNameEn + "(" + stockCode + ")" \ + + " 当前：" + stockEnd + ", 涨跌：" + stockZD + ", 幅度：" + stockFD \ + + ", 最后交易时间：" + stockDate + + twitter = content + + except Exception as err: + print(">>>>>> Exception: " + yahooCode + " " + str(err)) + else: + return twitter + finally: + None + +def test_china_index_data(): + for stockCode in ChinaStockIndexList: + twitter = getChinaStockIndexInfo(stockCode, period_daily) + print twitter['message'] + twitter['image'] + +def test_china_individual_data(): + + # for stockCode in ChinaStockIndividualList: + # twitter = getChinaStockIndividualInfo(stockCode, period_min) + # print(twitter['message'] + twitter['image']) + total = 0.0 + for stockCodeNumDict in ChinaStockNumIndividualList: + twitter = getChinaStockIndividualInfo(stockCodeNumDict['code'], period_min) + print twitter + if twitter != None: + total += twitter['stockUp'] * stockCodeNumDict['num'] + print twitter['message'] + twitter['image'] + print '当日盈亏:' + str(total) + +def test_global_index_data(): + for stockDict in WorldStockIndexList: + print getWorldStockIndexInfo(stockDict) + +def main(): + "main function" + print base64.b64decode(b'Q29weXJpZ2h0IChjKSAyMDEyIERvdWN1YmUgSW5jLiBBbGwgcmlnaHRzIHJlc2VydmVkLg==').decode() + # dataUrl = "http://ichart.yahoo.com/table.csv?s=600000.SS&a=08&b=25&c=2010&d=09&e=8&f=2010&g=d" + # stdout = urllib.request.urlopen(dataUrl) + # stdoutInfo = stdout.read().decode('utf-8') + ISOTIMEFORMAT='%Y-%m-%d %X' + while 1: + str = time.strftime(ISOTIMEFORMAT, time.localtime()) + print str + test_china_index_data() + # test_china_individual_data() + test_global_index_data() + print '\n' + sleep(10) #180s + +if __name__ == '__main__': + #plotData() + main() diff --git a/stock_trader.py b/stock_trader.py new file mode 100644 index 0000000..2d2e00b --- /dev/null +++ b/stock_trader.py @@ -0,0 +1,31 @@ +#coding=utf-8 +from trade_process.strategy.macd_back_test import macdmain +from trade_process.strategy.tread_tracking import stock_trader_main +from trade_process.strategy.macd_live_test import macd_live_main +from trade_process.efund_mail2 import main1 +from trade_process.fund_zf import main_zf +if __name__ == '__main__': + stockList = ['000725', '000783', '002167', '002505', '002600', '300315', '600000', '600011', '600048', '601001'] + ChinaStockIndexList = [ + "000001", # sh000001 上证指数 + "399001", # sz399001 深证成指 + "000300", # sh000300 沪深300 + "399005", # sz399005 中小板指 + "399006", # sz399006 创业板指 + "000003", # sh000003 B股指数 + "000016", # 上证50 + "000012", # 国债指数 + ] + code = []#[ ['160222', '国泰国证食品饮料行业指数分级'],['110022', 'eConsumption '],['110003', 'eSSE50'], ['110020', 'HS300'], ['110028', 'anxinB'], + # ['002963', 'egold'], ['003321', 'eoil'], ['004744', 'eGEI'], + # ['110031', 'eHSI'], ['161130', 'eNASDAQ100'],['161125', 'SPX500']] + # fundlist=main_zf() + # for i in range(0,1):#len(fundlist) + # code.append(fundlist[i]) + #均线策略 + # macd=macdmain(code) + # print macd + + main1() + # stock_trader_main(code) + # macd_live_main(code) diff --git a/stockdata/GAOpt.txt b/stockdata/GAOpt.txt new file mode 100644 index 0000000..954b011 --- /dev/null +++ b/stockdata/GAOpt.txt @@ -0,0 +1,291 @@ +MACD|110022| eConsumption |5, 10, 37| 69.115635200000014, +MACD|110003| eSSE50|6, 24, 65| 47.789947559999973, +MACD|110020| HS300|19, 34, 69| 23.323488000000015, +MACD|110028| anxinB|17, 32, 89| 12.543257519999996, +MACD|002963| egold|20, 19, 89| 1.5666868000000118, +MACD|003321| eoil|1, 13, 64| 24.28305155, +MACD|004744| eGEI|1, 50, 50| 1.9942221199999899, +MACD|110031| eHSI|17, 39, 96| 15.212850880000008, +MACD|161130| eNASDAQ100|20, 19, 97| 9.8934821999999887, +MACD|161125| SPX500|18, 43, 67| 9.1397807199999992, +MACD|110022| eConsumption |5, 10, 37| 69.115635200000014, +MACD|110003| eSSE50|6, 24, 65| 47.789947559999973, +MACD|160222|̩֤ʳƷҵָּ|3, 7, 97|32.97278 +MA|160222|̩֤ʳƷҵָּ|1, 10, 99|23.73437 +MA|160222|̩֤ʳƷҵָּ|11, 10, 13|27.05073 +MA|110022|eConsumption |20, 8, 90|30.12688 +MA|110003|eSSE50|17, 18, 56|25.34664 +MA|110020|HS300|20, 10, 44|13.54273 +MA|110028|anxinB|8, 8, 32|3.8957 +MA|002963|egold|1, 6, 12|2.401 +MA|003321|eoil|17, 6, 56|24.70722 +MA|004744|eGEI|6, 7, 100|7.05999 +MA|110031|eHSI|8, 6, 34|14.02487 +MA|161130|eNASDAQ100|9, 36, 12|11.02345 +MA|161125|SPX500|2, 39, 17|10.18168 + +DMA|160222|̩֤ʳƷҵָּ|14, 27, 78|10.8117 +MA|160222|̩֤ʳƷҵָּ|1, 10, 89|23.73437 +MACD|160222|̩֤ʳƷҵָּ|4, 9, 78|32.97278 +TRIX|160222|̩֤ʳƷҵָּ|12, 9, 78|7.02884 +DMA|110022|eConsumption |17, 31, 78|25.39119 +MA|110022|eConsumption |19, 7, 78|30.12688 +MACD|110022|eConsumption |10, 21, 78|28.49607 +TRIX|110022|eConsumption |1, 32, 78|29.85222 +DMA|110003|eSSE50|6, 30, 78|9.98048 +MA|110003|eSSE50|6, 6, 16|26.20661 +MACD|110003|eSSE50|19, 21, 72|25.34664 +TRIX|110003|eSSE50|9, 49, 78|9.94463 +DMA|110020|HS300|18, 29, 78|7.24066 +MA|110020|HS300|6, 6, 10|14.02331 +MACD|110020|HS300|6, 55, 78|9.52746 +TRIX|110020|HS300|13, 30, 78|6.44664 +DMA|110028|anxinB|20, 34, 78|3.71367 +MA|110028|anxinB|10, 8, 22|3.8957 +MACD|110028|anxinB|10, 25, 78|3.832 +TRIX|110028|anxinB|11, 20, 78|3.36012 +DMA|002963|egold|5, 16, 78|3.04324 +MA|002963|egold|1, 7, 10|2.401 +MACD|002963|egold|19, 10, 72|0.95787 +TRIX|002963|egold|6, 9, 78|2.71533 +DMA|003321|eoil|14, 45, 78|12.08337 +MA|003321|eoil|18, 6, 72|24.70722 +MACD|003321|eoil|3, 35, 78|17.40164 +TRIX|003321|eoil|6, 9, 78|6.83046 +DMA|004744|eGEI|2, 10, 78|2.72784 +MA|004744|eGEI|6, 7, 95|7.05999 +MACD|004744|eGEI|2, 6, 78|7.57617 +TRIX|004744|eGEI|5, 20, 78|1.82664 +DMA|110031|eHSI|2, 16, 78|11.32942 +MA|110031|eHSI|9, 7, 78|14.02487 +MACD|110031|eHSI|17, 11, 78|11.72215 +TRIX|110031|eHSI|6, 9, 78|11.27058 +DMA|161130|eNASDAQ100|17, 13, 78|4.7883 +MA|161130|eNASDAQ100|12, 34, 14|10.94492 +MACD|161130|eNASDAQ100|1, 17, 82|8.56408 +TRIX|161130|eNASDAQ100|6, 9, 78|2.15505 +DMA|161125|SPX500|2, 10, 78|2.21374 +MA|161125|SPX500|2, 38, 17|10.18168 +MACD|161125|SPX500|20, 15, 72|8.431 +TRIX|161125|SPX500|5, 20, 78|2.19041 +DMA|001865|ǰԴ¼C |11, 32, 80|10.64591 +MA|001865|ǰԴ¼C |20, 19, 77|27.74885 +MACD|001865|ǰԴ¼C |3, 10, 62|26.50087 +TRIX|001865|ǰԴ¼C |14, 7, 38|9.59872 +DMA|165312|50 Ʊָ|12, 55, 57|9.11024 +MA|165312|50 Ʊָ|1, 45, 12|6.2966 +MACD|165312|50 Ʊָ|9, 7, 80|6.80788 +TRIX|165312|50 Ʊָ|7, 59, 70|5.64648 +DMA|001892|ʢ˳ɳ |9, 31, 28|15.51121 +MA|001892|ʢ˳ɳ |6, 41, 46|29.17076 +MACD|001892|ʢ˳ɳ |4, 16, 70|28.4102 +TRIX|001892|ʢ˳ɳ |11, 34, 57|13.8076 +DMA|260110|˳Ǿѡ |19, 29, 46|9.08645 +MA|260110|˳Ǿѡ |5, 15, 94|16.07584 +MACD|260110|˳Ǿѡ |1, 7, 84|9.76416 +TRIX|260110|˳Ǿѡ |5, 44, 70|3.88812 +DMA|260116|˳ǺľA |7, 36, 84|11.57253 +MA|260116|˳ǺľA |6, 6, 12|29.42919 +MACD|260116|˳ǺľA |2, 21, 22|27.23138 +TRIX|260116|˳ǺľA |10, 52, 70|11.58646 +DMA|002085|ʢ+ |9, 34, 84|15.61371 +MA|002085|ʢ+ |12, 31, 16|28.66209 +MACD|002085|ʢ+ |2, 23, 79|27.78855 +TRIX|002085|ʢ+ |11, 34, 57|13.80577 +MA|260108|˳˳ɳ |19, 12, 91|18.66496 +MACD|260108|˳˳ɳ |4, 19, 99|19.27349 +MA|001740|й2025 |8, 6, 17|19.85835 +MACD|001740|й2025 |19, 8, 91|22.19493 +MA|160632|Ʒּ Ʊָ|2, 38, 94|15.60955 +MACD|160632|Ʒּ Ʊָ|3, 7, 84|28.96886 +MA|206007|ѡ |6, 6, 16|30.71428 +MACD|206007|ѡ |8, 20, 46|30.24909 +MA|001542|̩+Ʊ Ʊ|2, 54, 83|13.73456 +MACD|001542|̩+Ʊ Ʊ|7, 8, 62|29.22265 +MA|000854|ϲҵ Ʊ|17, 10, 66|31.1615 +MACD|000854|ϲҵ Ʊ|4, 8, 62|30.35519 +MA|161725|֤׾ָּ Ʊָ|5, 14, 12|15.08644 +MACD|161725|֤׾ָּ Ʊָ|19, 18, 84|18.2056 +MA|550016|ųԶC ּܸ|16, 8, 46|47.78 +MACD|550016|ųԶC ּܸ|9, 19, 91|47.78 +MA|000431|Ʒƴл |20, 10, 70|30.68181 +MACD|000431|Ʒƴл |7, 21, 29|30.15358 +MA|000248|֤ҪETF ӻ|3, 47, 55|17.36217 +MACD|000248|֤ҪETF ӻ|7, 9, 62|39.34048 +MA|519664|A |20, 6, 84|33.84116 +MACD|519664|A |2, 16, 79|34.4181 +MA|020005|̩Ƚ |9, 6, 28|17.63986 +MACD|020005|̩Ƚ |19, 21, 29|16.80808 +MA|000083|ҵ |20, 28, 98|33.64974 +MACD|000083|ҵ |3, 32, 22|32.07629 +MA|481012|֤ ӻ|16, 6, 36|30.93803 +MACD|481012|֤ ӻ|2, 18, 86|29.76042 +MA|519665|C |19, 6, 84|33.88362 +MACD|519665|C |2, 16, 86|33.95783 +MA|180012|ԣ |1, 52, 71|13.84414 +MACD|180012|ԣ |4, 15, 31|25.50005 +MA|070032|ʵŻ |3, 13, 68|17.79934 +MACD|070032|ʵŻ |4, 7, 62|33.33868 +MA|001170|̩˻ |19, 11, 82|28.59445 +MACD|001170|̩˻ |13, 21, 91|26.55327 +MA|040008|ѡ |9, 8, 47|23.24383 +MACD|040008|ѡ |8, 10, 62|22.43298 +MA|004008|˼·A |2, 47, 10|0.7119 +MACD|004008|˼·A |2, 7, 84|0.58005 +MA|004009|˼·C |3, 51, 17|0.31564 +MACD|004009|˼·C |2, 11, 84|0.33037 +MA|519056|ͨȵ |1, 25, 30|15.40842 +MACD|519056|ͨȵ |7, 9, 62|31.0742 +MA|260103|˳Ƕƽ |17, 8, 34|25.13643 +MACD|260103|˳Ƕƽ |7, 8, 84|25.36861 +MA|001287|A |1, 48, 27|25.32231 +MACD|001287|A |7, 8, 62|34.63417 +MA|000619|ҵ |8, 8, 84|29.99624 +MACD|000619|ҵ |8, 9, 27|29.75675 +MA|001387|¾ûA |8, 20, 16|0.85731 +MACD|001387|¾ûA |14, 6, 79|1.63479 +MA|001631|֤ʳƷָA Ʊָ|4, 43, 82|15.36307 +MACD|001631|֤ʳƷָA Ʊָ|2, 9, 62|29.38617 +MA|000742|̩¾û |6, 33, 73|12.26891 +MACD|000742|̩¾û |3, 7, 84|26.21631 +MA|001632|֤ʳƷָC Ʊָ|3, 54, 82|15.32682 +MACD|001632|֤ʳƷָC Ʊָ|2, 9, 62|29.23509 +MA|001606|ũҵ4.0 |1, 20, 72|19.79911 +MACD|001606|ũҵ4.0 |2, 28, 22|29.37062 +MA|000462|ũֶ |15, 12, 93|34.61638 +MACD|000462|ũֶ |4, 15, 46|34.24478 +MA|001112|йƻ |19, 6, 22|29.07516 +MACD|001112|йƻ |19, 20, 27|28.59173 +MA|001281|ѡA |16, 8, 46|26.79772 +MACD|001281|ѡA |1, 50, 87|22.94161 +MA|002072|ѡC |16, 8, 46|26.71202 +MACD|002072|ѡC |1, 50, 87|22.8868 +MA|310388| |16, 49, 28|2.26845 +MACD|310388| |5, 9, 15|20.45848 +MA|519690|ȽûA |4, 14, 77|20.23216 +MACD|519690|ȽûA |2, 16, 86|12.16381 +MA|001076|׷ĸ |8, 25, 42|13.77119 +MACD|001076|׷ĸ |2, 16, 46|32.92077 +MA|530015|֤60 ӻ|15, 9, 34|28.01786 +MACD|530015|֤60 ӻ|4, 6, 62|27.11091 +MA|360007|󱣵û |8, 8, 85|17.34228 +MACD|360007|󱣵û |20, 11, 84|18.82594 +MA|540012|źͷָA Ʊָ|14, 56, 17|10.73188 +MACD|540012|źͷָA Ʊָ|19, 6, 38|10.88099 +MA|003119|ʱԴA |9, 23, 15|29.95984 +MACD|003119|ʱԴA |1, 46, 22|29.6994 +MA|004528|ʢʢͨծA none|1, 25, 10|27.26729 +MACD|004528|ʢʢͨծA none|1, 44, 70|26.91351 +MA|003120|ʱԴC |7, 26, 12|29.98995 +MACD|003120|ʱԴC |1, 28, 79|29.5992 +MA|660010|ũԾѡ |1, 11, 100|21.87402 +MACD|660010|ũԾѡ |12, 15, 57|26.63535 +MA|000423|ǰԴ¼A |6, 6, 37|27.10071 +MACD|000423|ǰԴ¼A |3, 10, 84|26.17073 +MA|519606|̩ Ʊ|11, 8, 99|14.74101 +MACD|519606|̩ Ʊ|12, 14, 82|14.61055 +MA|001044|ʵѹƱ Ʊ|15, 9, 91|24.05337 +MACD|001044|ʵѹƱ Ʊ|3, 6, 84|20.06904 +MA|001583|³̬Ʊ Ʊ|6, 6, 10|21.27152 +MACD|001583|³̬Ʊ Ʊ|3, 32, 22|18.07508 +MA|001208|ŵ̼ùƱ Ʊ|15, 13, 31|29.34498 +MACD|001208|ŵ̼ùƱ Ʊ|5, 16, 84|30.05486 +MA|160605|й50 |6, 6, 84|29.01738 +MACD|160605|й50 |19, 38, 22|26.05775 +MA|020026|̩ɳѡ |8, 8, 12|10.39039 +MACD|020026|̩ɳѡ |4, 23, 13|8.42245 +MA|001605|ɳѡƱ Ʊ|8, 8, 16|28.61427 +MACD|001605|ɳѡƱ Ʊ|4, 7, 62|31.60846 +MA|001703|Ʊ Ʊ|2, 12, 80|14.43299 +MACD|001703|Ʊ Ʊ|7, 8, 84|25.9319 +MA|001179|°󽡿û |18, 12, 79|27.19698 +MACD|001179|°󽡿û |7, 8, 27|26.6181 +MA|164906|֤йָ QDII-ָ|8, 8, 64|9.02807 +MACD|164906|֤йָ QDII-ָ|15, 21, 84|11.59911 +MA|519066|Ƚ |15, 11, 91|16.88678 +MACD|519066|Ƚ |2, 38, 79|14.4195 +MA|110011|׷С̻ |6, 6, 79|25.94807 +MACD|110011|׷С̻ |4, 15, 94|25.94807 +MA|160628|زּ Ʊָ|11, 10, 13|27.29184 +MACD|160628|زּ Ʊָ|7, 8, 62|25.01878 +MA|519746|ծȯA ծȯ|16, 33, 12|0.0 +MACD|519746|ծȯA ծȯ|4, 11, 80|1.63224 +MA|002853|ԪûA |9, 6, 91|0.54829 +MACD|002853|ԪûA |1, 7, 84|4.22898 +MA|241001|йɳ QDII|8, 8, 11|12.48513 +MACD|241001|йɳ QDII|5, 14, 85|10.5786 +MA|519068|ɳ |9, 8, 28|26.81625 +MACD|519068|ɳ |11, 18, 22|24.90172 +MA|001581|ͨѡû |8, 8, 76|25.13814 +MACD|001581|ͨѡû |4, 7, 62|25.46318 +MA|000634|ʢû |1, 36, 13|13.3687 +MACD|000634|ʢû |4, 8, 27|21.65084 +MA|161129|׷ԭA QDII|19, 6, 99|25.02004 +MACD|161129|׷ԭA QDII|4, 54, 37|17.57164 +MA|540009|Ѻ Ʊ|7, 48, 58|6.05222 +MACD|540009|Ѻ Ʊ|8, 9, 46|13.54252 +MA|000029|۲ |1, 44, 13|21.55451 +MACD|000029|۲ |6, 9, 27|30.78001 +MA|519225|ͨծȯ ծȯ|1, 29, 16|23.51001 +MACD|519225|ͨծȯ ծȯ|1, 13, 84|23.3129 +MA|570001|ŵ¼ֵƻ |16, 8, 46|23.1882 +MACD|570001|ŵ¼ֵƻ |3, 42, 31|20.67356 +MA|000934|лѡ QDII|10, 10, 13|13.59015 +MACD|000934|лѡ QDII|4, 8, 46|14.31654 +MA|160218|̩֤زҵָּ Ʊָ|7, 6, 16|21.30797 +MACD|160218|̩֤زҵָּ Ʊָ|7, 9, 49|18.98304 +MA|001712|ƾѡ |8, 8, 62|22.53789 +MACD|001712|ƾѡ |2, 24, 29|21.85712 +MA|161721|̻300زָּ Ʊָ|17, 12, 58|18.06743 +MACD|161721|̻300زָּ Ʊָ|17, 12, 46|18.83812 +MA|002124|㷢˲ҵ |13, 8, 91|28.12136 +MACD|002124|㷢˲ҵ |1, 44, 77|23.99838 +MA|000988|ʵȫƱ QDII|8, 7, 13|5.54386 +MACD|000988|ʵȫƱ QDII|19, 16, 79|4.88145 +MA|020023|̩¼Ի |20, 16, 100|23.64391 +MACD|020023|̩¼Ի |10, 15, 22|22.06978 +MA|160216|̩Ʒ QDII|19, 6, 99|23.82199 +MACD|160216|̩Ʒ QDII|11, 16, 79|20.05076 +MA|001518| |17, 14, 61|20.50531 +MACD|001518| |2, 23, 84|19.26242 +MA|398061|кѻ |2, 11, 72|7.73835 +MACD|398061|кѻ |6, 9, 62|27.09197 +MA|002666|ǰԴ³ɳA |3, 17, 79|17.69615 +MACD|002666|ǰԴ³ɳA |2, 23, 84|24.14857 +DMA|000751|ʵ˲ҵƱ Ʊ|15, 42, 19|5.46908 +MA|000751|ʵ˲ҵƱ Ʊ|7, 5, 79|19.78493 +MACD|000751|ʵ˲ҵƱ Ʊ|5, 7, 12|19.33581 +MA|162605|˳Ƕ(LOF) |5, 5, 57|32.75414 +MACD|162605|˳Ƕ(LOF) |7, 10, 22|30.93892 +MA|002851|ϷƷʻ |12, 7, 91|29.55376 +MACD|002851|ϷƷʻ |1, 15, 11|25.78834 +MA|169101| |19, 44, 28|10.23124 +MACD|169101| |7, 9, 9|8.33333 +MA|519772|û |7, 7, 84|34.97707 +MACD|519772|û |18, 19, 4|33.5086 +MA|270041|㷢Ʒѡ |7, 6, 79|25.93774 +MACD|270041|㷢Ʒѡ |2, 9, 10|25.66987 +MA|217027|Ӳƾ50ָA Ʊָ|9, 5, 29|26.60504 +MACD|217027|Ӳƾ50ָA Ʊָ|2, 37, 4|21.43469 +MA|163412|ȫʲ(LOF) |19, 6, 22|30.05601 +MACD|163412|ȫʲ(LOF) |7, 11, 21|30.00747 +MA|160127|Ϸѷּ Ʊ|17, 5, 22|20.32771 +MACD|160127|Ϸѷּ Ʊ|2, 19, 25|20.54125 +MA|001725|߶Ʊ Ʊ|19, 6, 22|32.36023 +MACD|001725|߶Ʊ Ʊ|2, 19, 25|28.70576 +MA|000173|30 |7, 7, 20|31.15095 +MACD|000173|30 |5, 13, 6|28.80672 +MA|002624|㷢ѡ |19, 6, 22|31.14472 +MACD|002624|㷢ѡ |4, 6, 4|31.39336 +MA|519008|ƾѡ |19, 6, 22|24.25781 +MACD|519008|ƾѡ |4, 10, 18|22.61552 +MA|000167|㷢ûA |2, 38, 39|10.14022 +MACD|000167|㷢ûA |20, 12, 14|22.93245 +MA|001371|ֵ |2, 38, 12|8.79377 +MACD|001371|ֵ |11, 14, 15|8.45617 +MA|376510|Ͷ Ʊ|5, 5, 34|16.99092 +MACD|376510|Ͷ Ʊ|5, 15, 18|15.59401 +MA|163406|ȫּ |19, 6, 22|27.97797 +MACD|163406|ȫּ |3, 11, 14|28.4111 + + diff --git a/stockdata/GAOpt100-1.txt b/stockdata/GAOpt100-1.txt new file mode 100644 index 0000000..6438053 --- /dev/null +++ b/stockdata/GAOpt100-1.txt @@ -0,0 +1,545 @@ +MA|002963|egold|2, 37, 12|2.3427 +MA|002963|egold|2, 37, 12|2.3427 +MACD|002963|egold|19, 11, 18|1.76252 +MA|003321|eoil|8, 7, 62|24.42502 +MACD|003321|eoil|5, 8, 25|24.19651 +MA|004744|eGEI|2, 55, 25|0.98452 +MACD|004744|eGEI|7, 4, 4|2.83792 +MA|110003|eSSE50|20, 20, 29|24.2869 +MACD|110003|eSSE50|2, 27, 5|18.14658 +MA|110020|HS300|19, 5, 84|13.12161 +MACD|110020|HS300|15, 6, 10|10.16974 +MA|110031|eHSI|7, 7, 10|14.36478 +MACD|110031|eHSI|1, 49, 23|10.21904 +MA|161130|eNASDAQ100|20, 7, 91|11.30478 +MACD|161130|eNASDAQ100|1, 19, 24|11.22633 +MA|110028|anxinB|1, 24, 12|4.72306 +MACD|110028|anxinB|8, 4, 30|5.43585 +MA|110022|eConsumption |7, 7, 27|31.46783 +MACD|110022|eConsumption |2, 8, 28|19.73073 +MA|161125|SPX500|9, 7, 12|10.50693 +MACD|161125|SPX500|1, 20, 7|9.38309 +MA|161725|֤׾ָּ Ʊָ|5, 9, 12|12.91174 +MACD|161725|֤׾ָּ Ʊָ|11, 4, 5|19.77912 +MA|160222|̩֤ʳƷҵָּ Ʊָ|19, 5, 57|29.4182 +MACD|160222|̩֤ʳƷҵָּ Ʊָ|3, 6, 5|23.90961 +MA|110022|׷ҵ Ʊ|7, 7, 27|31.46783 +MACD|110022|׷ҵ Ʊ|2, 8, 28|19.73073 +MA|000248|֤ҪETF ӻ|5, 5, 31|32.04681 +MACD|000248|֤ҪETF ӻ|3, 5, 7|27.19769 +MA|162605|˳Ƕ(LOF) |6, 5, 12|30.19003 +MACD|162605|˳Ƕ(LOF) |7, 8, 5|27.0848 +MA|260108|˳˳ɳ |6, 5, 12|19.0824 +MACD|260108|˳˳ɳ |7, 8, 5|16.2189 +MA|001740|й2025 |2, 43, 35|22.58922 +MACD|001740|й2025 |17, 6, 14|26.48373 +MA|160632|Ʒּ Ʊָ|19, 7, 84|22.10569 +MACD|160632|Ʒּ Ʊָ|3, 9, 7|18.46979 +MA|000083|ҵ |19, 6, 22|31.15011 +MACD|000083|ҵ |7, 8, 4|26.48062 +MA|180012|ԣ |19, 9, 65|22.00224 +MACD|180012|ԣ |6, 11, 10|13.47839 +MA|001542|̩+Ʊ Ʊ|1, 48, 94|12.82707 +MACD|001542|̩+Ʊ Ʊ|2, 15, 14|19.1224 +MA|070032|ʵŻ |16, 5, 91|27.15053 +MACD|070032|ʵŻ |15, 33, 4|12.92633 +MA|000619|ҵ |1, 24, 71|13.26834 +MACD|000619|ҵ |4, 11, 14|19.69273 +MA|004008|˼·A |1, 51, 10|1.73616 +MACD|004008|˼·A |18, 13, 25|2.36077 +MA|001112|йƻ |4, 55, 45|9.85887 +MACD|001112|йƻ |3, 15, 7|21.38199 +MA|481012|֤ ӻ|7, 7, 12|28.47518 +MACD|481012|֤ ӻ|7, 8, 25|28.47518 +MA|004009|˼·C |8, 50, 13|1.35105 +MACD|004009|˼·C |18, 15, 28|1.44043 +MA|519664|A |2, 15, 45|14.37127 +MACD|519664|A |7, 15, 4|28.40349 +MA|206007|ѡ |7, 7, 84|27.66545 +MACD|206007|ѡ |2, 8, 10|25.32238 +MA|001387|¾ûA |9, 31, 15|2.56616 +MACD|001387|¾ûA |8, 4, 25|2.5797 +MA|519665|C |2, 24, 47|14.24056 +MACD|519665|C |6, 7, 25|28.68753 +MA|310388| |6, 16, 86|4.18381 +MACD|310388| |4, 11, 7|11.53562 +MA|001208|ŵ̼ùƱ Ʊ|7, 7, 62|27.7392 +MACD|001208|ŵ̼ùƱ Ʊ|2, 13, 8|26.80584 +MA|000854|ϲҵ Ʊ|9, 7, 39|28.16949 +MACD|000854|ϲҵ Ʊ|2, 8, 9|25.44791 +MA|000462|ũֶ |19, 6, 22|28.55242 +MACD|000462|ũֶ |2, 8, 5|25.66163 +MA|000742|̩¾û |5, 5, 22|16.55535 +MACD|000742|̩¾û |1, 14, 24|16.2489 +MA|001076|׷ĸ |5, 5, 19|32.0736 +MACD|001076|׷ĸ |2, 10, 13|26.1899 +MA|001606|ũҵ4.0 |19, 6, 22|21.64179 +MACD|001606|ũҵ4.0 |11, 15, 28|21.57632 +MA|110003|׷֤50ָA Ʊָ|20, 20, 29|24.2869 +MACD|110003|׷֤50ָA Ʊָ|2, 27, 5|18.14658 +MA|001631|֤ʳƷָA Ʊָ|19, 5, 27|22.86178 +MACD|001631|֤ʳƷָA Ʊָ|4, 11, 4|19.33031 +MA|000431|Ʒƴл |16, 7, 61|28.51763 +MACD|000431|Ʒƴл |1, 16, 25|25.89481 +MA|550016|ųԶC ּܸ|20, 5, 82|46.33926 +MACD|550016|ųԶC ּܸ|10, 4, 4|44.37566 +MA|519066|Ƚ |6, 5, 12|14.86805 +MACD|519066|Ƚ |4, 19, 13|13.47087 +MA|040008|ѡ |7, 5, 20|17.60528 +MACD|040008|ѡ |1, 38, 7|13.20098 +MA|519690|ȽûA |1, 5, 27|3.55505 +MACD|519690|ȽûA |2, 17, 11|3.08913 +MA|001632|֤ʳƷָC Ʊָ|19, 6, 91|22.75947 +MACD|001632|֤ʳƷָC Ʊָ|4, 11, 4|19.24065 +MA|260116|˳ǺľA |8, 7, 62|26.6666 +MACD|260116|˳ǺľA |4, 13, 4|24.5517 +MA|165312|50 Ʊָ|1, 7, 73|11.34574 +MACD|165312|50 Ʊָ|8, 6, 5|7.57796 +MA|001287|A |5, 5, 10|23.77526 +MACD|001287|A |1, 26, 7|19.45345 +MA|260110|˳Ǿѡ |5, 5, 29|9.22322 +MACD|260110|˳Ǿѡ |2, 4, 12|9.58932 +MA|169101| |20, 28, 100|9.90889 +MACD|169101| |20, 6, 13|18.97201 +MA|001281|ѡA |14, 6, 91|24.27128 +MACD|001281|ѡA |20, 11, 14|17.60129 +MA|002072|ѡC |20, 10, 91|24.219 +MACD|002072|ѡC |20, 11, 14|17.55579 +MA|110011|׷С̻ |19, 6, 22|23.66928 +MACD|110011|׷С̻ |2, 8, 25|22.7685 +MA|519772|û |5, 57, 31|6.7283 +MACD|519772|û |3, 15, 27|19.28457 +MA|001170|̩˻ |2, 54, 74|12.54725 +MACD|001170|̩˻ |4, 12, 18|24.70781 +MA|660010|ũԾѡ |19, 6, 22|21.15983 +MACD|660010|ũԾѡ |8, 20, 10|11.73932 +MA|519068|ɳ |19, 6, 22|23.82055 +MACD|519068|ɳ |3, 17, 15|20.54364 +MA|360007|󱣵û |2, 31, 25|20.56427 +MACD|360007|󱣵û |18, 17, 7|22.79146 +MA|217027|Ӳƾ50ָA Ʊָ|7, 7, 15|21.20363 +MACD|217027|Ӳƾ50ָA Ʊָ|4, 10, 6|20.10648 +MA|020005|̩Ƚ |5, 5, 13|14.42656 +MACD|020005|̩Ƚ |2, 8, 30|3.78229 +MA|540012|źͷָA Ʊָ|5, 5, 10|17.80648 +MACD|540012|źͷָA Ʊָ|17, 11, 11|25.01403 +MA|530015|֤60 ӻ|18, 35, 23|13.79416 +MACD|530015|֤60 ӻ|4, 8, 15|24.13326 +MA|001044|ʵѹƱ Ʊ|9, 7, 12|18.82961 +MACD|001044|ʵѹƱ Ʊ|3, 9, 6|18.26466 +MA|519606|̩ Ʊ|13, 10, 89|2.93671 +MACD|519606|̩ Ʊ|13, 16, 14|9.7732 +MA|002851|ϷƷʻ |17, 14, 45|25.0769 +MACD|002851|ϷƷʻ |20, 12, 30|20.60417 +MA|519746|ծȯA ծȯ|16, 32, 12|0.0 +MACD|519746|ծȯA ծȯ|4, 6, 19|1.48818 +MA|241001|йɳ QDII|19, 5, 79|9.99438 +MACD|241001|йɳ QDII|20, 4, 7|7.57824 +MA|000173|30 |1, 28, 10|13.82716 +MACD|000173|30 |11, 14, 5|22.37067 +MA|100022|ǿ |13, 12, 59|9.76229 +MACD|100022|ǿ |6, 10, 18|14.14801 +MA|002853|ԪûA |7, 7, 79|2.5409 +MACD|002853|ԪûA |2, 8, 22|2.43367 +MA|020026|̩ɳѡ |1, 24, 98|2.06215 +MACD|020026|̩ɳѡ |14, 17, 13|9.37435 +MA|001703|Ʊ Ʊ|5, 5, 84|21.43973 +MACD|001703|Ʊ Ʊ|9, 12, 24|20.21688 +MA|001712|ƾѡ |2, 31, 60|9.81494 +MACD|001712|ƾѡ |2, 18, 12|17.42009 +MA|540009|Ѻ Ʊ|20, 5, 82|9.18962 +MACD|540009|Ѻ Ʊ|4, 28, 5|7.49183 +MA|519056|ͨȵ |19, 5, 28|25.97743 +MACD|519056|ͨȵ |6, 20, 27|24.92077 +MA|164906|֤йָ QDII-ָ|5, 5, 31|11.29221 +MACD|164906|֤йָ QDII-ָ|4, 7, 12|10.64234 +MA|160127|Ϸѷּ Ʊ|5, 5, 39|17.7383 +MACD|160127|Ϸѷּ Ʊ|19, 21, 28|15.63403 +MA|001179|°󽡿û |1, 24, 76|12.93788 +MACD|001179|°󽡿û |2, 25, 6|13.56331 +MA|260103|˳Ƕƽ |19, 6, 22|22.95207 +MACD|260103|˳Ƕƽ |5, 7, 27|22.95207 +MA|001371|ֵ |2, 38, 12|9.1751 +MACD|001371|ֵ |1, 11, 5|8.48011 +MA|001583|³̬Ʊ Ʊ|7, 5, 22|16.46919 +MACD|001583|³̬Ʊ Ʊ|6, 8, 24|17.67756 +MA|000934|лѡ QDII|5, 5, 22|11.98598 +MACD|000934|лѡ QDII|18, 15, 6|9.93118 +MA|020023|̩¼Ի |16, 11, 65|17.55517 +MACD|020023|̩¼Ի |6, 12, 24|17.55517 +MA|001725|߶Ʊ Ʊ|7, 7, 53|28.61564 +MACD|001725|߶Ʊ Ʊ|1, 23, 11|16.49532 +MA|270041|㷢Ʒѡ |7, 7, 84|21.50904 +MACD|270041|㷢Ʒѡ |8, 14, 13|21.50904 +MA|000751|ʵ˲ҵƱ Ʊ|7, 7, 80|14.7188 +MACD|000751|ʵ˲ҵƱ Ʊ|10, 15, 28|13.73035 +MA|001605|ɳѡƱ Ʊ|2, 10, 93|12.4878 +MACD|001605|ɳѡƱ Ʊ|3, 9, 9|21.4424 +MA|570001|ŵ¼ֵƻ |19, 6, 22|20.71597 +MACD|570001|ŵ¼ֵƻ |10, 44, 10|8.48075 +MA|110015|׷ҵȻ |19, 31, 23|2.97455 +MACD|110015|׷ҵȻ |7, 15, 4|13.6418 +MA|002624|㷢ѡ |1, 28, 27|10.57097 +MACD|002624|㷢ѡ |8, 14, 14|23.06265 +MA|163412|ȫʲ(LOF) |7, 7, 91|26.19975 +MACD|163412|ȫʲ(LOF) |5, 6, 5|26.19975 +MA|002666|ǰԴ³ɳA |3, 28, 78|7.89739 +MACD|002666|ǰԴ³ɳA |5, 11, 11|16.21011 +MA|180013|ȲԻ |19, 6, 22|23.81262 +MACD|180013|ȲԻ |4, 11, 30|17.53443 +MA|519008|ƾѡ |6, 38, 89|8.28596 +MACD|519008|ƾѡ |9, 18, 5|16.55979 +MA|003120|ʱԴC |8, 15, 85|31.23123 +MACD|003120|ʱԴC |2, 15, 6|31.1 +MA|002667|ǰԴ³ɳC |2, 41, 87|8.85945 +MACD|002667|ǰԴ³ɳC |5, 10, 9|15.94044 +MA|003119|ʱԴA |5, 5, 30|31.11 +MACD|003119|ʱԴA |2, 16, 5|30.97899 +MA|003243|ͶĦй QDII|19, 6, 22|20.5873 +MACD|003243|ͶĦй QDII|13, 14, 18|14.66119 +MA|310398|Ż300ֵָ Ʊָ|6, 5, 84|20.1868 +MACD|310398|Ż300ֵָ Ʊָ|11, 12, 15|19.01979 +MA|000634|ʢû |2, 28, 17|11.06087 +MACD|000634|ʢû |18, 19, 10|12.13345 +MA|000029|۲ |10, 5, 49|22.26549 +MACD|000029|۲ |4, 5, 25|22.26549 +MA|376510|Ͷ Ʊ|7, 7, 84|10.91174 +MACD|376510|Ͷ Ʊ|15, 10, 4|14.51515 +MA|118001|׷޾ѡ QDII|9, 7, 68|17.73667 +MACD|118001|׷޾ѡ QDII|3, 4, 4|16.97297 +MA|002121|㷢Ʊ Ʊ|5, 5, 20|22.18335 +MACD|002121|㷢Ʊ Ʊ|6, 4, 15|15.5304 +MA|002595|ʱҵ4.0Ʊ Ʊ|19, 10, 92|16.51665 +MACD|002595|ʱҵ4.0Ʊ Ʊ|8, 14, 14|15.78711 +MA|000988|ʵȫƱ QDII|19, 5, 79|5.25623 +MACD|000988|ʵȫƱ QDII|17, 4, 11|6.32999 +MA|001878|ʵѡƱ Ʊ|19, 6, 22|17.07622 +MACD|001878|ʵѡƱ Ʊ|10, 20, 28|13.01702 +MA|000835|ԪʱйA50ָ Ʊָ|5, 5, 16|23.74602 +MACD|000835|ԪʱйA50ָ Ʊָ|2, 26, 6|18.57745 +MA|160216|̩Ʒ QDII|5, 5, 22|26.06383 +MACD|160216|̩Ʒ QDII|5, 8, 19|26.73796 +MA|001236|ʱ˿·ƱA Ʊ|16, 5, 91|16.34311 +MACD|001236|ʱ˿·ƱA Ʊ|3, 16, 22|16.34311 +MA|002697|ŷƱC Ʊ|19, 6, 22|23.38723 +MACD|002697|ŷƱC Ʊ|3, 13, 21|21.53096 +MA|320010|ŵ֤100 Ʊָ|7, 5, 22|19.73492 +MACD|320010|ŵ֤100 Ʊָ|1, 8, 14|10.89744 +MA|160628|زּ Ʊָ|7, 7, 34|22.96029 +MACD|160628|زּ Ʊָ|3, 40, 24|20.03524 +MA|163406|ȫּ |4, 6, 62|15.65178 +MACD|163406|ȫּ |5, 6, 25|23.45624 +MA|002621|ŷƱA Ʊ|19, 6, 22|22.8198 +MACD|002621|ŷƱA Ʊ|2, 17, 24|21.07459 +MA|398061|кѻ |19, 16, 86|16.37537 +MACD|398061|кѻ |7, 11, 25|16.37537 +MA|001657|θȻ |16, 8, 70|25.35164 +MACD|001657|θȻ |17, 11, 18|17.23577 +MA|350005|й2025 |2, 50, 35|8.87772 +MACD|350005|й2025 |10, 14, 5|18.65999 +MA|002189|ũĸ |7, 7, 24|20.8751 +MACD|002189|ũĸ |3, 22, 15|11.69656 +MA|000127|ũҵȻ |10, 7, 39|20.25502 +MACD|000127|ũҵȻ |7, 25, 10|11.10177 +MA|161129|׷ԭA QDII|7, 7, 84|24.66741 +MACD|161129|׷ԭA QDII|5, 13, 26|24.42485 +MA|000167|㷢ûA |7, 7, 10|18.24881 +MACD|000167|㷢ûA |3, 9, 7|15.86179 +MA|519165|» |19, 6, 22|19.77556 +MACD|519165|» |4, 8, 29|19.77556 +MA|519702|ƻ |4, 45, 63|11.62672 +MACD|519702|ƻ |3, 7, 24|23.66049 +MA|000423|ǰԴ¼A |19, 6, 22|21.83107 +MACD|000423|ǰԴ¼A |5, 6, 8|21.16169 +MA|002124|㷢˲ҵ |19, 6, 22|22.08233 +MACD|002124|㷢˲ҵ |2, 26, 7|20.08566 +MA|160605|й50 |19, 7, 99|24.06592 +MACD|160605|й50 |4, 6, 8|20.00816 +MA|001892|ʢ˳ɳ |7, 7, 16|29.01588 +MACD|001892|ʢ˳ɳ |1, 39, 7|15.67735 +MA|001865|ǰԴ¼C |19, 6, 22|21.83232 +MACD|001865|ǰԴ¼C |3, 6, 11|21.20378 +MA|000020|˳ƷͶʻ |20, 43, 65|3.66622 +MACD|000020|˳ƷͶʻ |5, 11, 4|8.33023 +MA|002214|кֵѡ |19, 6, 22|8.74913 +MACD|002214|кֵѡ |7, 6, 10|7.77548 +MA|310358|¾û |18, 16, 65|2.1342 +MACD|310358|¾û |3, 20, 28|5.04307 +MA|002085|ʢ+ |7, 7, 16|29.2232 +MACD|002085|ʢ+ |1, 39, 8|15.99293 +MA|003940|ʢѡûϷʽ |17, 48, 91|5.21936 +MACD|003940|ʢѡûϷʽ |1, 18, 24|10.58112 +MA|004528|ʢʢͨծA none|1, 24, 10|27.2872 +MACD|004528|ʢʢͨծA none|1, 11, 9|27.08476 +MA|001581|ͨѡû |5, 5, 84|22.4601 +MACD|001581|ͨѡû |2, 11, 4|18.38473 +MA|002534|ȹծȯA ծȯ|9, 54, 57|0.0 +MACD|002534|ȹծȯA ծȯ|20, 9, 21|0.44229 +MA|000529|㷢ƻ |19, 6, 22|16.7311 +MACD|000529|㷢ƻ |14, 16, 9|14.91349 +MA|004374|̩˼ϷA |9, 7, 13|5.91029 +MACD|004374|̩˼ϷA |16, 4, 12|6.33173 +MA|004375|̩˼ϷC |9, 7, 13|5.81459 +MACD|004375|̩˼ϷC |17, 4, 12|6.36353 +MA|001681|»ֵû |7, 7, 62|23.98046 +MACD|001681|»ֵû |6, 7, 24|23.98046 +MA|004183|ҵ none|2, 10, 76|13.88915 +MACD|004183|ҵ none|6, 11, 25|21.56473 +MA|163411|ȫѡ |2, 55, 41|8.92616 +MACD|163411|ȫѡ |4, 5, 24|26.83414 +MA|001458|㷢֤ȫָҪA ӻ|16, 7, 91|19.30764 +MACD|001458|㷢֤ȫָҪA ӻ|2, 9, 24|15.97156 +MA|002976|㷢֤ȫָҪC ӻ|7, 7, 91|19.20761 +MACD|002976|㷢֤ȫָҪC ӻ|2, 15, 15|15.88894 +MA|163805|Ի |4, 6, 70|11.06403 +MACD|163805|Ի |3, 9, 8|6.54591 +MA|001528|ŵȽƱ Ʊ|7, 5, 22|23.68543 +MACD|001528|ŵȽƱ Ʊ|4, 8, 7|14.17792 +MA|003154|» |1, 57, 46|10.1251 +MACD|003154|» |2, 15, 28|19.22268 +MA|004357|Ϸǻۻ |2, 28, 39|9.35951 +MACD|004357|Ϸǻۻ |3, 11, 24|21.93866 +MA|001663|+Ʊ Ʊ|19, 5, 49|16.18394 +MACD|001663|+Ʊ Ʊ|19, 22, 26|16.64047 +MA|501018|Ϸԭ QDII|1, 48, 31|7.85182 +MACD|501018|Ϸԭ QDII|1, 13, 5|18.78048 +MA|001163|й30Ʊ Ʊ|19, 6, 22|20.77456 +MACD|001163|й30Ʊ Ʊ|2, 12, 24|18.76952 +MA|002989|ͨͨǬоѡ |1, 12, 13|13.45044 +MACD|002989|ͨͨǬоѡ |7, 4, 15|12.71702 +MA|001410|ŴԴҵƱ Ʊ|20, 9, 86|0.0 +MACD|001410|ŴԴҵƱ Ʊ|13, 10, 6|1.686 +MA|164508|֤100ָǿּ Ʊָ|7, 7, 84|19.19002 +MACD|164508|֤100ָǿּ Ʊָ|2, 31, 9|14.09542 +MA|000532|˳ҵ |19, 7, 91|23.48627 +MACD|000532|˳ҵ |3, 9, 5|21.16945 +MA|519732|֧˫Ϣƽ |5, 54, 42|6.54417 +MACD|519732|֧˫Ϣƽ |2, 8, 9|16.44217 +MA|002142|ʱû |6, 38, 13|3.15156 +MACD|002142|ʱû |3, 6, 4|3.59569 +MA|206002|ѡɳ |19, 6, 22|18.42954 +MACD|206002|ѡɳ |1, 38, 14|11.22717 +MA|001186|彡Ʊ Ʊ|3, 50, 43|5.65349 +MACD|001186|彡Ʊ Ʊ|14, 17, 26|13.47116 +MA|100026|Ƚѡ |1, 5, 34|3.74049 +MACD|100026|Ƚѡ |4, 8, 9|8.53868 +MA|001047|ĸƱ Ʊ|13, 8, 24|18.84208 +MACD|001047|ĸƱ Ʊ|2, 11, 28|18.01393 +MA|519196|û |2, 15, 10|9.83887 +MACD|519196|û |17, 12, 26|11.11495 +MA|519712|Ļ |3, 48, 12|9.07886 +MACD|519712|Ļ |4, 5, 18|11.91849 +MA|519035|첩 |19, 5, 79|17.46464 +MACD|519035|첩 |4, 17, 7|15.38408 +MA|003597|ʢʢڻC |19, 6, 22|16.63343 +MACD|003597|ʢʢڻC |1, 8, 26|10.61786 +MA|000001|ָ֤|9, 7, 33|13.30042 +MACD|000001|ָ֤|5, 42, 9|10.53893 +MA|000002|ָ|9, 7, 33|13.33075 +MACD|000002|ָ|5, 42, 9|10.55361 +MA|000003|¹ָ|8, 4, 16|7.82504 +MACD|000003|¹ָ|6, 35, 18|6.28743 +MA|000004|ҵָ|9, 7, 33|12.08445 +MACD|000004|ҵָ|2, 34, 6|11.47795 +MA|000005|ҵָ|9, 17, 21|3.35852 +MACD|000005|ҵָ|1, 8, 30|3.40761 +MA|000006|زָ|7, 6, 56|27.74703 +MACD|000006|زָ|9, 22, 8|27.25603 +MA|000007|ָ|1, 17, 26|2.8486 +MACD|000007|ָ|19, 13, 6|4.90651 +MA|000008|ۺָ|8, 5, 36|21.57696 +MACD|000008|ۺָ|3, 22, 15|20.34097 +MA|000009|֤380|9, 8, 22|7.19836 +MACD|000009|֤380|18, 11, 4|8.81905 +MA|000010|֤180|8, 6, 21|25.32315 +MACD|000010|֤180|2, 34, 30|24.36277 +MA|000011|ָ|9, 8, 40|11.25696 +MACD|000011|ָ|19, 27, 9|11.07025 +MA|000012|ծָ|9, 6, 46|0.92664 +MACD|000012|ծָ|7, 24, 10|0.7274 +MA|000013|ծָ|1, 20, 20|2.13239 +MACD|000013|ծָ|3, 22, 11|2.07276 +MA|000015|ָ|9, 6, 59|20.70243 +MACD|000015|ָ|7, 27, 4|20.6211 +MA|000016|֤50|5, 4, 22|34.11237 +MACD|000016|֤50|3, 42, 5|33.15233 +MA|000017|ָ|9, 6, 32|13.33326 +MACD|000017|ָ|5, 42, 9|10.55348 +MA|000018|180|10, 7, 48|32.38308 +MACD|000018|180|3, 38, 28|25.13646 +MA|000019|ָ|4, 4, 51|18.05163 +MACD|000019|ָ|19, 37, 30|18.28338 +MA|000021|180|9, 8, 51|26.01608 +MACD|000021|180|3, 50, 27|22.19288 +MA|000022|˾ծ|1, 10, 21|1.75054 +MACD|000022|˾ծ|1, 25, 4|1.73097 +MA|000025|180|5, 4, 34|5.54666 +MACD|000025|180|15, 22, 28|4.58516 +MA|000026|180Դ|4, 4, 22|28.92227 +MACD|000026|180Դ|16, 5, 30|19.46262 +MA|000027|180|4, 4, 49|32.85546 +MACD|000027|180|2, 31, 5|32.27131 +MA|000028|180ɳ|5, 4, 41|33.78924 +MACD|000028|180ɳ|1, 44, 4|35.79002 +MA|000030|180Rɳ|4, 4, 57|28.54271 +MACD|000030|180Rɳ|4, 34, 4|28.2352 +MA|000031|180Rֵ|5, 5, 27|23.22914 +MACD|000031|180Rֵ|1, 45, 25|21.05657 +MA|000032|֤Դ|4, 4, 34|13.21472 +MACD|000032|֤Դ|5, 11, 12|17.33865 +MA|000033|֤|5, 4, 34|24.85447 +MACD|000033|֤|16, 6, 19|13.40617 +MA|000034|֤ҵ|4, 4, 41|5.42947 +MACD|000034|֤ҵ|7, 20, 10|4.90636 +MA|000035|֤ѡ|5, 4, 41|8.66692 +MACD|000035|֤ѡ|19, 20, 27|7.69272 +MA|000036|֤|4, 4, 25|45.29508 +MACD|000036|֤|8, 21, 18|44.11303 +MA|000037|֤ҽҩ|5, 4, 41|19.43095 +MACD|000037|֤ҽҩ|7, 15, 29|16.77715 +MA|000038|֤|4, 4, 20|35.69207 +MACD|000038|֤|1, 38, 27|28.75442 +MA|000039|֤Ϣ|2, 8, 42|9.08734 +MACD|000039|֤Ϣ|3, 12, 4|8.22474 +MA|000040|֤|4, 4, 41|10.94645 +MACD|000040|֤|13, 30, 10|11.98957 +MA|000041|֤|5, 4, 57|1.17984 +MACD|000041|֤|10, 23, 7|0.0 +MA|000042|֤|4, 4, 41|24.06396 +MACD|000042|֤|15, 25, 11|20.54074 +MA|000043||4, 4, 31|26.06822 +MACD|000043||15, 28, 11|20.89461 +MA|000044|֤|4, 4, 49|12.20186 +MACD|000044|֤|14, 29, 9|11.95993 +MA|000045|֤С|5, 5, 41|5.57018 +MACD|000045|֤С|12, 22, 8|-0.09573 +MA|000046|֤С|4, 4, 26|5.40812 +MACD|000046|֤С|15, 29, 7|6.43986 +MA|000047|֤ȫָ|4, 4, 59|18.24753 +MACD|000047|֤ȫָ|15, 30, 11|17.72711 +MA|000048|ָ|4, 4, 35|28.85245 +MACD|000048|ָ|15, 30, 11|21.7464 +MA|000049|֤|4, 4, 38|23.661 +MACD|000049|֤|12, 25, 8|21.67459 +MA|000050|50Ȩ|4, 4, 41|18.17815 +MACD|000050|50Ȩ|15, 29, 11|15.96531 +MA|000051|180Ȩ|4, 4, 49|7.72909 +MACD|000051|180Ȩ|13, 29, 7|7.67015 +MA|000052|50|4, 4, 41|30.10156 +MACD|000052|50|10, 29, 8|22.09457 +MA|000053|180|4, 4, 41|25.12024 +MACD|000053|180|15, 30, 10|19.67431 +MA|399001|ָ֤|4, 4, 51|14.09072 +MACD|399001|ָ֤|11, 23, 7|9.38056 +MA|399002|ָR|4, 4, 54|14.15682 +MACD|399002|ָR|11, 23, 7|10.65939 +MA|399003|ɷݣָ|4, 4, 60|11.52215 +MACD|399003|ɷݣָ|15, 30, 11|10.09048 +MA|399004|֤100R|4, 4, 28|32.57285 +MACD|399004|֤100R|11, 22, 8|30.31821 +MA|399005|Сָ|4, 4, 35|15.31913 +MACD|399005|Сָ|13, 29, 9|10.21135 +MA|399006|ҵָ|4, 9, 37|0.32931 +MACD|399006|ҵָ|14, 24, 9|-3.47639 +MA|399007|֤300|4, 4, 31|17.80712 +MACD|399007|֤300|12, 22, 8|14.80193 +MA|399008|С300|4, 4, 46|8.0507 +MACD|399008|С300|14, 30, 7|4.17058 +MA|399009|֤200|1, 14, 27|0.99248 +MACD|399009|֤200|10, 25, 7|1.0173 +MA|399010|֤700|4, 8, 43|4.80871 +MACD|399010|֤700|15, 26, 11|0.0 +MA|399011|֤1000|4, 4, 56|2.00941 +MACD|399011|֤1000|15, 29, 10|6.30782 +MA|399012|ҵ300|2, 9, 42|0.07077 +MACD|399012|ҵ300|12, 30, 9|-3.4689 +MA|399013|оѡ|4, 4, 48|11.88163 +MACD|399013|оѡ|11, 22, 8|6.86013 +MA|399015|С|5, 7, 51|3.72382 +MACD|399015|С|10, 26, 9|-4.363 +MA|399016|֤|5, 15, 57|8.56922 +MACD|399016|֤|14, 28, 11|-4.28074 +MA|399017|SME|1, 14, 26|-0.02696 +MACD|399017|SME|13, 30, 11|-3.96341 +MA|399018|ҵ|1, 5, 59|1.07089 +MACD|399018|ҵ|15, 26, 11|0.0 +MA|399100|ָ|4, 14, 20|2.18547 +MACD|399100|ָ|11, 25, 11|5.12664 +MA|399101|С|1, 14, 27|-0.00375 +MACD|399101|С|15, 25, 11|1.40686 +MA|399102|ҵ|1, 4, 56|0.62591 +MACD|399102|ҵ|15, 29, 7|-3.28502 +MA|399103|ָָ|4, 4, 56|5.22897 +MACD|399103|ָָ|15, 26, 11|6.28425 +MA|399106|ָ֤|1, 13, 27|1.17366 +MACD|399106|ָ֤|10, 24, 7|3.74081 +MA|399107|ָ֤|2, 10, 29|1.17478 +MACD|399107|ָ֤|11, 22, 7|3.74137 +MA|399108|ָ֤|4, 4, 20|6.92561 +MACD|399108|ָ֤|11, 24, 11|5.8901 +MA|399231|ũָ|3, 14, 59|5.31592 +MACD|399231|ũָ|15, 30, 7|7.46542 +MA|399232|ɿָ|5, 4, 22|1.919 +MACD|399232|ɿָ|15, 30, 11|0.25213 +MA|399233|ָ|4, 11, 44|-0.14366 +MACD|399233|ָ|12, 22, 8|3.7001 +MA|399234|ˮָ|1, 5, 51|0.11406 +MACD|399234|ˮָ|10, 22, 9|-1.23743 +MA|399235|ָ|1, 5, 34|-0.00972 +MACD|399235|ָ|15, 30, 9|-7.84206 +MA|399236|ָ|5, 4, 27|0.27941 +MACD|399236|ָ|10, 24, 7|-0.02782 +MA|399237|ָ|1, 13, 27|1.44686 +MACD|399237|ָ|10, 22, 7|-8.33941 +MA|399238|ָ|1, 4, 34|1.07949 +MACD|399238|ָ|15, 27, 8|0.0 +MA|399239|ITָ|5, 9, 46|5.78768 +MACD|399239|ITָ|15, 26, 11|0.0 +MA|399240|ָ|1, 5, 20|7.15715 +MACD|399240|ָ|10, 23, 7|12.88254 +MA|399241|زָ|4, 4, 41|25.65285 +MACD|399241|زָ|15, 22, 9|24.85262 +MA|399242|ָ|1, 5, 31|4.09354 +MACD|399242|ָ|11, 24, 8|-8.7289 +MA|399243|ָ|4, 11, 29|22.65028 +MACD|399243|ָ|15, 29, 7|-8.68601 +MA|399244|ָ|4, 4, 41|2.72283 +MACD|399244|ָ|15, 24, 8|-1.9225 +MA|399248|Ļָ|2, 9, 45|4.41103 +MACD|399248|Ļָ|10, 24, 10|-4.64267 +MA|399249|ָ|1, 14, 27|-0.85169 +MACD|399249|ָ|15, 26, 8|0.0 +MA|399298|и|5, 14, 20|2.29562 +MACD|399298|и|10, 22, 7|1.1874 +MA|399299|е|4, 4, 41|2.77751 +MACD|399299|е|15, 26, 8|2.66597 +MA|399300|300|4, 4, 26|27.10965 +MACD|399300|300|14, 30, 7|26.35064 +MA|399301|ծ|3, 15, 20|2.55727 +MACD|399301|ծ|10, 26, 8|1.23422 +MA|399302|˾ծ|4, 14, 20|2.562 +MACD|399302|˾ծ|10, 22, 7|1.29736 +MA|399303|֤2000|5, 7, 44|3.89036 +MACD|399303|֤2000|10, 25, 7|0.95384 +MA|399306|֤ETF|4, 4, 28|20.96965 +MACD|399306|֤ETF|10, 24, 11|19.08031 +MA|399307|֤תծ|5, 4, 21|3.09106 +MACD|399307|֤תծ|12, 26, 9|2.15586 +MA|399310|֤50|4, 4, 41|42.71805 +MACD|399310|֤50|15, 26, 7|41.98073 +MA|001668|ȫ QDII|4, 4, 41|14.7401 +MACD|001668|ȫ QDII|15, 27, 10|8.03506 +MA|001659|¶ |4, 4, 41|18.09751 +MACD|001659|¶ |15, 30, 9|13.6374 +MA|100020|ֵ |1, 14, 57|2.89264 +MACD|100020|ֵ |10, 22, 7|10.62599 +MA|001714|ҵƱ Ʊ|2, 15, 43|9.38761 +MACD|001714|ҵƱ Ʊ|10, 22, 8|6.85927 +MA|519642| |4, 4, 41|17.48747 +MACD|519642| |10, 29, 7|10.0469 +MA|004263|û none|4, 4, 41|11.41803 +MACD|004263|û none|12, 26, 8|11.41803 +MA|004505|ʱ |4, 4, 41|18.40781 +MACD|004505|ʱ |13, 22, 7|12.73935 +MA|160218|̩֤زҵָּ Ʊָ|4, 4, 54|18.25133 +MACD|160218|̩֤زҵָּ Ʊָ|15, 29, 10|16.51388 diff --git a/stockdata/GAOpt100.txt b/stockdata/GAOpt100.txt new file mode 100644 index 0000000..55410a2 --- /dev/null +++ b/stockdata/GAOpt100.txt @@ -0,0 +1,453 @@ + +MACD|000018|180|15, 26, 8|22.30936 +MA|000019|ָ|6, 6, 29|17.80594 +MACD|000019|ָ|15, 30, 10|9.22657 +MA|000020|ָ|6, 14, 48|1.25486 +MACD|000020|ָ|10, 27, 8|-0.86807 +MA|000021|180|6, 6, 29|25.53164 +MACD|000021|180|15, 29, 10|20.75419 +MA|000022|˾ծ|2, 16, 23|1.73686 +MACD|000022|˾ծ|12, 22, 8|1.24243 +MA|000025|180|3, 9, 22|4.04991 +MACD|000025|180|12, 27, 7|1.22472 +MA|000026|180Դ|2, 13, 24|12.49502 +MACD|000026|180Դ|15, 30, 9|1.00286 +MA|000027|180|6, 18, 57|9.41673 +MACD|000027|180|11, 29, 7|31.92389 +MA|000028|180ɳ|5, 14, 35|11.03262 +MACD|000028|180ɳ|15, 29, 10|27.14122 +MA|000029|180ֵ|3, 12, 41|11.53913 +MACD|000029|180ֵ|14, 27, 8|21.95246 +MA|000030|180Rɳ|6, 15, 51|18.93202 +MACD|000030|180Rɳ|10, 28, 7|25.13673 +MA|000031|180Rֵ|3, 16, 55|9.67706 +MACD|000031|180Rֵ|15, 29, 10|19.60996 +MA|000032|֤Դ|2, 12, 21|12.48738 +MACD|000032|֤Դ|14, 26, 8|1.45369 +MA|000033|֤|3, 16, 21|9.19966 +MACD|000033|֤|15, 30, 8|0.76006 +MA|000034|֤ҵ|6, 12, 34|3.42715 +MACD|000034|֤ҵ|10, 22, 7|4.90636 +MA|000001|ָ֤|2, 16, 27|6.25213 +MACD|000001|ָ֤|15, 26, 8|8.8204 +MA|000002|ָ|2, 16, 27|6.27792 +MACD|000002|ָ|15, 26, 8|8.87341 +MA|000003|¹ָ|4, 13, 20|2.15388 +MACD|000003|¹ָ|15, 30, 10|6.11969 +MA|000004|ҵָ|2, 16, 23|5.54915 +MACD|000004|ҵָ|14, 26, 11|8.97696 +MA|000005|ҵָ|5, 12, 21|3.16432 +MACD|000005|ҵָ|15, 29, 11|0.0 +MA|000006|زָ|4, 12, 55|20.81004 +MACD|000006|زָ|15, 24, 9|20.01122 +MA|000007|ָ|2, 11, 25|2.8486 +MACD|000007|ָ|15, 29, 11|0.0 +MA|000008|ۺָ|5, 12, 41|10.04623 +MACD|000008|ۺָ|11, 26, 7|19.37983 +MA|000009|֤380|2, 7, 34|1.09071 +MACD|000009|֤380|13, 23, 11|4.62444 +MA|000010|֤180|2, 7, 56|9.42135 +MACD|000010|֤180|15, 29, 9|21.99683 +MA|000011|ָ|2, 9, 59|4.23744 +MACD|000011|ָ|14, 29, 10|11.07025 +MA|000012|ծָ|2, 16, 34|0.16567 +MACD|000012|ծָ|10, 24, 7|0.7274 +MA|000013|ծָ|2, 11, 27|2.08789 +MACD|000013|ծָ|12, 30, 10|2.06593 +MA|000015|ָ|3, 14, 50|10.0989 +MACD|000015|ָ|10, 24, 7|20.23897 +MA|000016|֤50|6, 10, 51|10.61882 +MACD|000016|֤50|15, 30, 10|25.42533 +MA|000017|ָ|2, 16, 27|6.27116 +MACD|000017|ָ|15, 26, 8|8.87827 +MA|000035|֤ѡ|3, 16, 20|4.18617 +MACD|000035|֤ѡ|10, 27, 7|7.19024 +MA|000036|֤|6, 12, 57|13.6696 +MACD|000036|֤|12, 22, 8|44.11303 +MA|000037|֤ҽҩ|5, 14, 43|2.65155 +MACD|000037|֤ҽҩ|15, 23, 7|16.77715 +MA|000038|֤|2, 17, 57|12.44444 +MACD|000038|֤|12, 30, 7|25.96199 +MA|000039|֤Ϣ|3, 7, 28|9.08734 +MACD|000039|֤Ϣ|14, 28, 11|-3.40234 +MA|000040|֤|6, 18, 27|17.68656 +MACD|000040|֤|13, 30, 10|11.98957 +MA|000041|֤|2, 14, 23|0.54543 +MACD|000041|֤|10, 23, 7|0.0 +MA|000042|֤|6, 18, 60|11.32065 +MACD|000042|֤|15, 25, 11|20.54074 +MA|000043||2, 7, 47|11.51203 +MACD|000043||15, 28, 11|20.89461 +MA|000044|֤|3, 17, 21|5.40083 +MACD|000044|֤|14, 29, 9|11.95993 +MA|000045|֤С|2, 15, 35|0.53516 +MACD|000045|֤С|12, 22, 8|-0.09573 +MA|000046|֤С|4, 18, 24|3.22797 +MACD|000046|֤С|14, 28, 8|6.43986 +MA|000047|֤ȫָ|5, 18, 48|7.11406 +MACD|000047|֤ȫָ|15, 31, 11|17.72711 +MA|000048|ָ|4, 17, 51|10.36821 +MACD|000048|ָ|15, 31, 12|22.32677 +MA|000049|֤|3, 11, 42|6.63345 +MACD|000049|֤|11, 25, 8|21.67459 +MA|000050|50Ȩ|3, 9, 34|8.93702 +MACD|000050|50Ȩ|15, 30, 12|16.57635 +MA|000051|180Ȩ|2, 12, 21|5.77748 +MACD|000051|180Ȩ|14, 31, 7|7.67015 +MA|000052|50|2, 9, 57|12.38291 +MACD|000052|50|11, 30, 8|22.09457 +MA|000053|180|4, 14, 56|10.46637 +MACD|000053|180|15, 26, 7|19.67431 +MA|399001|ָ֤|2, 18, 27|3.3799 +MACD|399001|ָ֤|15, 27, 12|9.46976 +MA|399002|ָR|2, 18, 27|3.35718 +MACD|399002|ָR|11, 22, 8|10.65939 +MA|399003|ɷݣָ|2, 12, 21|6.84482 +MACD|399003|ɷݣָ|9, 29, 7|11.52644 +MA|399004|֤100R|2, 16, 42|7.81221 +MACD|399004|֤100R|9, 21, 8|30.8456 +MA|399005|Сָ|2, 13, 56|-0.12722 +MACD|399005|Сָ|10, 28, 12|10.52294 +MA|399006|ҵָ|4, 12, 34|0.32931 +MACD|399006|ҵָ|10, 26, 12|-3.47639 +MA|399007|֤300|5, 14, 51|4.03324 +MACD|399007|֤300|9, 23, 6|14.80193 +MA|399008|С300|2, 13, 21|-0.28394 +MACD|399008|С300|15, 28, 6|4.17058 +MA|399009|֤200|4, 14, 45|0.08615 +MACD|399009|֤200|9, 21, 6|2.5679 +MA|399010|֤700|4, 8, 43|4.80871 +MACD|399010|֤700|14, 30, 11|0.0 +MA|399011|֤1000|4, 18, 37|1.64847 +MACD|399011|֤1000|15, 26, 10|6.30782 +MA|399012|ҵ300|4, 12, 27|0.07077 +MACD|399012|ҵ300|15, 29, 8|-3.4689 +MA|399013|оѡ|2, 18, 27|2.78964 +MACD|399013|оѡ|9, 25, 6|7.29704 +MA|399015|С|5, 7, 46|3.72382 +MACD|399015|С|11, 21, 6|0.79099 +MA|399016|֤|5, 15, 41|8.56922 +MACD|399016|֤|14, 30, 10|-4.25027 +MA|399017|SME|2, 6, 20|-0.14092 +MACD|399017|SME|9, 24, 6|2.73145 +MA|399018|ҵ|4, 9, 20|0.0 +MACD|399018|ҵ|15, 24, 11|0.0 +MA|399100|ָ|2, 19, 20|2.18547 +MACD|399100|ָ|14, 24, 9|5.12664 +MA|399101|С|5, 5, 46|3.58904 +MACD|399101|С|15, 25, 12|1.40686 +MA|399102|ҵ|5, 9, 29|0.0 +MACD|399102|ҵ|13, 30, 8|-3.28502 +MA|399103|ָָ|5, 5, 28|5.22897 +MACD|399103|ָָ|15, 25, 10|6.28425 +MA|399106|ָ֤|2, 9, 31|1.17366 +MACD|399106|ָ֤|9, 24, 8|3.74081 +MA|399107|ָ֤|2, 9, 31|1.17478 +MACD|399107|ָ֤|9, 24, 8|3.74137 +MA|399108|ָ֤|5, 5, 54|9.53845 +MACD|399108|ָ֤|13, 22, 9|5.98615 +MA|399231|ũָ|3, 14, 58|5.31592 +MACD|399231|ũָ|15, 30, 7|7.46542 +MA|399232|ɿָ|5, 9, 44|0.29197 +MACD|399232|ɿָ|15, 31, 10|0.25213 +MA|399233|ָ|5, 5, 42|6.0521 +MACD|399233|ָ|9, 24, 11|3.7001 +MA|399234|ˮָ|2, 5, 36|-0.30361 +MACD|399234|ˮָ|11, 23, 8|-1.23743 +MA|399235|ָ|5, 6, 56|1.35454 +MACD|399235|ָ|14, 28, 10|-7.84206 +MA|399236|ָ|5, 9, 48|-0.03059 +MACD|399236|ָ|9, 23, 8|-0.02782 +MA|399237|ָ|2, 9, 21|1.44686 +MACD|399237|ָ|15, 30, 12|-5.49396 +MA|399238|ָ|4, 6, 37|0.2954 +MACD|399238|ָ|14, 25, 8|0.0 +MA|399239|ITָ|5, 9, 45|5.78768 +MACD|399239|ITָ|15, 25, 12|0.0 +MA|399240|ָ|2, 9, 21|6.85854 +MACD|399240|ָ|9, 30, 6|12.88254 +MA|399241|زָ|5, 5, 51|26.19015 +MACD|399241|زָ|12, 24, 12|24.85262 +MA|399242|ָ|2, 9, 21|4.09354 +MACD|399242|ָ|9, 21, 6|2.62408 +MA|399243|ָ|4, 11, 28|22.65028 +MACD|399243|ָ|15, 31, 7|-7.98793 +MA|399244|ָ|5, 5, 28|2.72283 +MACD|399244|ָ|14, 22, 9|-1.9225 +MA|399248|Ļָ|3, 9, 30|4.41103 +MACD|399248|Ļָ|15, 25, 12|0.0 +MA|399249|ָ|2, 9, 21|-0.85169 +MACD|399249|ָ|11, 24, 7|0.0 +MA|399298|и|4, 18, 21|2.29562 +MACD|399298|и|9, 22, 6|1.49463 +MA|399299|е|5, 5, 28|2.76877 +MACD|399299|е|15, 24, 8|2.66597 +MA|399300|300|5, 5, 21|27.10965 +MACD|399300|300|14, 31, 10|26.35064 +MA|399301|ծ|2, 19, 24|2.55727 +MACD|399301|ծ|9, 24, 6|1.5308 +MA|399302|˾ծ|2, 16, 21|2.56587 +MACD|399302|˾ծ|9, 22, 6|1.76591 +MA|399303|֤2000|5, 7, 51|3.89036 +MACD|399303|֤2000|9, 29, 7|1.09386 +MA|399306|֤ETF|5, 5, 24|20.96965 +MACD|399306|֤ETF|9, 24, 8|19.08031 +MA|399307|֤תծ|4, 9, 20|2.22685 +MACD|399307|֤תծ|15, 25, 6|2.15586 +MA|399310|֤50|5, 5, 28|42.35191 +MACD|399310|֤50|14, 29, 6|41.98073 +MA|002963|egold|3, 18, 20|2.00703 +MACD|002963|egold|13, 29, 11|0.03044 +MA|003321|eoil|2, 9, 32|3.20248 +MACD|003321|eoil|15, 30, 10|18.47168 +MA|004744|eGEI|3, 9, 56|0.31462 +MACD|004744|eGEI|11, 31, 11|0.0 +MA|110003|eSSE50|5, 5, 28|22.44016 +MACD|110003|eSSE50|9, 22, 6|8.64163 +MA|110020|HS300|5, 5, 28|12.92021 +MACD|110020|HS300|14, 24, 7|0.0 +MA|110031|eHSI|5, 5, 28|17.44001 +MACD|110031|eHSI|15, 31, 10|11.74248 +MA|161130|eNASDAQ100|5, 5, 28|11.6675 +MACD|161130|eNASDAQ100|15, 24, 10|6.65961 +MA|110028|anxinB|5, 5, 24|4.65936 +MACD|110028|anxinB|15, 31, 12|2.72238 +MA|110022|eConsumption |5, 5, 28|32.07768 +MACD|110022|eConsumption |13, 30, 6|14.20697 +MA|161125|SPX500|5, 5, 28|11.17264 +MACD|161125|SPX500|9, 24, 6|5.88507 +MA|161725|֤׾ָּ Ʊָ|3, 17, 20|12.66606 +MACD|161725|֤׾ָּ Ʊָ|9, 24, 6|7.20346 +MA|160222|̩֤ʳƷҵָּ Ʊָ|5, 5, 28|29.16321 +MACD|160222|̩֤ʳƷҵָּ Ʊָ|9, 25, 9|10.29204 +MA|000248|֤ҪETF ӻ|5, 5, 28|31.82672 +MACD|000248|֤ҪETF ӻ|9, 24, 9|10.68504 +MA|162605|˳Ƕ(LOF) |5, 5, 28|30.03619 +MACD|162605|˳Ƕ(LOF) |15, 30, 11|8.03759 +MA|260108|˳˳ɳ |5, 5, 28|18.95496 +MACD|260108|˳˳ɳ |10, 22, 10|0.0 +MA|001740|й2025 |2, 9, 46|22.36529 +MACD|001740|й2025 |15, 24, 12|21.17808 +MA|160632|Ʒּ Ʊָ|5, 5, 28|23.81611 +MACD|160632|Ʒּ Ʊָ|9, 24, 6|10.75252 +MA|000083|ҵ |5, 5, 28|31.11461 +MACD|000083|ҵ |15, 25, 12|12.60671 +MA|001542|̩+Ʊ Ʊ|5, 5, 28|17.50802 +MACD|001542|̩+Ʊ Ʊ|15, 31, 12|10.18592 +MA|000619|ҵ |4, 9, 57|12.92338 +MACD|000619|ҵ |15, 25, 12|11.01842 +MA|070032|ʵŻ |5, 5, 28|26.68715 +MACD|070032|ʵŻ |13, 30, 12|12.19877 +MA|180012|ԣ |5, 5, 57|21.21962 +MACD|180012|ԣ |10, 24, 8|4.87535 +MA|481012|֤ ӻ|5, 5, 28|28.52387 +MACD|481012|֤ ӻ|9, 24, 6|20.15778 +MA|519664|A |4, 9, 41|13.87287 +MACD|519664|A |9, 24, 6|11.42729 +MA|001112|йƻ |5, 5, 28|23.53577 +MACD|001112|йƻ |9, 24, 8|6.71455 +MA|519665|C |4, 9, 41|13.73585 +MACD|519665|C |9, 24, 6|11.32039 +MA|001387|¾ûA |2, 9, 36|0.86907 +MACD|001387|¾ûA |15, 29, 12|0.0 +MA|310388| |5, 17, 33|1.94631 +MACD|310388| |15, 29, 12|1.15204 +MA|001076|׷ĸ |3, 11, 36|12.45559 +MACD|001076|׷ĸ |15, 30, 10|10.94844 +MA|206007|ѡ |4, 11, 35|11.48935 +MACD|206007|ѡ |15, 31, 12|11.34128 +MA|260116|˳ǺľA |5, 5, 28|27.74449 +MACD|260116|˳ǺľA |13, 30, 9|14.24889 +MA|000462|ũֶ |3, 11, 57|13.72375 +MACD|000462|ũֶ |15, 24, 12|11.32905 +MA|000742|̩¾û |5, 5, 28|14.26838 +MACD|000742|̩¾û |14, 24, 8|4.66296 +MA|001606|ũҵ4.0 |5, 5, 28|22.71381 +MACD|001606|ũҵ4.0 |13, 27, 10|8.94718 +MA|550016|ųԶC ּܸ|5, 5, 28|46.42819 +MACD|550016|ųԶC ּܸ|13, 24, 8|40.9415 +MA|001208|ŵ̼ùƱ Ʊ|5, 5, 28|26.22243 +MACD|001208|ŵ̼ùƱ Ʊ|9, 25, 7|10.86635 +MA|519772|û |5, 5, 33|7.32574 +MACD|519772|û |9, 24, 8|13.37339 +MA|260110|˳Ǿѡ |2, 9, 54|2.97532 +MACD|260110|˳Ǿѡ |15, 24, 7|-1.82216 +MA|000854|ϲҵ Ʊ|5, 11, 35|11.23595 +MACD|000854|ϲҵ Ʊ|9, 29, 6|18.5255 +MA|660010|ũԾѡ |5, 9, 58|9.29739 +MACD|660010|ũԾѡ |9, 24, 6|11.16404 +MA|165312|50 Ʊָ|2, 9, 20|2.80028 +MACD|165312|50 Ʊָ|14, 24, 8|0.0 +MA|040008|ѡ |5, 5, 45|17.21688 +MACD|040008|ѡ |9, 24, 6|7.42604 +MA|360007|󱣵û |2, 18, 21|20.28213 +MACD|360007|󱣵û |9, 24, 6|15.66665 +MA|169101| |2, 14, 57|9.25694 +MACD|169101| |14, 24, 8|7.90809 +MA|001631|֤ʳƷָA Ʊָ|5, 5, 28|22.53268 +MACD|001631|֤ʳƷָA Ʊָ|9, 21, 6|10.18305 +MA|001281|ѡA |5, 5, 21|23.91732 +MACD|001281|ѡA |14, 24, 6|10.05931 +MA|001287|A |5, 5, 28|21.04136 +MACD|001287|A |9, 24, 8|9.37687 +MA|000431|Ʒƴл |5, 11, 35|11.3846 +MACD|000431|Ʒƴл |15, 31, 12|11.07392 +MA|002072|ѡC |5, 5, 21|23.86419 +MACD|002072|ѡC |14, 21, 7|10.05757 +MA|519690|ȽûA |4, 7, 43|1.80661 +MACD|519690|ȽûA |11, 31, 11|-5.47862 +MA|001632|֤ʳƷָC Ʊָ|5, 5, 28|22.42101 +MACD|001632|֤ʳƷָC Ʊָ|9, 21, 6|10.11126 +MA|001170|̩˻ |5, 9, 43|10.9261 +MACD|001170|̩˻ |9, 24, 8|10.49067 +MA|519066|Ƚ |5, 5, 28|14.34914 +MACD|519066|Ƚ |9, 24, 6|0.94332 +MA|540009|Ѻ Ʊ|5, 5, 28|10.10499 +MACD|540009|Ѻ Ʊ|14, 24, 6|8.39297 +MA|110011|׷С̻ |5, 5, 28|23.0047 +MACD|110011|׷С̻ |15, 31, 11|9.72689 +MA|540012|źͷָA Ʊָ|5, 5, 32|14.3273 +MACD|540012|źͷָA Ʊָ|15, 24, 7|11.49186 +MA|530015|֤60 ӻ|5, 5, 28|23.85793 +MACD|530015|֤60 ӻ|9, 22, 6|17.86439 +MA|217027|Ӳƾ50ָA Ʊָ|5, 5, 21|20.89197 +MACD|217027|Ӳƾ50ָA Ʊָ|9, 21, 6|10.23026 +MA|001044|ʵѹƱ Ʊ|5, 5, 21|19.57668 +MACD|001044|ʵѹƱ Ʊ|9, 24, 6|6.10205 +MA|519068|ɳ |5, 5, 28|23.91683 +MA|000018|180|5, 5, 21|30.76524 +MACD|519068|ɳ |13, 34, 12|7.29549 +MA|270050|㷢¾û |3, 8, 50|3.38248 +MACD|270050|㷢¾û |3, 26, 8|6.71152 +MA|270041|㷢Ʒѡ |5, 9, 43|6.3973 +MACD|270041|㷢Ʒѡ |3, 38, 19|5.65656 +MA|502056|㷢ҽָּ Ʊָ|4, 18, 21|1.30692 +MACD|502056|㷢ҽָּ Ʊָ|3, 33, 7|1.19178 +MA|270042|㷢˹100ָ QDII-ָ|5, 5, 28|12.66666 +MACD|270042|㷢˹100ָ QDII-ָ|5, 26, 6|7.20149 +MA|270010|㷢300ETFA ӻ|5, 5, 28|9.83318 +MACD|270010|㷢300ETFA ӻ|7, 22, 11|5.37862 + +MA|002124|㷢˲ҵ|2, 9, 54|14.20545 +MACD|002124|㷢˲ҵ|3, 23, 6|14.9767 + +MA|002124|㷢˲ҵ|2, 9, 46|14.20545 +MACD|002124|㷢˲ҵ|3, 23, 6|14.63508 +MA|002963|egold|5, 6, 17|1.40699 +MACD|002963|egold|20, 26, 19|0.0 +MA|003321|eoil|5, 19, 10|16.64486 +MACD|003321|eoil|18, 26, 5|15.66147 +MA|004744|eGEI|2, 7, 13|0.68801 +MACD|004744|eGEI|19, 26, 10|0.0 +MA|110003|eSSE50|3, 19, 21|8.82616 +MACD|110003|eSSE50|3, 26, 5|15.25248 +MA|110020|HS300|2, 6, 55|6.33577 +MACD|110020|HS300|20, 39, 5|4.12515 +MA|110031|eHSI|3, 9, 19|10.72522 +MACD|110031|eHSI|3, 26, 13|10.08802 +MA|161130|eNASDAQ100|4, 10, 48|5.9706 +MACD|161130|eNASDAQ100|10, 26, 8|5.59138 +MA|110028|anxinB|2, 6, 8|3.53012 +MACD|110028|anxinB|15, 26, 13|0.44179 +MA|110022|eConsumption |5, 14, 60|13.26343 +MACD|110022|eConsumption |4, 27, 5|13.66247 +MA|161125|SPX500|2, 7, 22|2.63515 +MACD|161125|SPX500|4, 24, 12|5.59204 +MA|002124|㷢˲ҵ|2, 9, 46|14.20545 +MA|270007|㷢̳ɳ |5, 8, 42|6.85049 +MACD|270007|㷢̳ɳ |5, 26, 6|7.63547 +MA|270023|㷢ȫѡƱ(QDII) QDII|2, 9, 7|6.38554 +MACD|270023|㷢ȫѡƱ(QDII) QDII|18, 26, 8|3.88429 +MA|270002|㷢Ƚ |3, 9, 30|0.39729 +MACD|270002|㷢Ƚ |3, 24, 11|2.34982 +MA|270021|㷢 |3, 8, 18|0.55083 +MACD|270021|㷢 |9, 21, 20|0.0 +MA|162714|㷢֤100ָּ Ʊָ|3, 9, 60|3.12348 +MACD|162714|㷢֤100ָּ Ʊָ|5, 26, 8|0.61804 +MA|270001|㷢۸ |2, 7, 57|2.60364 +MACD|270001|㷢۸ |9, 26, 16|0.0 +MA|270025|㷢ҵȻA |2, 9, 59|8.20512 +MACD|270025|㷢ҵȻA |5, 26, 15|0.0 +MA|270028|㷢ҵѡ |2, 6, 46|0.55866 +MACD|270028|㷢ҵѡ |16, 26, 13|0.0 +MA|270027|㷢ȫũҵָ QDII-ָ|2, 17, 54|2.50809 +MACD|270027|㷢ȫũҵָ QDII-ָ|4, 21, 9|2.19256 +MA|270044|㷢˫ծծȯA ծȯ|5, 19, 44|2.33871 +MACD|270044|㷢˫ծծȯA ծȯ|4, 21, 5|2.07336 +MA|270006|㷢ѡ |5, 12, 55|2.23843 +MACD|270006|㷢ѡ |9, 22, 16|8.38594 +MA|270045|㷢˫ծծȯC ծȯ|4, 19, 57|2.1987 +MACD|270045|㷢˫ծծȯC ծȯ|3, 30, 5|1.76328 +MA|162719|㷢˹ʯָA QDII-ָ|5, 18, 32|10.05345 +MACD|162719|㷢˹ʯָA QDII-ָ|3, 33, 5|10.27079 +MA|270048|㷢ծծȯA ծȯ|2, 9, 11|0.0 +MACD|270048|㷢ծծȯA ծȯ|4, 33, 20|-0.25338 +MA|270049|㷢ծծȯC ծȯ|2, 9, 7|-0.00854 +MACD|270049|㷢ծծȯC ծȯ|16, 21, 10|0.0 +MA|270022|㷢 |5, 8, 9|0.41537 +MACD|270022|㷢 |4, 28, 19|3.84968 +MA|270029|㷢۲ծȯA ծȯ|4, 11, 55|0.62893 +MACD|270029|㷢۲ծȯA ծȯ|3, 36, 5|1.53218 +MA|270030|㷢۲ծȯB ծȯ|5, 15, 14|0.53832 +MACD|270030|㷢۲ծȯB ծȯ|3, 30, 5|1.27757 +MA|162703|㷢С̳ɳ(LOF) |2, 6, 13|0.15417 +MACD|162703|㷢С̳ɳ(LOF) |3, 36, 17|16.07934 +MA|270008|㷢ľѡ |2, 9, 21|1.3269 +MACD|270008|㷢ľѡ |3, 26, 7|2.23394 +MA|270026|㷢С300 ӻ|2, 9, 8|-1.47176 +MACD|270026|㷢С300 ӻ|3, 37, 6|5.50068 +MA|270005|㷢۷ |2, 7, 40|1.0468 +MACD|270005|㷢۷ |5, 30, 19|1.4657 +MA|501303|㷢͹ָA none|3, 8, 9|5.26208 +MACD|501303|㷢͹ָA none|16, 26, 17|0.0 +MA|004119|㷢û |5, 9, 7|5.21415 +MACD|004119|㷢û |3, 26, 7|12.67661 +MA|270009|㷢ǿծȯ ծȯ|5, 9, 16|0.43867 +MACD|270009|㷢ǿծȯ ծȯ|5, 26, 5|0.89029 +MA|004243|㷢˹ʯָC QDII-ָ|5, 19, 36|9.97267 +MACD|004243|㷢˹ʯָC QDII-ָ|3, 26, 6|10.04748 +MA|162712|㷢ծȯ(LOF) ծȯ|3, 12, 8|0.76706 +MACD|162712|㷢ծȯ(LOF) ծȯ|3, 30, 5|1.40935 +MA|003745|㷢Ԫ˹Ʊ Ʊ|5, 9, 18|1.67859 +MACD|003745|㷢Ԫ˹Ʊ Ʊ|3, 26, 7|4.92835 +MA|002976|㷢֤ȫָҪC ӻ|3, 12, 40|6.18633 +MACD|002976|㷢֤ȫָҪC ӻ|3, 36, 8|21.92601 +MA|002987|㷢300ETFC ӻ|5, 9, 57|9.83236 +MACD|002987|㷢300ETFC ӻ|9, 25, 9|18.17226 +MA|002624|㷢ѡ |3, 9, 30|22.22221 +MACD|002624|㷢ѡ |3, 26, 5|32.82629 +MA|002979|㷢֤ȫָڵزC ӻ|2, 6, 14|8.20811 +MACD|002979|㷢֤ȫָڵزC ӻ|20, 30, 7|21.42019 +MA|162711|㷢֤500ETFA ӻ|2, 9, 8|-0.50599 +MACD|162711|㷢֤500ETFA ӻ|3, 26, 6|6.36342 +MA|002939|㷢 |3, 9, 58|0.86664 +MACD|002939|㷢 |9, 21, 11|20.40406 +MA|004855|㷢֤ȫָָC none|5, 9, 55|-1.88826 +MACD|004855|㷢֤ȫָָC none|20, 26, 10|0.0 +MA|002121|㷢Ʊ Ʊ|2, 9, 54|9.89323 +MACD|002121|㷢Ʊ Ʊ|4, 26, 12|36.46479 +MA|005402|㷢ԴѡƱ none|2, 9, 14|3.2046 +MA|002863|ڳɳϷʽ |3, 9, 46|-0.18518 +MACD|002863|ڳɳϷʽ |16, 26, 17|0.0 +MA|164908|֤(LOF) Ʊָ|2, 6, 52|4.26195 +MACD|164908|֤(LOF) Ʊָ|17, 26, 13|0.0 +MA|501030|ָ֤A Ʊָ|4, 7, 52|4.12179 +MACD|501030|ָ֤A Ʊָ|14, 26, 15|0.0 +MA|501031|ָ֤C Ʊָ|4, 7, 52|4.15547 +MACD|501031|ָ֤C Ʊָ|15, 26, 13|0.0 +MA|001822|û |2, 6, 55|2.39453 +MACD|001822|û |3, 38, 9|2.96912 +MA|000800|δ |2, 7, 21|-0.43812 +MACD|000800|δ |3, 36, 20|-1.2987 +MA|630011|⾫ѡ |2, 12, 17|-0.50761 +MACD|630011|⾫ѡ |3, 23, 6|0.81522 +MA|000654|ҵ |2, 9, 10|-1.16279 +MACD|000654|ҵ |3, 22, 5|0.16794 +MA|630005|̶̬ |5, 12, 12|-0.53476 +MACD|630005|̶̬ |5, 21, 20|-1.63609 +MA|001679|ǰԴйϡȱʲA |5, 19, 58|2.45041 +MACD|001679|ǰԴйϡȱʲA |3, 26, 6|-2.80843 +MA|001071|ý廥 |2, 6, 55|-1.82167 +MACD|001071|ý廥 |15, 32, 13|11.54683 +MA|002079|ǰԴйϡȱʲC |4, 7, 15|0.22857 +MACD|002079|ǰԴйϡȱʲC |3, 21, 5|-2.6422 diff --git a/stockdata/IgnoreListStock.txt b/stockdata/IgnoreListStock.txt new file mode 100644 index 0000000..e69de29 diff --git a/stockdata/dongfangcf/399300.csv b/stockdata/dongfangcf/399300.csv new file mode 100644 index 0000000..fec7939 --- /dev/null +++ b/stockdata/dongfangcf/399300.csv @@ -0,0 +1,250 @@ +,Ʊ,,̼,߼,ͼ,̼,ǰ,ǵ,ǵ,,ɽ,ɽ,ֵ,ֵͨ +2018-02-02,'399300,300,4271.2326,4271.7647,4181.7793,4213.9386,4245.8978,25.3348,0.5967,,14606682700,1.91169867288e+11,, +2018-02-01,'399300,300,4245.8978,4287.3888,4214.2909,4276.3425,4275.8986,-30.0008,-0.7016,,19000293400,2.39230298932e+11,, +2018-01-31,'399300,300,4275.8986,4287.8576,4232.7727,4234.1101,4256.1021,19.7965,0.4651,,14521016400,1.94401798025e+11,, +2018-01-30,'399300,300,4256.1021,4308.5115,4251.5706,4286.6834,4302.0181,-45.916,-1.0673,,13903473000,1.86650566154e+11,, +2018-01-29,'399300,300,4302.0181,4395.9053,4287.1124,4387.0577,4381.2996,-79.2815,-1.8095,,19110035400,2.56176323789e+11,, 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+2017-02-09,'399300,300,3396.2924,3406.5888,3379.7872,3381.8992,3383.2878,13.0046,0.3844,,9755851500,1.02134228699e+11,, +2017-02-08,'399300,300,3383.2878,3383.6859,3347.4302,3361.7811,3365.6849,17.6029,0.523,,7540287900,82869735773.3,, +2017-02-07,'399300,300,3365.6849,3376.7099,3354.6251,3371.7695,3373.2051,-7.5202,-0.2229,,6375446200,69758178999.1,, +2017-02-06,'399300,300,3373.2051,3381.3348,3358.7516,3368.1081,3364.4923,8.7128,0.259,,6852136000,73408134767.9,, diff --git a/stockdata/stockcode.txt b/stockdata/stockcode.txt new file mode 100644 index 0000000..dc1e3cb --- /dev/null +++ b/stockdata/stockcode.txt @@ -0,0 +1,55 @@ +399006 ҵָ 1829.34 16.06 0.89% 39458473 6467087 1813.28 1811.11 1852.22 1807.51 +000003 Bָ 347.34 1.22 0.35% 205023 17374 346.12 346.39 347.49 346.25 +399106 ָ֤ 1960.74 -0.19 -0.01% 128435395 17864530 1960.93 1957.15 1962.11 1954.55 +399005 Сָ 7735.89 -2.66 -0.03% 42420988 5960737 7738.55 7729.53 7757.73 7722.33 +399002 ָR 13851.88 -14.75 -0.11% 46396882 5426701 13866.63 13843.70 13876.30 13809.35 +399001 ָ֤ 11595.22 -12.35 -0.11% 128435395 17864530 11607.57 11588.38 11615.67 11559.63 +000002 Aָ 3722.99 -5.36 -0.14% 145876081 17226990 3728.35 3723.83 3730.47 3695.32 +000001 ָ֤ 3554.40 -5.07 -0.14% 146081103 17244363 3559.47 3555.17 3561.50 3528.03 +000300 300 4369.30 -20.59 -0.47% 122636938 15661989 4389.89 4381.98 4383.15 4336.24 +399003 ɷBָ 6832.18 -33.66 -0.49% 159051 10004 6865.84 6872.14 6872.14 6811.48 +399550 50 7799.60 -50.88 -0.65% 18200029 3739922 7850.48 7848.96 7852.29 7750.90 +000016 ֤50 3158.30 -22.03 -0.69% 49732905 6206055 3180.33 3171.35 3171.35 3124.46 +000850 300ɫ 4245.92 86.29 2.07% 11472162 1023125 4159.64 4205.53 4251.30 4203.21 +000026 180Դ 3224.15 61.82 1.96% 16158750 1451073 3162.32 3201.67 3235.30 3192.40 +000811 ϸɫ 5901.84 104.68 1.81% 16203561 1612367 5797.16 5859.60 5911.04 5856.02 +000823 800ɫ 5317.98 91.67 1.75% 15125464 1523227 5226.31 5275.98 5322.13 5275.98 +000820 úָ̿ 3100.64 51.61 1.69% 5412616 578119 3049.03 3070.03 3133.42 3070.03 +399395 ֤ɫ 5253.67 96.73 1.88% 15733140 1510650 5156.94 5211.28 5256.40 5211.28 +399959 ָ 1169.73 21.32 1.86% 4785815 594991 1148.41 1146.33 1173.22 1145.74 +399436 ֤ú̿ 3556.77 64.14 1.84% 4945375 540165 3492.63 3518.18 3595.22 3518.18 +399368 ֤ 6382.16 112.45 1.79% 5008489 658014 6269.71 6255.30 6402.66 6251.64 +399232 ɿָ 2273.39 39.66 1.78% 2476069 262634 2233.73 2253.16 2283.69 2253.16 +000013 ծָ 213.93 0.01 0.01% 357067 34302 213.92 213.95 213.95 213.93 +000012 ծָ 161.18 0.01 0.00% 50643 4747 161.18 161.19 161.20 161.18 +000011 ָ 6470.11 -14.93 -0.23% 23075850 1361338 6485.04 6482.00 6482.00 6447.67 +000010 ֤180 9412.50 -40.68 -0.43% 84908334 9998669 9453.19 9435.60 9435.60 9326.03 + +000850 300ɫ ɷֹ 4245.92 86.29 2.07% 1.16% 1147 102 4159.64 4205.53 4251.30 4203.21 0.21% +00026 180Դ ɷֹ 3224.15 61.82 1.96% 1.36% 1616 145 3162.32 3201.67 3235.30 3192.40 0.17% +399959 ָ ɷֹ 1169.73 21.32 1.86% 2.39% 479 59.5 1148.41 1146.33 1173.22 1145.74 -0.04% +000811 ϸɫ ɷֹ 5901.84 104.68 1.81% 0.95% 1620 161 5797.16 5859.60 5911.04 5856.02 0.23% +399232 ɿָ ɷֹ 2273.39 39.66 1.78% 1.37% 248 26.3 2233.73 2253.16 2283.69 2253.16 0.05% +399973 ֤ ɷֹ 1267.51 21.43 1.72% 2.26% 192 33.2 1246.07 1244.21 1271.87 1243.67 -0.01% +000145 Դ ɷֹ 4454.13 74.04 1.69% 1.00% 1651 165 4380.09 4419.82 4462.17 4418.23 0.15% +000820 úָ̿ ɷֹ 3100.64 51.61 1.69% 2.08% 541 57.8 3049.03 3070.03 3133.42 3070.03 0.09% +399998 ֤ú̿ ɷֹ 1871.74 30.72 1.67% 2.07% 522 56.0 1841.02 1852.19 1890.22 1852.19 0.07% +000068 ֤Դ ɷֹ 2421.73 39.24 1.65% 1.06% 2187 203 2382.49 2405.38 2429.37 2404.21 0.14% +399944 ڵԴ ɷֹ 3201.32 52.05 1.65% 0.95% 2292 226 3149.27 3180.09 3210.05 3180.09 0.16% +000944 ڵԴ ɷֹ 3201.32 52.05 1.65% 0.95% 2292 226 3149.27 3180.09 3210.05 3180.09 0.14% +399961 ֤ ɷֹ 3065.16 49.03 1.63% 0.96% 2409 232 3016.13 3045.56 3074.63 3045.56 0.15% +399249 ָ ɷֹ 789.08 12.64 1.63% 1.91% 40.9 3.73 776.44 774.65 789.36 774.53 0.22% +000961 ֤ ɷֹ 3065.16 49.03 1.63% 0.96% 2409 232 3016.13 3045.56 3074.63 3045.56 0.14% +000909 300 ɷֹ 2736.49 43.57 1.62% 1.11% 1652 157 2692.92 2708.41 2737.78 2707.98 0.13% +399909 300 ɷֹ 2736.49 43.57 1.62% 1.11% 1652 157 2692.92 2708.41 2737.78 2707.98 0.16% +000094 ֤ ɷֹ 2753.92 43.62 1.61% 1.07% 2211 205 2710.30 2736.94 2763.75 2734.62 0.15% +000805 AԴ ɷֹ 4982.71 78.36 1.60% 1.04% 2399 231 4904.35 4944.11 4995.19 4944.11 0.09% +399967 ֤ ɷֹ 8691.95 136.86 1.60% 2.18% 486 66.6 8555.10 8536.23 8717.44 8530.89 -0.02% + +NDX ˹ + +AUM 281.70 1.90 0.68% 281.25 281.85 280.75 279.80 113890 58110 55780 289248 +AGTD T+D 3819 77 2.06% 3823 3765 3773 3742 2018-01-25 11:29:59 +GLNC COMEXƽ 1362.3 6.0 0.44% 1362.8 1355.2 1357.0 1356.3 2018-01-25 11:40:14 +SLNC COMEX 17.600 0.111 0.63% 17.605 17.470 17.555 17.489 2018-01-25 11:40:13 +NYMEX ԭ \ No newline at end of file diff --git a/stockdata/stockcodewangyiShangz.txt b/stockdata/stockcodewangyiShangz.txt new file mode 100644 index 0000000..08d6e25 --- /dev/null +++ b/stockdata/stockcodewangyiShangz.txt @@ -0,0 +1,40 @@ +15 000016 ֤50 3163.65 -16.68 -0.52% 0.01 59.32 739.64 3180.33 3171.35 3171.35 3124.46 +1 000001 ָ֤ 3561.27 1.80 0.05% 0.01 176.40 2076.49 3559.47 3555.17 3561.67 3528.03 +11 000011 ָ 6475.15 -9.89 -0.15% 0.01 29.48 170.01 6485.04 6482.00 6482.00 6447.67 +12 000012 ծָ 161.17 -0.01 -0.01% 0.00 619.12 5815.76 161.18 161.19 161.20 161.17 +13 000013 ծָ 213.94 0.02 0.01% 0.00 6124.99 5.90 213.92 213.95 213.95 213.93 +14 000015 ָ 3259.71 -5.43 -0.17% 0.01 42.04 416.80 3265.14 3261.86 3263.32 3227.47 +2 000002 ָ 3730.20 1.85 0.05% 0.01 176.15 2074.38 3728.35 3723.83 3730.62 3695.32 +4 000004 ҵָ 2789.62 14.52 0.52% 0.01 85.53 1030.47 2775.10 2779.36 2791.40 2771.46 +5 000005 ҵָ 3402.42 -20.87 -0.61% 0.01 9.80 136.18 3423.29 3403.73 3404.51 3383.14 +6 000006 زָ 8474.63 -72.33 -0.85% 0.03 8.05 103.09 8546.96 8522.93 8522.93 8285.62 +7 000007 ָ 6243.54 77.39 1.26% 0.02 22.44 198.31 6166.15 6158.51 6245.52 6143.97 +8 000008 ۺָ 3400.29 -24.20 -0.71% 0.02 50.58 608.43 3424.49 3410.31 3410.31 3358.02 +9 000009 ֤380 5738.29 19.59 0.34% 0.01 33.05 399.49 5718.70 5716.02 5740.00 5707.48 +10 000010 ֤180 9430.67 -22.52 -0.24% 0.01 101.75 1202.31 9453.19 9435.60 9435.60 9326.03 +16 000017 ָ 3007.77 1.48 0.05% 0.01 175.27 2054.03 3006.29 3002.66 3008.11 2979.65 +17 000018 180 6026.05 -58.45 -0.96% 0.02 37.51 463.68 6084.50 6054.17 6054.17 5961.10 +18 000019 ָ 1187.46 -1.79 -0.15% 0.01 81.87 921.37 1189.25 1187.24 1187.63 1175.56 +19 000020 ָ 1259.54 4.45 0.35% 0.01 13.35 171.05 1255.09 1253.29 1260.12 1251.21 +21 000022 ˾ծ 182.26 0.03 0.02% 0.00 3886.14 3.78 182.23 182.26 182.27 182.25 +22 000025 180 2059.36 19.26 0.94% 0.01 15.64 127.69 2040.10 2040.31 2060.44 2036.09 +23 000026 180Դ 3226.06 63.74 2.02% 0.01 18.60 170.54 3162.32 3201.67 3235.30 3192.40 +24 000027 180 1280.15 18.26 1.45% 0.03 7.64 73.89 1261.89 1255.10 1284.36 1248.99 +25 000028 180ɳ 3698.13 -24.73 -0.66% 0.02 25.17 460.85 3722.86 3719.03 3719.03 3658.68 +26 000029 180ֵ 4717.53 36.57 0.78% 0.02 86.46 889.36 4680.96 4668.04 4750.06 4667.99 +27 000030 180Rɳ 2314.55 9.49 0.41% 0.01 68.24 995.70 2305.06 2298.11 2323.21 2297.85 +28 000031 180Rֵ 3455.12 21.03 0.61% 0.02 122.27 1301.14 3434.09 3424.25 3477.60 3423.74 +29 000032 ֤Դ 1835.32 2.57 0.14% 0.02 9.96 89.04 1832.75 1822.04 1840.57 1811.43 +30 000033 ֤ 2421.70 -14.77 -0.61% 0.01 24.58 249.78 2436.47 2415.57 2431.66 2408.68 +31 000034 ֤ҵ 2557.26 24.12 0.95% 0.01 27.74 285.98 2533.14 2531.02 2567.08 2530.77 +32 000035 ֤ѡ 3056.99 13.06 0.43% 0.01 5.62 93.16 3043.93 3041.07 3066.65 3036.53 +33 000036 ֤ 9729.36 43.68 0.45% 0.01 3.97 97.98 9685.68 9677.09 9753.11 9665.87 +34 000037 ֤ҽҩ 7165.88 12.91 0.18% 0.01 3.29 78.36 7152.97 7148.10 7178.45 7135.69 +35 000038 ֤ 5935.56 30.56 0.52% 0.02 57.46 642.70 5905.00 5887.89 5991.23 5887.89 +36 000039 ֤Ϣ 3507.91 9.69 0.28% 0.01 3.59 65.71 3498.22 3491.05 3520.78 3487.43 +37 000040 ֤ 3446.78 -20.02 -0.58% 0.01 3.94 48.33 3466.80 3462.74 3471.74 3441.34 +38 000041 ֤ 2135.27 -4.12 -0.19% 0.00 4.03 24.82 2139.39 2137.18 2139.96 2129.41 +39 000042 ֤ 1842.06 10.62 0.58% 0.02 77.92 704.67 1831.44 1824.57 1855.44 1823.72 +44 000047 ֤ȫָ 3652.69 14.25 0.39% 0.01 171.44 1952.69 3638.44 3628.58 3669.26 3627.71 +45 000048 ָ 1629.02 11.85 0.73% 0.02 42.38 662.45 1617.17 1613.86 1638.91 1613.86 + diff --git a/stockdata/stockcodewangyiShenz.txt b/stockdata/stockcodewangyiShenz.txt new file mode 100644 index 0000000..f075708 --- /dev/null +++ b/stockdata/stockcodewangyiShenz.txt @@ -0,0 +1,32 @@ +44 399300 300 4381.30 16.22 0.37% 0.01 177.23 2233.31 4365.08 4352.22 4403.34 4351.49 +4 399004 ֤100R 6150.90 -22.31 -0.36% 0.01 39.58 604.90 6173.21 6160.29 6169.76 6104.60 +5 399005 Сָ 7745.61 7.06 0.09% 0.00 17.80 290.18 7738.55 7729.53 7757.73 7722.33 +6 399006 ҵָ 1829.05 15.78 0.87% 0.02 23.13 362.12 1813.28 1811.11 1852.23 1807.52 +1 399001 ָ֤ 11609.08 1.51 0.01% 0.00 183.43 2137.22 11607.57 11588.38 11615.67 11559.63 +2 399002 ָR 13868.43 1.80 0.01% 0.00 89.69 1274.81 13866.63 13843.70 13876.30 13809.35 +3 399003 ɷݣָ 6839.27 -26.57 -0.39% 0.01 684.43 4760.20 6865.84 6872.14 6872.14 6811.48 +7 399007 ֤300 4891.74 -4.21 -0.09% 0.01 71.73 1040.11 4895.94 4887.42 4896.02 4866.96 +8 399008 С300 1516.34 2.26 0.15% 0.01 31.88 461.04 1514.08 1512.11 1518.39 1510.71 +19 399101 С 11536.80 19.75 0.17% 0.00 50.96 718.94 11517.05 11499.73 11538.74 11486.09 +20 399102 ҵ 2255.72 13.58 0.61% 0.02 44.52 731.02 2242.14 2237.99 2273.78 2234.59 +22 399106 ָ֤ 1963.39 2.46 0.13% 0.00 151.46 2102.24 1960.93 1957.15 1963.50 1954.55 +25 399231 ũָ 1304.92 -1.36 -0.10% 0.01 1.38 16.93 1306.28 1305.54 1309.17 1299.11 +26 399232 ɿָ 2240.26 -25.98 -1.15% 0.01 2.63 27.23 2266.23 2246.23 2250.29 2236.89 +27 399233 ָ 2140.18 -3.08 -0.14% 0.01 102.41 1489.77 2143.26 2137.76 2147.12 2135.74 +28 399234 ˮָ 905.77 -3.70 -0.41% 0.00 2.32 19.88 909.47 907.13 909.12 905.55 +29 399235 ָ 1920.25 -5.25 -0.27% 0.01 3.52 40.54 1925.50 1921.58 1932.85 1915.81 +30 399236 ָ 1842.84 -8.87 -0.48% 0.01 3.86 46.77 1851.71 1849.68 1854.96 1841.82 +31 399237 ָ 1413.79 12.13 0.87% 0.01 2.81 30.57 1401.65 1397.94 1416.19 1396.16 +32 399238 ָ 1552.27 1.40 0.09% 0.01 681.37 5642.95 1550.87 1550.72 1558.33 1548.42 +33 399239 ITָ 1916.67 -2.49 -0.13% 0.01 18.30 288.61 1919.16 1909.19 1929.22 1908.81 +34 399240 ָ 1214.24 -3.23 -0.27% 0.01 10.79 128.40 1217.47 1213.20 1229.56 1211.64 +35 399241 زָ 3534.74 28.48 0.81% 0.02 12.08 152.71 3506.25 3495.92 3568.30 3486.03 +36 399242 ָ 1756.70 -3.63 -0.21% 0.01 4.43 46.98 1760.33 1760.08 1767.58 1752.78 +37 399243 ָ 2109.66 -21.53 -1.01% 0.01 8605.52 20.47 2131.19 2120.98 2130.12 2107.19 +38 399244 ָ 1589.49 -9.40 -0.59% 0.01 1.32 24.18 1598.89 1595.06 1600.14 1588.04 +39 399248 Ļָ 1522.97 6.39 0.42% 0.01 4.35 55.08 1516.59 1512.72 1529.41 1509.72 +40 399249 ָ 779.22 -6.70 -0.85% 0.01 4987.54 4.87 785.92 784.81 784.81 778.12 +45 399301 ծ 158.50 0.02 0.01% 0.00 386.88 3.78 158.49 158.51 158.54 158.50 +46 399302 ˾ծ 161.68 0.02 0.01% 0.00 386.73 3.78 161.66 161.68 161.70 161.67 +48 399306 ֤ETF 1284.04 6.12 0.48% 0.01 13.15 22.38 1277.92 1276.90 1288.05 1275.22 +49 399307 ֤תծ 191.68 0.20 0.10% 0.01 489.12 5.12 191.49 191.55 192.14 191.16 diff --git a/stockdata/stockcodewangyidetail.txt b/stockdata/stockcodewangyidetail.txt new file mode 100644 index 0000000..adf8502 --- /dev/null +++ b/stockdata/stockcodewangyidetail.txt @@ -0,0 +1,3 @@ +1 W00004 վ225 +2 W00031 ¹DAXָ +3 HSI001 ָ \ No newline at end of file diff --git a/stockdata/stockcodewangyiqdii.txt b/stockdata/stockcodewangyiqdii.txt new file mode 100644 index 0000000..e06990d --- /dev/null +++ b/stockdata/stockcodewangyiqdii.txt @@ -0,0 +1,2 @@ +1 NASDAQ ˹ 3561.27 1.80 0.05% 0.01 176.40 2076.49 3559.47 3555.17 3561.67 3528.03 +2 SP500 500 3730.20 1.85 0.05% 0.01 176.15 2074.38 3728.35 3723.83 3730.62 3695.32 \ No newline at end of file diff --git a/trade_process/__init__.py b/trade_process/__init__.py index 9d4bb85..1f56271 100644 --- a/trade_process/__init__.py +++ b/trade_process/__init__.py @@ -1 +1 @@ -__author__ = 'cbb' +__author__ = 'cbb' diff --git a/trade_process/__init__.pyc b/trade_process/__init__.pyc new file mode 100644 index 0000000..c6e600f Binary files /dev/null and b/trade_process/__init__.pyc differ diff --git a/trade_process/dongfangcf/399307.csv b/trade_process/dongfangcf/399307.csv new file mode 100644 index 0000000..678844c --- /dev/null +++ b/trade_process/dongfangcf/399307.csv @@ -0,0 +1,246 @@ +,Ʊ,,̼,߼,ͼ,̼,ǰ,ǵ,ǵ,,ɽ,ɽ,ֵ,ֵͨ +2018-01-26,'399307,֤תծ,191.6822,192.1395,191.1625,191.5485,191.4865,0.1957,0.1022,,4891199,512406147.931,, +2018-01-25,'399307,֤תծ,191.4865,191.9322,189.5327,189.5327,189.5787,1.9078,1.0063,,7160037,743034134.523,, +2018-01-24,'399307,֤תծ,189.5787,189.5932,187.9129,187.9954,188.0245,1.5542,0.8266,,3965334,403408272.655,, +2018-01-23,'399307,֤תծ,188.0245,188.2967,187.3681,187.8975,187.5066,0.5179,0.2762,,2953724,302123248.662,, +2018-01-22,'399307,֤תծ,187.5066,187.6527,185.0705,185.2946,185.1969,2.3097,1.2472,,2952880,313359809.36,, +2018-01-19,'399307,֤תծ,185.1969,185.6881,184.6927,184.8541,185.0765,0.1204,0.0651,,1781952,181597265.692,, +2018-01-18,'399307,֤תծ,185.0765,185.111,184.066,184.1763,184.4281,0.6484,0.3516,,2335703,239026520.59,, +2018-01-17,'399307,֤תծ,184.4281,185.4243,183.4969,184.6081,184.5247,-0.0966,-0.0524,,2492686,247824871.068,, +2018-01-16,'399307,֤תծ,184.5247,184.9917,184.4632,184.7083,184.6735,-0.1488,-0.0806,,1560711,156296391.237,, +2018-01-15,'399307,֤תծ,184.6735,186.6803,184.6438,186.5784,186.6871,-2.0136,-1.0786,,1987953,202526787.371,, +2018-01-12,'399307,֤תծ,186.6871,187.1092,186.5642,187.0468,187.1066,-0.4195,-0.2242,,1912916,198756533.554,, +2018-01-11,'399307,֤תծ,187.1066,187.1693,185.9555,186.6534,186.6885,0.4181,0.224,,1552818,157602971.779,, +2018-01-10,'399307,֤תծ,186.6885,187.6209,186.0098,187.6209,187.6665,-0.978,-0.5211,,2281942,237491227.894,, 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from spider import * +#import itchat +#from sleep import * +# import datetime +# import one_predict +import json,time +import sched + +import datetime +import re +# import sys +import urllib2 +import smtplib +from email.mime.text import MIMEText +from email.mime.multipart import MIMEMultipart +from email.utils import formataddr +from email.mime.application import MIMEApplication +from fund_zf import main_zf +from strategy.macd_back_test import macdmain,mainStock +# from numpy import mean, ptp, var, std + +import requests +from bs4 import BeautifulSoup + + +def spider(url): + r = requests.get(url) + page = r.text + soup = BeautifulSoup(page, 'html.parser') + value = soup.find_all(id='gz_gszzl') + value = value[0].get_text().replace('%','') + value=value.encode('utf-8') + value1 = soup.find_all(id='gz_gsz') + value1 = value1[0].get_text().encode('utf-8') + try: + return [float(value1),float(value1),float(value)] + except ValueError as e: + return +# 是否已在list中 +def get_index(fund_code, all_fund_list): + fund_num = len(all_fund_list) + fund_index = 0 + while fund_index < fund_num: + if fund_code in all_fund_list[fund_index]: + break; + fund_index += 1 + return fund_index + + +# 获取基金类型 +def get_type(fund_code, all_fund_list): + fund_type = 'none' + for fund in all_fund_list: + if fund_code in fund: + fund_type = fund[3] + break + + return fund_type + +# 获取某一基金在某一日的累计净值数据 +def get_jingzhi(strfundcode, strdate,stredate): + try: + url = 'http://fund.eastmoney.com/f10/F10DataApi.aspx?type=lsjz&code=' + str(strfundcode) + '&page=1&per=1000&sdate=' + str(strdate) + '&edate=' + str(stredate) + print u'数据链接：'+url + '\n' + response = urllib2.urlopen(url) + except urllib2.HTTPError, e: + print e + urllib_error_tag = True + except StandardError, e: + print e + urllib_error_tag = True + else: + urllib_error_tag = False + + if urllib_error_tag == True: + return '-1' + + json_fund_value = response.read().decode('utf-8') + # print json_fund_value + + tr_re = re.compile(r'(.*?)') + item_re = re.compile( + r'''(\d{4}-\d{2}-\d{2})(.*?)(.*?)(.*?)(.*?)(.*?)''', + re.X) + + # 获取不到 返回0 + jingzhi ='-1' + result=[] + + for line in tr_re.findall(json_fund_value): + # print line + '\n' + match = item_re.match(line) + if match: + entry = match.groups() + entry[3].rstrip('%') + date = datetime.datetime.strptime(entry[0], '%Y-%m-%d') + # jingzhi = entry[2] + if entry[3]!='' and entry[2]!='': + result.append([date, float(entry[1]),float(entry[2]), float(entry[3].rstrip('%'))]) + else: + result.append([date, float(entry[1]), float(0), 0]) + jingzhi1 = entry[1] + jingzhi2 = entry[2] + # print jingzhi2 + + if jingzhi2.strip() == '': + # 040028 + # 净值日期 每万份收益 7日年化收益率（%） 申购状态 赎回状态 分红送配 + # 2017-01-06 1.4414 暂停申购 暂停赎回 + # 2017-01-05 1.4369 暂停申购 暂停赎回 + jingzhi = '-1' + elif jingzhi2.find('%') > -1: + # 040003 + # 净值日期 每万份收益 7日年化收益率（%） 申购状态 赎回状态 分红送配 + # 2017-03-27 1.1149 3.9450% 限制大额申购 开放赎回 + # 2017-03-26* 2.2240 3.8970% 限制大额申购 开放赎回 + jingzhi = '-1' + elif float(jingzhi1) > float(jingzhi2): + # 502015 + # 净值日期 单位净值 累计净值 日增长率 申购状态 赎回状态 分红送配 + # 2017-03-27 0.6980 0.3785 -2.24% 场内买入 场内卖出 + # 2017-03-24 0.7140 0.3945 5.15% 场内买入 场内卖出 + jingzhi = entry[1] + else: + # + # 净值日期 单位净值 累计净值 日增长率 申购状态 赎回状态 分红送配 + # 2017-03-28 1.7720 1.7720 -0.23% 开放申购 开放赎回 + # 2017-03-27 1.7761 1.7761 -0.43% 开放申购 开放赎回 + jingzhi = entry[2] + return result + +#获取历史数据 +def get_histrydata(strfundcode,numdays): + # 当前日期 + strtoday = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d') + tdatetime = datetime.datetime.strptime(strtoday, '%Y-%m-%d') + print u'结束时间：' + strtoday + + # 昨天 + yestodaytime = tdatetime - datetime.timedelta(days=1) + yestoday = datetime.datetime.strftime(yestodaytime, '%Y-%m-%d') + + # 前年今日 + sdatetime = tdatetime - datetime.timedelta(days=numdays) + strsdate = datetime.datetime.strftime(sdatetime, '%Y-%m-%d') + print u'开始时间：' + strsdate + + # 1.1 起始时间 + strsdate = strsdate # sys.argv[1] + stredate = yestoday # sys.argv[2] + + # 今日零时 + # strtoday = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d') + # tdatetime = datetime.datetime.strptime(strtoday, '%Y-%m-%d') + # print tdatetime + + # 开始时间 如果是周六 周日 调整到周五 + # print strsdate + sdatetime = datetime.datetime.strptime(strsdate, '%Y-%m-%d') + sdatetime.isoweekday() + if sdatetime.isoweekday() == 7: + sdatetime = sdatetime + datetime.timedelta(days=-2) + elif sdatetime.isoweekday() == 6: + sdatetime = sdatetime + datetime.timedelta(days=-1) + + strsdate = datetime.datetime.strftime(sdatetime, '%Y-%m-%d') + # print strsdate + + # 结束时间 如果是周六 周日 调整到周五 + # print stredate + edatetime = datetime.datetime.strptime(stredate, '%Y-%m-%d') + edatetime.isoweekday() + if edatetime.isoweekday() == 7: + edatetime = edatetime + datetime.timedelta(days=-2) + elif edatetime.isoweekday() == 6: + edatetime = edatetime + datetime.timedelta(days=-1) + + stredate = datetime.datetime.strftime(edatetime, '%Y-%m-%d') + # print stredate + + # 判断时间段 今日净值要下午才出 一律不处理 + if edatetime <= sdatetime or tdatetime <= sdatetime or tdatetime <= edatetime: + print '判断时间段 今日净值要下午才出 一律不处理' + print 'date input error!\n' + + jingzhimin = get_jingzhi(strfundcode[0], strsdate, stredate) + if not(tdatetime.isoweekday() == 7 or tdatetime.isoweekday() == 6): + url = 'http://fund.eastmoney.com/%s.html' % strfundcode[0] + todayvalue = spider(url) + if todayvalue != None: + jingzhimin.insert(0, [tdatetime, todayvalue[0], todayvalue[1], todayvalue[2]]) + return jingzhimin + +#计算交易策略 +def fenxi(strfundcode,jingzhimin,method): + #获取历史数据 + # jingzhimin=get_histrydata(strfundcode,numdays) + years = [x[0] for x in jingzhimin] + price = [x[1] for x in jingzhimin] + changeprice = [x[3]/x[1] for x in jingzhimin] + price_stre = [((x[1] - (sum(price)/len(price)))/(max(price)-min(price)))*(max(changeprice)-min(changeprice)) for x in jingzhimin] + # plt.plot(years, changeprice, 'g',label='change') + starty = [0 for x in jingzhimin] + jizhipoints=jizhi(jingzhimin) + celue_point=celue(jingzhimin,jizhipoints,method) + # change_fenxi(change5days) + return celue_point + +def stdDeviation(a): + l = len(a) + m = sum(a) / l + d = 0 + for i in a: d += (i - m) ** 2 + return (d * (1 / l)) ** 0.5 + +#统计 +# def caculate(data): +# # 极差 +# ptp(data) +# # 方差 +# var(data) +# # 标准差 +# std(data) +# # 变异系数 +# mean(data) / std(data) +# norm.ppf(0.05, mean(data), std(data)) + +#求极值 +def jizhi(funddata): + jizhipoints={} + alldata = [x[1] for x in funddata[::-2]] + jizhipoints['allmax']= [alldata.index(max(alldata)),max(alldata)] + jizhipoints['allmin']= [alldata.index(min(alldata)),min(alldata)] + data30days = [x[1] for x in funddata[:-30:-2]] + jizhipoints['30daysmax'] = [data30days.index(max(data30days)),max(data30days)] + jizhipoints['30daysmin'] = [data30days.index(min(data30days)),min(data30days)] + data180days = [x[1] for x in funddata[:-180:-2]] + jizhipoints['180daysmax'] = [data180days.index(max(data180days)), max(data180days)] + jizhipoints['180daysmin'] = [data180days.index(min(data180days)), min(data180days)] + data5days = [x[1] for x in funddata[:-5:-2]] + jizhipoints['5daysmax'] = [data5days.index(max(data5days)), max(data5days)] + jizhipoints['5daysmin'] = [data5days.index(min(data5days)), min(data5days)] + return jizhipoints + +#交易策略监测 +def celue(funddata,jizhipoints,method): + todaydata = funddata[0] + data5days = [x[1] for x in funddata[:5:1]] + change = [x[3] for x in funddata[:5:1]] + change7 = [x[3] for x in funddata[:7:1]] + change12days= [x[3] for x in funddata[:90:1]] + if stdDeviation(change12days)<0.3: + r=1.005;rmax=1.002 + elif stdDeviation(change12days) > 0.6: + r = 1.009;rmax = 1.002 + else: + r = 1.005;rmax = 1.002 + buy_points = [] + sell_point = [] + + #根据净值比较 + if(todaydata[1]<=r*jizhipoints['allmin'][1])and(sum(change)<-1.1): + buy_points.append({'估值接近1年最小净值':str(todaydata[1])+str('<=')+str(r*jizhipoints['allmin'][1])}) + elif(todaydata[1]<=r*jizhipoints['180daysmin'][1])and(sum(change)<-1.1): + buy_points.append({'估值接近180天最小净值': str(todaydata[1])+str('<=')+str(r*jizhipoints['180daysmin'][1])}) + elif(todaydata[1]<=r*jizhipoints['30daysmin'][1])and(sum(change)<-0.7): + buy_points.append({'估值接近30天最小净值': str(todaydata[1] )+str('<=')+str( r*jizhipoints['30daysmin'][1])}) + # elif (todaydata[1] <= r*jizhipoints['5daysmin'][1]): + # buy_points['today<1.02*5min'] = str(todaydata[1] )+str('<=')+str( r*jizhipoints['5daysmin'][1]) + #---------------------------- + elif (todaydata[1] >= rmax*jizhipoints['allmax'][1])and method=='general': + sell_point.append({'估值接近1年最大净值': str(todaydata[1] )+str('>=')+str( rmax*jizhipoints['allmax'][1])}) + elif (todaydata[1] >= rmax*jizhipoints['180daysmax'][1])and method=='general': + sell_point.append({'估值接近180天最大净值': str(todaydata[1] )+str('>=')+str( rmax*jizhipoints['180daysmax'][1])}) + # elif (todaydata[1] >= rmax*jizhipoints['30daysmax'][1]): + # sell_point.append({'today>0.9*30max': str(todaydata[1] )+str('<=')+str( rmax*jizhipoints['30daysmax'][1])}) + # elif (todaydata[1] >= rmax*jizhipoints['5daysmax'][1]): + # sell_point.append({'today>0.9*30max': str(todaydata[1] )+str('<=')+str( rmax*jizhipoints['5daysmax'][1])}) + + # def change_fenxi(change): + if(sum(change)/len(change)>=0.5)and change[0]<0.2 and change[0]= 0.7)and change[0]<0.2 and sum(change7[:2]) / len(change7[:2])<0.4 and method == 'top' : + sell_point.append({'5天涨幅均值': round(sum(change) / len(change), 5)}) + + big=[];small=[];small7=[];big7=[] + if len(change)>3: + for i in range(len(change)): + if (change[i]>0): + big.append(change[i]) + elif (change[i]<0) : + small.append(change[i]) + for j in range(0,len(change7)): + if (change7[j]>0.05)and method=='top': + big7.append(change7[j]) + elif (change7[j]<0.05)and method=='top': + small7.append(change7[j]) + if len(big)>=4 and change[0]<0.2 and change[0]=5 and change[0]<0.2 and change[0]=3): + # sell_point.append({u'go up 4days': big}) + if (len(small)>=4 ) : + buy_points.append({'已跌四天':change7}) + elif(((max(data5days[2:])-data5days[0])/max(data5days[2:]))>=0.025)and(sum(change)<-1.1): + down=round((max(data5days[2:])-data5days[0])/max(data5days[2:]),5) + buy_points.append({'五天跌幅超2.5%': down}) + + return {'sell':sell_point,'buy':buy_points} + +def main_run(all_fund_list): + code = [['002124','广发新兴产业'],['002963', 'egold'], ['003321', 'eoil'], ['004744', 'eGEI'], ['110003', 'eSSE50'], ['110020', 'HS300'], + ['110031', 'eHSI'], ['161130', 'eNASDAQ100'], ['110028', 'anxinB'], ['110022', 'eConsumption '], + ['161125', 'SPX500']] + buysell = [] + buysell1 = [] + for i in code: + jingzhimin = get_histrydata(i, 365*1) + buysell1.append(macdmain(i,jingzhimin)) + # save(strfundcode=i ,numdays=365*1) + sb=fenxi(strfundcode=i, jingzhimin=jingzhimin, method ='general') + if sb: + buysell.append([i,sb,'code']) + for i in all_fund_list: + # save(strfundcode=i ,numdays=365*1) + jingzhimin1 = get_histrydata(i, 365*1) + buysell1.append(macdmain(i,jingzhimin1)) + sb=fenxi(strfundcode=i, jingzhimin=jingzhimin1, method='top') + if sb: + buysell.append([i, sb, 'all_fund_list']) + buysellstock = mainStock() + return buysell,buysell1,buysellstock + +s = sched.scheduler(time.time, time.sleep) +class DateEncoder(json.JSONEncoder): + def default(self, obj): + if isinstance(obj, datetime): + return obj.__str__() + return json.JSONEncoder.default(self, obj) + + # sleep() +#@itchat.msg_register(itchat.content.TEXT) +def print_content(msg): + print msg['Text'] +def readmsg(msg): + return msg +def send_email(text): + #text = self.claw_content() + #open('b.txt', 'w').write(str(text[1]) + '\n' + str(text[0]) + '\n') + username = 'lsl_cug@126.com' # input("请输入账号:") + password = '123456lsl' # input("请输入密码:") + sender = username + # sender='' + receiver = ['1627041882@qq.com','1006209211@qq.com'] # '760140853@qq.com','xxxxxxxxxx@qq.com','xxxxxxxxxx@126.com','994992333@qq.com','1847725033@qq.com','1847725033@qq.com','849281511@qq.com' + if sender =='': + username = str(raw_input("Please Input Sender Email Address,for example:xxxxxxxxxx@126.com \n")) + sender = username + password = str(raw_input("Please Input Sender Password \n")) + receiver.append(str(raw_input("Please Input Receiver Address,for example:xxxxxxxxxx@qq.com \n"))) + iRec = 1 + while iRec>0: + CmdRec = ''#raw_input("Whether you want to add another Receiver Address(default n)? \n") + if CmdRec == 'y' or CmdRec == 'yes': + iRec = 1 + receiver.append(str(raw_input("Please Input Receiver Address,for example:xxxxxxxxxx@qq.com \n"))) + else: + iRec = 0 + + ConfirmRec = ''#raw_input("Confirm? \n") + if ConfirmRec == 'n' or ConfirmRec == 'no': + print("What Do You Want? Maybe run this program again.\n") + exit(1) + else: + for rece in receiver: + print("OK \n"+ username + " sendto " + rece) + # 创建一个带附件的实例 + msg = MIMEMultipart() + # msg = MIMEText(str(text), 'plain', 'utf-8') + msg['From'] = formataddr(['user', sender]) # 括号里的对应发件人邮箱昵称、发件人邮箱账号 + msg['To'] = ",".join(receiver) # 括号里的对应收件人邮箱昵称、收件人邮箱账号 + + subject_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + subject = '基金信息' + msg['Subject'] = subject + + # 邮件正文内容 + # print str(text[0]) + realText = text#sendmsg #str(text) #'\n'+str(subject_time)+'：主人，有人来招人啦！^—^\n'+ '海投网：'+str(text[1])+'.'#+str(text[0])#'地大就业网招聘公告：'+str(text1[1])+'地大就业网gis等招聘信息：'+str(text2[1])+'.'#+str(text1[0])+'\n'+str(text2[1])+'\n'+str(text2[0]) + print realText + msg.attach(MIMEText(realText, 'plain', 'utf-8')) + # 构造附件1，传送当前目录下的 test.txt 文件 + # att1 = MIMEApplication(open('b.txt', 'rb').read()) + # 这里的filename可以任意写，写什么名字，邮件中显示什么名字 + # att1.add_header('Content-Disposition', 'attachment', filename='hjx.txt') + # msg.attach(att1) + # # 构造附件1，传送当前目录下的 test.txt 文件 + # att2 = MIMEApplication(open('b1.txt', 'rb').read()) + # # 这里的filename可以任意写，写什么名字，邮件中显示什么名字 + # att2.add_header('Content-Disposition', 'attachment', filename='b1.txt') + # msg.attach(att2) + #att3 = MIMEApplication(open('基金综合排名结果.txt', 'rb').read()) + # 这里的filename可以任意写，写什么名字，邮件中显示什么名字 + #att3.add_header('Content-Disposition', 'attachment', filename='b2.txt') + #msg.attach(att3) + smtpserver = 'smtp.126.com' + try: + smtp = smtplib.SMTP(smtpserver,25) + smtp.starttls() + smtp.login(username, password) + smtp.sendmail(sender, receiver, msg.as_string()) + smtp.quit() + print(u"邮件发送成功") + except smtplib.SMTPException, e: + print(u"Error: 无法发送邮件:" + str(e)) + smtp.quit() + try: + username = '15623863340@163.com' # input("请输入账号:") + password = '133499cug' # input("请输入密码:") + sender = username + # sender='' + # receiver = [ '1627041882@qq.com','994992333@qq.com','1847725033@qq.com'] # 'xxxxxxxxxx@qq.com','xxxxxxxxxx@126.com','994992333@qq.com','1847725033@qq.com' + msg['From'] = formataddr(['lishulin', sender]) # 括号里的对应发件人邮箱昵称、发件人邮箱账号 + msg['To'] = ",".join(receiver) # 括号里的对应收件人邮箱昵称、收件人邮箱账号 + smtpserver = 'smtp.163.com' + smtp = smtplib.SMTP(smtpserver,25) + smtp.starttls() + smtp.login(username, password) + for jj in receiver: + smtp.sendmail(sender, jj, msg.as_string()) + time.sleep(6) + smtp.quit() + print(u"邮件发送成功") + except smtplib.SMTPException, e1: + print(u"Error: 无法发送邮件:"+str(e1)) + smtp.quit() + try: + username = '15623863340@sina.com' # input("请输入账号:") + password = '133499' # input("请输入密码:") + sender = username + # sender='' + # receiver = [ '1627041882@qq.com','994992333@qq.com','1847725033@qq.com'] # 'xxxxxxxxxx@qq.com','xxxxxxxxxx@126.com','994992333@qq.com','1847725033@qq.com' + msg['From'] = formataddr(['15623863340@sina.com', sender]) # 括号里的对应发件人邮箱昵称、发件人邮箱账号 + msg['To'] = ",".join(receiver) # 括号里的对应收件人邮箱昵称、收件人邮箱账号 + smtpserver = 'smtp.sina.cn' + smtp = smtplib.SMTP(smtpserver,25) + smtp.starttls() + smtp.login(username, password) + smtp.sendmail(sender, receiver, msg.as_string()) + smtp.quit() + print(u"邮件发送成功") + except smtplib.SMTPException, e2: + print(u"Error: 无法发送邮件:" + str(e2)) + smtp.quit() +def deb_print(): + now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + H=now[9:11] + M = now[12:14] + S = now[15:] + # print now + print H,M, S + return H,M, S + +def check_time(H, M,S): + strtoday1 = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d') + sdatetime = datetime.datetime.strptime(strtoday1, '%Y-%m-%d') + sdatetime.isoweekday() + #if sdatetime.isoweekday() == 7: + if(H == "14" and int(M) == 30 and S == "20")and(sdatetime.isoweekday() != 7)and(sdatetime.isoweekday() != 6):#(H == "14" and M == "08" and S == "10") or + # itchat.auto_login(hotReload=True) + # itchat.run + # curTime = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + # itchat.send(curTime, 'filehelper') + # itchat.send(str(curTime), toUserName='@9f545f6d5c3f89aa63956c3f386733232f7176569f71a26f477909c02828d735') + buysell = main_run(all_fund_list) + print(str(buysell)) + sendmsg=[] + # f = open("buysell.txt",'a') + for i in buysell: + # write_str = str(i) + '\n' + # f.write(write_str) + st = '' + if i[1]['buy'] != []: + st = ' 买 ' + for j in i[1]['buy']: + st += str(j) + ' ' + elif i[1]['sell'] != []: + st += ' 卖 ' + for k in i[1]['sell']: + st += str(k) + ' ' + if st != '': + sendmsg.append(str(i[0]) + st+(' http://fund.eastmoney.com/%s.html'%i[0][0]) +(' http://www.efunds.com.cn/html/fund/%s_fundinfo.htm '%i[0][0])) + send_email(sendmsg) + # itchat.send(str(i[0]) + st+(' http://fund.eastmoney.com/%s.html'%i[0][0]) +('-- http://www.efunds.com.cn/html/fund/%s_fundinfo.htm'%i[0][0]), 'filehelper') + # itchat.send(str(i[0]) + st, toUserName='@9f545f6d5c3f89aa63956c3f386733232f7176569f71a26f477909c02828d735') + s.enter(1, 1, check_time, deb_print()) + + +def main1(): + now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + H = now[9:11] + M = now[12:14] + S = now[15:] + code = [['002963', 'egold'], ['003321', 'eoil'], ['004744', 'eGEI'], ['110003', 'eSSE50'], ['110020', 'HS300'], + ['110031', 'eHSI'], ['161130', 'eNASDAQ100'], ['110028', 'anxinB'], ['110022', 'eConsumption '], + ['161125', 'SPX500']] + all_fund_list1 = main_zf('All') + #sign=macdmain(code) + if (int(H)): # >= 19(H == "14" and M == "08" and S == "10") or + ibuysell1 = main_run(all_fund_list1[:80]) + print(str(ibuysell1)) + sendmsg = '股市有风险，投资需谨慎。模型难免有误，具体买卖需看下走势。1、根据MACD与MA均线模型获取近两三天内买卖信号如，[2018-01-29 00:, signal:, -1]，signal(1:买, -1:卖, 0:默认)，该模型有一定参考依据，但信号常反应较慢，需结合走势判断。 2、连续四五天涨跌与今日净值是否接近最大最小净值判断买卖信息，该模型在适合小涨小跌，大涨大跌时需谨慎。 \n' + sendmsg +='A股 https://www.baidu.com/s?tn=baidurt&rtt=1&bsst=1&wd=A%B9%C9&origin=ps'+ '\n' + sendmsg +='股票的走势比较、指标比较 http://quotes.money.163.com/tools/stockcomp/0000016,1399005,1399006,1399300,1399959.html'+ '\n' + sendmsg +='行业板块资金流向 http://data.eastmoney.com/bkzj/hy.html'+ '\n' + # f = open("buysell.txt",'a') + for j in ibuysell1[1]: + for m in j: + if len(m) > 0: + if m[2][3]> -35 : + sendmsg += str(m[0][0].encode("utf-8")) + ',' + str(m[0][1].encode("utf-8")) + ',' + str( + m[1]) + ',回测收益' + str(m[2][0]) + ',买卖：' + str(m[2][1][0]) + ',交易次数：' + str( + m[2][2]) + ',超额收益' + str(m[2][3]) + ',' + ( + ' http://fund.eastmoney.com/%s.html' % m[0][0]) + '\n' + sendmsg +='1、根据MACD与MA均线模型获取沪深股指近两三天内买卖信号 '+ '\n' + for n in ibuysell1[2] : + # for n in k: + if len(n) > 0 and n[2][3]> -30 and n[0][0][:3] == '000': + if n[2][3]> -30 and n[0][0][:3] == '000': + sendmsg += str(n[0][0].encode("utf-8")) + ',' + str(n[0][1].encode("utf-8")) + ',' + str( + n[1]) + ',回测收益' + str(n[2][0]) + ',买卖：' + str(n[2][1][0]) + ',交易次数：' + str( + n[2][2]) + ',超额收益' + str(n[2][3]) + ',' + ( + ' http://quotes.money.163.com/0%s.html' % n[0][0]) + '\n' + elif len(n) > 0 and n[2][3]> -30 and n[0][0][:3] == '399': + if n[2][3]> -30 and n[0][0][:3] == '399': + sendmsg += str(n[0][0].encode("utf-8")) + ',' + str(n[0][1].encode("utf-8")) + ',' + str( + n[1]) + ',回测收益' + str(n[2][0]) + ',买卖：' + str(n[2][1][0]) + ',交易次数：' + str( + n[2][2]) + ',超额收益' + str(n[2][3]) + ',' + ( + ' http://quotes.money.163.com/1%s.html' % n[0][0]) + '\n' + buysmg = '' + sellmsg='' + sendmsg +='2、连续四五天涨跌与今日净值是否接近最大最小净值判断买卖信息，该模型在适合小涨小跌，大涨大跌时需谨慎。\n' + for i in ibuysell1[0]: + # write_str = str(i) + '\n' + # f.write(write_str) + st = ' ' + if i[1]['buy'] != []: + st = ' 买 ' + for j in i[1]['buy']: + for h in j: + st += h + str(j[h]) + ' ' + elif i[1]['sell'] != []: + st += ' 卖 ' + for k in i[1]['sell']: + for t in k: + st += t + str(k[t]) + ' ' + if st != ' ' and '买'in st: + if i[2]=='code': + buysmg += i[0][0] + ',' + i[0][1] + st + (' http://fund.eastmoney.com/%s.html' % i[0][0]) + ( + ' http://www.efunds.com.cn/html/fund/%s_fundinfo.htm' % i[0][0]) + '\n' + else: + buysmg += i[0][0] + ',' + i[0][1] + st + (' http://fund.eastmoney.com/%s.html' % i[0][0]) + '\n' + if st != ' ' and '卖'in st: + if i[2] == 'code': + sellmsg += i[0][0] + ',' + i[0][1] + st + (' http://fund.eastmoney.com/%s.html' % i[0][0]) + ( + ' http://www.efunds.com.cn/html/fund/%s_fundinfo.htm' % i[0][0]) + '\n' + else: + sellmsg += i[0][0] + ',' + i[0][1] + st + (' http://fund.eastmoney.com/%s.html' % i[0][0]) + '\n' + sendmsg +=buysmg + sendmsg += sellmsg + print sendmsg + send_email(sendmsg) + # while True: + # now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + # # date = datetime.datetime.strftime(datetime.datetime.now(), "%Y%m%d") + # H = now[9:11] + # M = now[12:14] + # S = now[15:] + # # print H,S,M + # print 'Start:'+'13:05' +' and 14:30 '+'send to email.'+"\n" +'please wait:' + # + # # print time.localtime() + # # print time.strftime("%y-%m-%d %H:%M:%S",time.localtime()) + # # xiaozhao = Xiaozhao() + # # xiaozhao.send_email() + # s.enter(1, 1, check_time, deb_print()) + # s.run() + +if __name__=='__main__': + # itchat.auto_login(hotReload=True) + # itchat.run + # + # curTime = now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + # itchat.send(curTime, 'filehelper') + # buysell = one_predict.main_run() + # f = open("buysell.txt",'a') + # for i in buysell: + # # write_str = str(i) + '\n' + # # f.write(write_str) + # st = '' + # if i[1]['buy'] != []: + # st = u'买入点' + # for j in i[1]['buy']: + # st += str(j) + ' ' + # elif i[1]['sell'] != []: + # st += u' 卖出点' + # for k in i[1]['sell']: + # st += str(k) + ' ' + # if st != '': + # itchat.send(str(i[0]) + st, 'filehelper') + # itchat.send(str(i[0]) + st,toUserName='@9f545f6d5c3f89aa63956c3f386733232f7176569f71a26f477909c02828d735') + all_fund_list = main_zf() + main1() + # f.close() + # itchat.send("@fil@%s" % 'buysell.txt', 'filehelper') + # for (name, fund_id) in ids.items(): + # url = 'http://fund.eastmoney.com/%s.html' % fund_id + # value = spider(url) + # sign = value[0] + # value_num = value[1:-1] + # name = name.decode('utf-8') + # buysell=one_predict.main_run() + # itchat.send(buysell , 'filehelper') + # if(sign == '-' and float(value_num)>2.5): + # content = u'%s跌幅很大，可以补仓'+sign+value_num + # itchat.send(content % name, 'filehelper') + # else: + # content = u'%s 不需要补仓'+sign+value_num + # itchat.send(content % name, 'filehelper') diff --git a/trade_process/efund_mail2.pyc b/trade_process/efund_mail2.pyc new file mode 100644 index 0000000..7d0f649 Binary files /dev/null and b/trade_process/efund_mail2.pyc differ diff --git a/trade_process/efund_mail2_BACKUP_5352.py b/trade_process/efund_mail2_BACKUP_5352.py new file mode 100644 index 0000000..83b8b85 --- /dev/null +++ b/trade_process/efund_mail2_BACKUP_5352.py @@ -0,0 +1,582 @@ +#!/usr/bin/python2.7 +# -*- coding: utf-8 -*- +# from url_info import * +# from spider import * +#import itchat +#from sleep import * +# import datetime +# import one_predict +import json,time +import sched + +import datetime +import re +# import sys +import urllib2 +import smtplib +from email.mime.text import MIMEText +from email.mime.multipart import MIMEMultipart +from email.utils import formataddr +from email.mime.application import MIMEApplication +from fund_zf import main_zf +# from strategy.macd_back_test import macdmain +# from numpy import mean, ptp, var, std + +import requests +from bs4 import BeautifulSoup + + +def spider(url): + r = requests.get(url) + page = r.text + soup = BeautifulSoup(page, 'html.parser') + value = soup.find_all(id='gz_gszzl') + value = value[0].get_text().replace('%','') + value=value.encode('utf-8') + value1 = soup.find_all(id='gz_gsz') + value1 = value1[0].get_text().encode('utf-8') + try: + return [float(value1),float(value1),float(value)] + except ValueError as e: + return +# 是否已在list中 +def get_index(fund_code, all_fund_list): + fund_num = len(all_fund_list) + fund_index = 0 + while fund_index < fund_num: + if fund_code in all_fund_list[fund_index]: + break; + fund_index += 1 + return fund_index + + +# 获取基金类型 +def get_type(fund_code, all_fund_list): + fund_type = 'none' + for fund in all_fund_list: + if fund_code in fund: + fund_type = fund[3] + break + + return fund_type + +# 获取某一基金在某一日的累计净值数据 +def get_jingzhi(strfundcode, strdate,stredate): + try: + url = 'http://fund.eastmoney.com/f10/F10DataApi.aspx?type=lsjz&code=' + str(strfundcode) + '&page=1&per=1000&sdate=' + str(strdate) + '&edate=' + str(stredate) + print u'数据链接：'+url + '\n' + response = urllib2.urlopen(url) + except urllib2.HTTPError, e: + print e + urllib_error_tag = True + except StandardError, e: + print e + urllib_error_tag = True + else: + urllib_error_tag = False + + if urllib_error_tag == True: + return '-1' + + json_fund_value = response.read().decode('utf-8') + # print json_fund_value + + tr_re = re.compile(r'(.*?)') + item_re = re.compile( + r'''(\d{4}-\d{2}-\d{2})(.*?)(.*?)(.*?)(.*?)(.*?)''', + re.X) + + # 获取不到 返回0 + jingzhi ='-1' + result=[] + + for line in tr_re.findall(json_fund_value): + # print line + '\n' + match = item_re.match(line) + if match: + entry = match.groups() + entry[3].rstrip('%') + date = datetime.datetime.strptime(entry[0], '%Y-%m-%d') + # jingzhi = entry[2] + if entry[3]!='': + result.append([date, float(entry[1]),float(entry[2]), float(entry[3].rstrip('%'))]) + else: + result.append([date, float(entry[1]), float(entry[2]), 0]) + jingzhi1 = entry[1] + jingzhi2 = entry[2] + # print jingzhi2 + + if jingzhi2.strip() == '': + # 040028 + # 净值日期 每万份收益 7日年化收益率（%） 申购状态 赎回状态 分红送配 + # 2017-01-06 1.4414 暂停申购 暂停赎回 + # 2017-01-05 1.4369 暂停申购 暂停赎回 + jingzhi = '-1' + elif jingzhi2.find('%') > -1: + # 040003 + # 净值日期 每万份收益 7日年化收益率（%） 申购状态 赎回状态 分红送配 + # 2017-03-27 1.1149 3.9450% 限制大额申购 开放赎回 + # 2017-03-26* 2.2240 3.8970% 限制大额申购 开放赎回 + jingzhi = '-1' + elif float(jingzhi1) > float(jingzhi2): + # 502015 + # 净值日期 单位净值 累计净值 日增长率 申购状态 赎回状态 分红送配 + # 2017-03-27 0.6980 0.3785 -2.24% 场内买入 场内卖出 + # 2017-03-24 0.7140 0.3945 5.15% 场内买入 场内卖出 + jingzhi = entry[1] + else: + # + # 净值日期 单位净值 累计净值 日增长率 申购状态 赎回状态 分红送配 + # 2017-03-28 1.7720 1.7720 -0.23% 开放申购 开放赎回 + # 2017-03-27 1.7761 1.7761 -0.43% 开放申购 开放赎回 + jingzhi = entry[2] + return result + +#获取历史数据 +def get_histrydata(strfundcode,numdays): + # 当前日期 + strtoday = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d') + tdatetime = datetime.datetime.strptime(strtoday, '%Y-%m-%d') + print u'结束时间：' + strtoday + url = 'http://fund.eastmoney.com/%s.html' % strfundcode[0] + todayvalue = spider(url) + + # 昨天 + yestodaytime = tdatetime - datetime.timedelta(days=1) + yestoday = datetime.datetime.strftime(yestodaytime, '%Y-%m-%d') + + # 前年今日 + sdatetime = tdatetime - datetime.timedelta(days=numdays) + strsdate = datetime.datetime.strftime(sdatetime, '%Y-%m-%d') + print u'开始时间：' + strsdate + + # 1.1 起始时间 + strsdate = strsdate # sys.argv[1] + stredate = yestoday # sys.argv[2] + + # 今日零时 + # strtoday = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d') + # tdatetime = datetime.datetime.strptime(strtoday, '%Y-%m-%d') + # print tdatetime + + # 开始时间 如果是周六 周日 调整到周五 + # print strsdate + sdatetime = datetime.datetime.strptime(strsdate, '%Y-%m-%d') + sdatetime.isoweekday() + if sdatetime.isoweekday() == 7: + sdatetime = sdatetime + datetime.timedelta(days=-2) + elif sdatetime.isoweekday() == 6: + sdatetime = sdatetime + datetime.timedelta(days=-1) + + strsdate = datetime.datetime.strftime(sdatetime, '%Y-%m-%d') + # print strsdate + + # 结束时间 如果是周六 周日 调整到周五 + # print stredate + edatetime = datetime.datetime.strptime(stredate, '%Y-%m-%d') + edatetime.isoweekday() + if edatetime.isoweekday() == 7: + edatetime = edatetime + datetime.timedelta(days=-2) + elif edatetime.isoweekday() == 6: + edatetime = edatetime + datetime.timedelta(days=-1) + + stredate = datetime.datetime.strftime(edatetime, '%Y-%m-%d') + # print stredate + + # 判断时间段 今日净值要下午才出 一律不处理 + if edatetime <= sdatetime or tdatetime <= sdatetime or tdatetime <= edatetime: + print '判断时间段 今日净值要下午才出 一律不处理' + print 'date input error!\n' + + jingzhimin = get_jingzhi(strfundcode[0], strsdate, stredate) + if todayvalue != None: + jingzhimin.insert(0, [tdatetime, todayvalue[0], todayvalue[1], todayvalue[2]]) + return jingzhimin + +#计算交易策略 +def fenxi(strfundcode,numdays): + #获取历史数据 + jingzhimin=get_histrydata(strfundcode,numdays) + years = [x[0] for x in jingzhimin] + price = [x[1] for x in jingzhimin] + changeprice = [x[3]/x[1] for x in jingzhimin] + price_stre = [((x[1] - (sum(price)/len(price)))/(max(price)-min(price)))*(max(changeprice)-min(changeprice)) for x in jingzhimin] + # plt.plot(years, changeprice, 'g',label='change') + starty = [0 for x in jingzhimin] + jizhipoints=jizhi(jingzhimin) + celue_point=celue(jingzhimin,jizhipoints) + # change_fenxi(change5days) + return celue_point + +def stdDeviation(a): + l = len(a) + m = sum(a) / l + d = 0 + for i in a: d += (i - m) ** 2 + return (d * (1 / l)) ** 0.5 + +#统计 +# def caculate(data): +# # 极差 +# ptp(data) +# # 方差 +# var(data) +# # 标准差 +# std(data) +# # 变异系数 +# mean(data) / std(data) +# norm.ppf(0.05, mean(data), std(data)) + +#求极值 +def jizhi(funddata): + jizhipoints={} + alldata = [x[1] for x in funddata[::-2]] + jizhipoints['allmax']= [alldata.index(max(alldata)),max(alldata)] + jizhipoints['allmin']= [alldata.index(min(alldata)),min(alldata)] + data30days = [x[1] for x in funddata[:-30:-2]] + jizhipoints['30daysmax'] = [data30days.index(max(data30days)),max(data30days)] + jizhipoints['30daysmin'] = [data30days.index(min(data30days)),min(data30days)] + data180days = [x[1] for x in funddata[:-180:-2]] + jizhipoints['180daysmax'] = [data180days.index(max(data180days)), max(data180days)] + jizhipoints['180daysmin'] = [data180days.index(min(data180days)), min(data180days)] + data5days = [x[1] for x in funddata[:-5:-2]] + jizhipoints['5daysmax'] = [data5days.index(max(data5days)), max(data5days)] + jizhipoints['5daysmin'] = [data5days.index(min(data5days)), min(data5days)] + return jizhipoints + +#交易策略监测 +def celue(funddata,jizhipoints): + todaydata = funddata[0] + data5days = [x[1] for x in funddata[:6:1]] + change = [x[3] for x in funddata[:5:1]] + change7 = [x[3] for x in funddata[:7:1]] + change12days= [x[3] for x in funddata[:90:1]] + if stdDeviation(change12days)<0.3: + r=1.005;rmax=0.995 + elif stdDeviation(change12days) > 0.6: + r = 1.005;rmax = 0.995 + else: + r = 1.005;rmax = 0.995 + buy_points = [] + sell_point = [] + + #根据净值比较 + if(todaydata[1]<=r*jizhipoints['allmin'][1]): + buy_points.append({'today<1.02*allmin':str(todaydata[1])+str('<=')+str(r*jizhipoints['allmin'][1])}) + elif(todaydata[1]<=r*jizhipoints['180daysmin'][1]): + buy_points.append({'today<1.02*180min': str(todaydata[1])+str('<=')+str(r*jizhipoints['180daysmin'][1])}) + # elif(todaydata[1]<=r*jizhipoints['30daysmin'][1]): + # buy_points.append({'today<1.02*30min': str(todaydata[1] )+str('<=')+str( r*jizhipoints['30daysmin'][1])}) + # elif (todaydata[1] <= r*jizhipoints['5daysmin'][1]): + # buy_points['today<1.02*5min'] = str(todaydata[1] )+str('<=')+str( r*jizhipoints['5daysmin'][1]) + #---------------------------- + elif (todaydata[1] >= rmax*jizhipoints['allmax'][1]): + sell_point.append({'today>0.98*allmax': str(todaydata[1] )+str('>=')+str( rmax*jizhipoints['allmax'][1])}) + elif (todaydata[1] >= rmax*jizhipoints['180daysmax'][1]): + sell_point.append({'today>0.98*180max': str(todaydata[1] )+str('>=')+str( rmax*jizhipoints['180daysmax'][1])}) + # elif (todaydata[1] >= rmax*jizhipoints['30daysmax'][1]): + # sell_point.append({'today>0.9*30max': str(todaydata[1] )+str('<=')+str( rmax*jizhipoints['30daysmax'][1])}) + # elif (todaydata[1] >= rmax*jizhipoints['5daysmax'][1]): + # sell_point.append({'today>0.9*30max': str(todaydata[1] )+str('<=')+str( rmax*jizhipoints['5daysmax'][1])}) + + # def change_fenxi(change): + if(sum(change)/len(change)>=0.46): + sell_point.append({'mean5up':round(sum(change)/len(change),5)}) + elif(sum(change)/len(change)<=-0.46): + buy_points.append({'mean5up':round(sum(change)/len(change),5)}) + + big=[];small=[] + if len(change)>3: + for i in range(len(change)): + if (change[i]>0): + big.append(change[i]) + elif (change[i]<0): + small.append(change[i]) + if len(big)>=4 and change[0]<0.2 : + sell_point.append({'go up 4days':change7}) + # elif (change[0]=3): + # sell_point.append({u'go up 4days': big}) + if (len(small)>=4 and change[0]>0) or (len(small)>=3 and change[0]<-0.5 and change[1]<0.1): + buy_points.append({'go down 3days':change7}) + return {'sell':sell_point,'buy':buy_points} + +def main_run(all_fund_list): + code = [['002963', 'egold'], ['003321', 'eoil'], ['004744', 'eGEI'], ['110003', 'eSSE50'], ['110020', 'HS300'], + ['110031', 'eHSI'], ['161130', 'eNASDAQ100'], ['110028', 'anxinB'], ['110022', 'eConsumption '], + ['161125', 'SPX500']] + + buysell = [] + for i in code: + # save(strfundcode=i ,numdays=365*1) + sb=fenxi(strfundcode=i, numdays=365 * 1) + if sb: + buysell.append([i,sb]) + for i in all_fund_list[0:50]: + # save(strfundcode=i ,numdays=365*1) + sb=fenxi(strfundcode=i, numdays=365 * 1) + if sb: + buysell.append([i,sb]) + return buysell + +s = sched.scheduler(time.time, time.sleep) +class DateEncoder(json.JSONEncoder): + def default(self, obj): + if isinstance(obj, datetime): + return obj.__str__() + return json.JSONEncoder.default(self, obj) + + # sleep() +#@itchat.msg_register(itchat.content.TEXT) +def print_content(msg): + print msg['Text'] +def readmsg(msg): + return msg +def send_email(text): + #text = self.claw_content() + #open('b.txt', 'w').write(str(text[1]) + '\n' + str(text[0]) + '\n') + username = 'lsl_cug@126.com' # input("请输入账号:") + password = '123456lsl' # input("请输入密码:") + sender = username + # sender='' + receiver = ['1627041882@qq.com','760140853@qq.com'] # '760140853@qq.com','xxxxxxxxxx@qq.com','xxxxxxxxxx@126.com','994992333@qq.com','1847725033@qq.com','1847725033@qq.com','849281511@qq.com' + if sender =='': + username = str(raw_input("Please Input Sender Email Address,for example:xxxxxxxxxx@126.com \n")) + sender = username + password = str(raw_input("Please Input Sender Password \n")) + receiver.append(str(raw_input("Please Input Receiver Address,for example:xxxxxxxxxx@qq.com \n"))) + iRec = 1 + while iRec>0: + CmdRec = ''#raw_input("Whether you want to add another Receiver Address(default n)? \n") + if CmdRec == 'y' or CmdRec == 'yes': + iRec = 1 + receiver.append(str(raw_input("Please Input Receiver Address,for example:xxxxxxxxxx@qq.com \n"))) + else: + iRec = 0 + + ConfirmRec = ''#raw_input("Confirm? \n") + if ConfirmRec == 'n' or ConfirmRec == 'no': + print("What Do You Want? Maybe run this program again.\n") + exit(1) + else: + for rece in receiver: + print("OK \n"+ username + " sendto " + rece) + # 创建一个带附件的实例 + msg = MIMEMultipart() + # msg = MIMEText(str(text), 'plain', 'utf-8') + msg['From'] = formataddr(['user', sender]) # 括号里的对应发件人邮箱昵称、发件人邮箱账号 + msg['To'] = ",".join(receiver) # 括号里的对应收件人邮箱昵称、收件人邮箱账号 + + subject_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + subject = '基金信息' + msg['Subject'] = subject + + # 邮件正文内容 + # print str(text[0]) + realText = text#sendmsg #str(text) #'\n'+str(subject_time)+'：主人，有人来招人啦！^—^\n'+ '海投网：'+str(text[1])+'.'#+str(text[0])#'地大就业网招聘公告：'+str(text1[1])+'地大就业网gis等招聘信息：'+str(text2[1])+'.'#+str(text1[0])+'\n'+str(text2[1])+'\n'+str(text2[0]) + print realText + msg.attach(MIMEText(realText, 'plain', 'utf-8')) + + # 构造附件1，传送当前目录下的 test.txt 文件 + # att1 = MIMEApplication(open('b.txt', 'rb').read()) + # 这里的filename可以任意写，写什么名字，邮件中显示什么名字 + # att1.add_header('Content-Disposition', 'attachment', filename='hjx.txt') + # msg.attach(att1) + # # 构造附件1，传送当前目录下的 test.txt 文件 + # att2 = MIMEApplication(open('b1.txt', 'rb').read()) + # # 这里的filename可以任意写，写什么名字，邮件中显示什么名字 + # att2.add_header('Content-Disposition', 'attachment', filename='b1.txt') + # msg.attach(att2) + # att3 = MIMEApplication(open('b2.txt', 'rb').read()) + # # 这里的filename可以任意写，写什么名字，邮件中显示什么名字 + # att3.add_header('Content-Disposition', 'attachment', filename='b2.txt') + # msg.attach(att3) + smtpserver = 'smtp.126.com' + try: + smtp = smtplib.SMTP(smtpserver,25) + smtp.starttls() + smtp.login(username, password) + smtp.sendmail(sender, receiver, msg.as_string()) + smtp.quit() + print(u"邮件发送成功") + except smtplib.SMTPException, e: + print(u"Error: 无法发送邮件:" + str(e)) + smtp.quit() + try: + username = 'lishulincug@163.com' # input("请输入账号:") + password = '123456lsl' # input("请输入密码:") + sender = username + # sender='' + # receiver = [ '1627041882@qq.com','994992333@qq.com','1847725033@qq.com'] # 'xxxxxxxxxx@qq.com','xxxxxxxxxx@126.com','994992333@qq.com','1847725033@qq.com' + msg['From'] = formataddr(['lishulin', sender]) # 括号里的对应发件人邮箱昵称、发件人邮箱账号 + msg['To'] = ",".join(receiver) # 括号里的对应收件人邮箱昵称、收件人邮箱账号 + smtpserver = 'smtp.163.com' + smtp = smtplib.SMTP(smtpserver,25) + smtp.starttls() + smtp.login(username, password) + for jj in receiver: + smtp.sendmail(sender, jj, msg.as_string()) + time.sleep(35) + smtp.quit() + print(u"邮件发送成功") + except smtplib.SMTPException, e1: + print(u"Error: 无法发送邮件:"+str(e1)) + smtp.quit() + try: + username = '15623863340@sina.cn' # input("请输入账号:") + password = '133499' # input("请输入密码:") + sender = username + # sender='' + # receiver = [ '1627041882@qq.com','994992333@qq.com','1847725033@qq.com'] # 'xxxxxxxxxx@qq.com','xxxxxxxxxx@126.com','994992333@qq.com','1847725033@qq.com' + msg['From'] = formataddr(['15623863340@sina.cn', sender]) # 括号里的对应发件人邮箱昵称、发件人邮箱账号 + msg['To'] = ",".join(receiver) # 括号里的对应收件人邮箱昵称、收件人邮箱账号 + smtpserver = 'smtp.sina.cn' + smtp = smtplib.SMTP(smtpserver,25) + smtp.starttls() + smtp.login(username, password) + smtp.sendmail(sender, receiver, msg.as_string()) + smtp.quit() + print(u"邮件发送成功") + except smtplib.SMTPException, e2: + print(u"Error: 无法发送邮件:" + str(e2)) + smtp.quit() +def deb_print(): + now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + H=now[9:11] + M = now[12:14] + S = now[15:] + # print now + print H,M, S + return H,M, S + +def check_time(H, M,S): + strtoday1 = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d') + sdatetime = datetime.datetime.strptime(strtoday1, '%Y-%m-%d') + sdatetime.isoweekday() + #if sdatetime.isoweekday() == 7: + if(H == "14" and int(M) == 30 and S == "20")and(sdatetime.isoweekday() != 7)and(sdatetime.isoweekday() != 6):#(H == "14" and M == "08" and S == "10") or + # itchat.auto_login(hotReload=True) + # itchat.run + # curTime = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + # itchat.send(curTime, 'filehelper') + # itchat.send(str(curTime), toUserName='@9f545f6d5c3f89aa63956c3f386733232f7176569f71a26f477909c02828d735') + buysell = main_run(all_fund_list) + print(str(buysell)) + sendmsg=[] + # f = open("buysell.txt",'a') + for i in buysell: + # write_str = str(i) + '\n' + # f.write(write_str) + st = '' + if i[1]['buy'] != []: + st = ' 买 ' + for j in i[1]['buy']: + st += str(j) + ' ' + elif i[1]['sell'] != []: + st += ' 卖 ' + for k in i[1]['sell']: + st += str(k) + ' ' + if st != '': + sendmsg.append(str(i[0]) + st+(' http://fund.eastmoney.com/%s.html'%i[0][0]) +(' http://www.efunds.com.cn/html/fund/%s_fundinfo.htm '%i[0][0])) + send_email(sendmsg) + # itchat.send(str(i[0]) + st+(' http://fund.eastmoney.com/%s.html'%i[0][0]) +('-- http://www.efunds.com.cn/html/fund/%s_fundinfo.htm'%i[0][0]), 'filehelper') + # itchat.send(str(i[0]) + st, toUserName='@9f545f6d5c3f89aa63956c3f386733232f7176569f71a26f477909c02828d735') + s.enter(1, 1, check_time, deb_print()) + + +def main1(sign): + now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + H = now[9:11] + M = now[12:14] + S = now[15:] + code = [['002963', 'egold'], ['003321', 'eoil'], ['004744', 'eGEI'], ['110003', 'eSSE50'], ['110020', 'HS300'], + ['110031', 'eHSI'], ['161130', 'eNASDAQ100'], ['110028', 'anxinB'], ['110022', 'eConsumption '], + ['161125', 'SPX500']] + all_fund_list = main_zf() + #sign=macdmain(code) + if (int(H)): # >= 19(H == "14" and M == "08" and S == "10") or + buysell1 = main_run(all_fund_list) + print(str(buysell1)) + sendmsg = '' + # f = open("buysell.txt",'a') + for j in sign: +<<<<<<< Updated upstream + #sendmsg +=str(j[0])+','+str(j[1])+','+str(j[2][0])+','+str(j[2][1][0])+'\n' +======= +>>>>>>> Stashed changes + sendmsg +=str(j[0])+','+str(j[1])+','+str(j[2][0])+','+str(j[2][1][0])+','+ (' http://fund.eastmoney.com/%s.html' % j[0][0])+'\n' + for i in buysell1: + # write_str = str(i) + '\n' + # f.write(write_str) + st = ' ' + if i[1]['buy'] != []: + st = ' 买 ' + for j in i[1]['buy']: + for h in j: + st += h + str(j[h]) + ' ' + elif i[1]['sell'] != []: + st += ' 卖 ' + for k in i[1]['sell']: + for t in k: + st += t + str(k[t]) + ' ' + if st != ' ': + sendmsg += i[0][0] + ',' + i[0][1] + st + (' http://fund.eastmoney.com/%s.html' % i[0][0]) + ( + ' http://www.efunds.com.cn/html/fund/%s_fundinfo.htm' % i[0][0]) + '\n' + # print sendmsg + send_email(sendmsg) + while True: + now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + # date = datetime.datetime.strftime(datetime.datetime.now(), "%Y%m%d") + H = now[9:11] + M = now[12:14] + S = now[15:] + # print H,S,M + print 'Start:'+'13:05' +' and 14:30 '+'send to email.'+"\n" +'please wait:' + + # print time.localtime() + # print time.strftime("%y-%m-%d %H:%M:%S",time.localtime()) + # xiaozhao = Xiaozhao() + # xiaozhao.send_email() + s.enter(1, 1, check_time, deb_print()) + s.run() + +if __name__=='__main__': + # itchat.auto_login(hotReload=True) + # itchat.run + # + # curTime = now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + # itchat.send(curTime, 'filehelper') + # buysell = one_predict.main_run() + # f = open("buysell.txt",'a') + # for i in buysell: + # # write_str = str(i) + '\n' + # # f.write(write_str) + # st = '' + # if i[1]['buy'] != []: + # st = u'买入点' + # for j in i[1]['buy']: + # st += str(j) + ' ' + # elif i[1]['sell'] != []: + # st += u' 卖出点' + # for k in i[1]['sell']: + # st += str(k) + ' ' + # if st != '': + # itchat.send(str(i[0]) + st, 'filehelper') + # itchat.send(str(i[0]) + st,toUserName='@9f545f6d5c3f89aa63956c3f386733232f7176569f71a26f477909c02828d735') + all_fund_list = main_zf() + main1() + # f.close() + # itchat.send("@fil@%s" % 'buysell.txt', 'filehelper') + # for (name, fund_id) in ids.items(): + # url = 'http://fund.eastmoney.com/%s.html' % fund_id + # value = spider(url) + # sign = value[0] + # value_num = value[1:-1] + # name = name.decode('utf-8') + # buysell=one_predict.main_run() + # itchat.send(buysell , 'filehelper') + # if(sign == '-' and float(value_num)>2.5): + # content = u'%s跌幅很大，可以补仓'+sign+value_num + # itchat.send(content % name, 'filehelper') + # else: + # content = u'%s 不需要补仓'+sign+value_num + # itchat.send(content % name, 'filehelper') diff --git a/trade_process/efund_mail2_BASE_5352.py b/trade_process/efund_mail2_BASE_5352.py new file mode 100644 index 0000000..f653d2e --- /dev/null +++ b/trade_process/efund_mail2_BASE_5352.py @@ -0,0 +1,578 @@ +#!/usr/bin/python2.7 +# -*- coding: utf-8 -*- +# from url_info import * +# from spider import * +#import itchat +#from sleep import * +# import datetime +# import one_predict +import json,time +import sched + +import datetime +import re +# import sys +import urllib2 +import smtplib +from email.mime.text import MIMEText +from email.mime.multipart import MIMEMultipart +from email.utils import formataddr +from email.mime.application import MIMEApplication +from fund_zf import main_zf +from trade_process.strategy.macd_back_test import macdmain +# from numpy import mean, ptp, var, std + +import requests +from bs4 import BeautifulSoup + + +def spider(url): + r = requests.get(url) + page = r.text + soup = BeautifulSoup(page, 'html.parser') + value = soup.find_all(id='gz_gszzl') + value = value[0].get_text().replace('%','') + value=value.encode('utf-8') + value1 = soup.find_all(id='gz_gsz') + value1 = value1[0].get_text().encode('utf-8') + try: + return [float(value1),float(value1),float(value)] + except ValueError as e: + return +# 是否已在list中 +def get_index(fund_code, all_fund_list): + fund_num = len(all_fund_list) + fund_index = 0 + while fund_index < fund_num: + if fund_code in all_fund_list[fund_index]: + break; + fund_index += 1 + return fund_index + + +# 获取基金类型 +def get_type(fund_code, all_fund_list): + fund_type = 'none' + for fund in all_fund_list: + if fund_code in fund: + fund_type = fund[3] + break + + return fund_type + +# 获取某一基金在某一日的累计净值数据 +def get_jingzhi(strfundcode, strdate,stredate): + try: + url = 'http://fund.eastmoney.com/f10/F10DataApi.aspx?type=lsjz&code=' + str(strfundcode) + '&page=1&per=1000&sdate=' + str(strdate) + '&edate=' + str(stredate) + print u'数据链接：'+url + '\n' + response = urllib2.urlopen(url) + except urllib2.HTTPError, e: + print e + urllib_error_tag = True + except StandardError, e: + print e + urllib_error_tag = True + else: + urllib_error_tag = False + + if urllib_error_tag == True: + return '-1' + + json_fund_value = response.read().decode('utf-8') + # print json_fund_value + + tr_re = re.compile(r'(.*?)') + item_re = re.compile( + r'''(\d{4}-\d{2}-\d{2})(.*?)(.*?)(.*?)(.*?)(.*?)''', + re.X) + + # 获取不到 返回0 + jingzhi ='-1' + result=[] + + for line in tr_re.findall(json_fund_value): + # print line + '\n' + match = item_re.match(line) + if match: + entry = match.groups() + entry[3].rstrip('%') + date = datetime.datetime.strptime(entry[0], '%Y-%m-%d') + # jingzhi = entry[2] + if entry[3]!='': + result.append([date, float(entry[1]),float(entry[2]), float(entry[3].rstrip('%'))]) + else: + result.append([date, float(entry[1]), float(entry[2]), 0]) + jingzhi1 = entry[1] + jingzhi2 = entry[2] + # print jingzhi2 + + if jingzhi2.strip() == '': + # 040028 + # 净值日期 每万份收益 7日年化收益率（%） 申购状态 赎回状态 分红送配 + # 2017-01-06 1.4414 暂停申购 暂停赎回 + # 2017-01-05 1.4369 暂停申购 暂停赎回 + jingzhi = '-1' + elif jingzhi2.find('%') > -1: + # 040003 + # 净值日期 每万份收益 7日年化收益率（%） 申购状态 赎回状态 分红送配 + # 2017-03-27 1.1149 3.9450% 限制大额申购 开放赎回 + # 2017-03-26* 2.2240 3.8970% 限制大额申购 开放赎回 + jingzhi = '-1' + elif float(jingzhi1) > float(jingzhi2): + # 502015 + # 净值日期 单位净值 累计净值 日增长率 申购状态 赎回状态 分红送配 + # 2017-03-27 0.6980 0.3785 -2.24% 场内买入 场内卖出 + # 2017-03-24 0.7140 0.3945 5.15% 场内买入 场内卖出 + jingzhi = entry[1] + else: + # + # 净值日期 单位净值 累计净值 日增长率 申购状态 赎回状态 分红送配 + # 2017-03-28 1.7720 1.7720 -0.23% 开放申购 开放赎回 + # 2017-03-27 1.7761 1.7761 -0.43% 开放申购 开放赎回 + jingzhi = entry[2] + return result + +#获取历史数据 +def get_histrydata(strfundcode,numdays): + # 当前日期 + strtoday = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d') + tdatetime = datetime.datetime.strptime(strtoday, '%Y-%m-%d') + print u'结束时间：' + strtoday + url = 'http://fund.eastmoney.com/%s.html' % strfundcode[0] + todayvalue = spider(url) + + # 昨天 + yestodaytime = tdatetime - datetime.timedelta(days=1) + yestoday = datetime.datetime.strftime(yestodaytime, '%Y-%m-%d') + + # 前年今日 + sdatetime = tdatetime - datetime.timedelta(days=numdays) + strsdate = datetime.datetime.strftime(sdatetime, '%Y-%m-%d') + print u'开始时间：' + strsdate + + # 1.1 起始时间 + strsdate = strsdate # sys.argv[1] + stredate = yestoday # sys.argv[2] + + # 今日零时 + # strtoday = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d') + # tdatetime = datetime.datetime.strptime(strtoday, '%Y-%m-%d') + # print tdatetime + + # 开始时间 如果是周六 周日 调整到周五 + # print strsdate + sdatetime = datetime.datetime.strptime(strsdate, '%Y-%m-%d') + sdatetime.isoweekday() + if sdatetime.isoweekday() == 7: + sdatetime = sdatetime + datetime.timedelta(days=-2) + elif sdatetime.isoweekday() == 6: + sdatetime = sdatetime + datetime.timedelta(days=-1) + + strsdate = datetime.datetime.strftime(sdatetime, '%Y-%m-%d') + # print strsdate + + # 结束时间 如果是周六 周日 调整到周五 + # print stredate + edatetime = datetime.datetime.strptime(stredate, '%Y-%m-%d') + edatetime.isoweekday() + if edatetime.isoweekday() == 7: + edatetime = edatetime + datetime.timedelta(days=-2) + elif edatetime.isoweekday() == 6: + edatetime = edatetime + datetime.timedelta(days=-1) + + stredate = datetime.datetime.strftime(edatetime, '%Y-%m-%d') + # print stredate + + # 判断时间段 今日净值要下午才出 一律不处理 + if edatetime <= sdatetime or tdatetime <= sdatetime or tdatetime <= edatetime: + print '判断时间段 今日净值要下午才出 一律不处理' + print 'date input error!\n' + + jingzhimin = get_jingzhi(strfundcode[0], strsdate, stredate) + if todayvalue != None: + jingzhimin.insert(0, [tdatetime, todayvalue[0], todayvalue[1], todayvalue[2]]) + return jingzhimin + +#计算交易策略 +def fenxi(strfundcode,numdays): + #获取历史数据 + jingzhimin=get_histrydata(strfundcode,numdays) + years = [x[0] for x in jingzhimin] + price = [x[1] for x in jingzhimin] + changeprice = [x[3]/x[1] for x in jingzhimin] + price_stre = [((x[1] - (sum(price)/len(price)))/(max(price)-min(price)))*(max(changeprice)-min(changeprice)) for x in jingzhimin] + # plt.plot(years, changeprice, 'g',label='change') + starty = [0 for x in jingzhimin] + jizhipoints=jizhi(jingzhimin) + celue_point=celue(jingzhimin,jizhipoints) + # change_fenxi(change5days) + return celue_point + +def stdDeviation(a): + l = len(a) + m = sum(a) / l + d = 0 + for i in a: d += (i - m) ** 2 + return (d * (1 / l)) ** 0.5 + +#统计 +# def caculate(data): +# # 极差 +# ptp(data) +# # 方差 +# var(data) +# # 标准差 +# std(data) +# # 变异系数 +# mean(data) / std(data) +# norm.ppf(0.05, mean(data), std(data)) + +#求极值 +def jizhi(funddata): + jizhipoints={} + alldata = [x[1] for x in funddata[::-2]] + jizhipoints['allmax']= [alldata.index(max(alldata)),max(alldata)] + jizhipoints['allmin']= [alldata.index(min(alldata)),min(alldata)] + data30days = [x[1] for x in funddata[:-30:-2]] + jizhipoints['30daysmax'] = [data30days.index(max(data30days)),max(data30days)] + jizhipoints['30daysmin'] = [data30days.index(min(data30days)),min(data30days)] + data180days = [x[1] for x in funddata[:-180:-2]] + jizhipoints['180daysmax'] = [data180days.index(max(data180days)), max(data180days)] + jizhipoints['180daysmin'] = [data180days.index(min(data180days)), min(data180days)] + data5days = [x[1] for x in funddata[:-5:-2]] + jizhipoints['5daysmax'] = [data5days.index(max(data5days)), max(data5days)] + jizhipoints['5daysmin'] = [data5days.index(min(data5days)), min(data5days)] + return jizhipoints + +#交易策略监测 +def celue(funddata,jizhipoints): + todaydata = funddata[0] + data5days = [x[1] for x in funddata[:6:1]] + change = [x[3] for x in funddata[:5:1]] + change7 = [x[3] for x in funddata[:7:1]] + change12days= [x[3] for x in funddata[:90:1]] + if stdDeviation(change12days)<0.3: + r=1.005;rmax=0.995 + elif stdDeviation(change12days) > 0.6: + r = 1.005;rmax = 0.995 + else: + r = 1.005;rmax = 0.995 + buy_points = [] + sell_point = [] + + #根据净值比较 + if(todaydata[1]<=r*jizhipoints['allmin'][1]): + buy_points.append({'today<1.02*allmin':str(todaydata[1])+str('<=')+str(r*jizhipoints['allmin'][1])}) + elif(todaydata[1]<=r*jizhipoints['180daysmin'][1]): + buy_points.append({'today<1.02*180min': str(todaydata[1])+str('<=')+str(r*jizhipoints['180daysmin'][1])}) + # elif(todaydata[1]<=r*jizhipoints['30daysmin'][1]): + # buy_points.append({'today<1.02*30min': str(todaydata[1] )+str('<=')+str( r*jizhipoints['30daysmin'][1])}) + # elif (todaydata[1] <= r*jizhipoints['5daysmin'][1]): + # buy_points['today<1.02*5min'] = str(todaydata[1] )+str('<=')+str( r*jizhipoints['5daysmin'][1]) + #---------------------------- + elif (todaydata[1] >= rmax*jizhipoints['allmax'][1]): + sell_point.append({'today>0.98*allmax': str(todaydata[1] )+str('>=')+str( rmax*jizhipoints['allmax'][1])}) + elif (todaydata[1] >= rmax*jizhipoints['180daysmax'][1]): + sell_point.append({'today>0.98*180max': str(todaydata[1] )+str('>=')+str( rmax*jizhipoints['180daysmax'][1])}) + # elif (todaydata[1] >= rmax*jizhipoints['30daysmax'][1]): + # sell_point.append({'today>0.9*30max': str(todaydata[1] )+str('<=')+str( rmax*jizhipoints['30daysmax'][1])}) + # elif (todaydata[1] >= rmax*jizhipoints['5daysmax'][1]): + # sell_point.append({'today>0.9*30max': str(todaydata[1] )+str('<=')+str( rmax*jizhipoints['5daysmax'][1])}) + + # def change_fenxi(change): + if(sum(change)/len(change)>=0.46): + sell_point.append({'mean5up':round(sum(change)/len(change),5)}) + elif(sum(change)/len(change)<=-0.46): + buy_points.append({'mean5up':round(sum(change)/len(change),5)}) + + big=[];small=[] + if len(change)>3: + for i in range(len(change)): + if (change[i]>0): + big.append(change[i]) + elif (change[i]<0): + small.append(change[i]) + if len(big)>=4 and change[0]<0.2 : + sell_point.append({'go up 4days':change7}) + # elif (change[0]=3): + # sell_point.append({u'go up 4days': big}) + if (len(small)>=4 and change[0]>0) or (len(small)>=3 and change[0]<-0.5 and change[1]<0.1): + buy_points.append({'go down 3days':change7}) + return {'sell':sell_point,'buy':buy_points} + +def main_run(all_fund_list): + code = [['002963', 'egold'], ['003321', 'eoil'], ['004744', 'eGEI'], ['110003', 'eSSE50'], ['110020', 'HS300'], + ['110031', 'eHSI'], ['161130', 'eNASDAQ100'], ['110028', 'anxinB'], ['110022', 'eConsumption '], + ['161125', 'SPX500']] + + buysell = [] + for i in code: + # save(strfundcode=i ,numdays=365*1) + sb=fenxi(strfundcode=i, numdays=365 * 1) + if sb: + buysell.append([i,sb]) + for i in all_fund_list[0:50]: + # save(strfundcode=i ,numdays=365*1) + sb=fenxi(strfundcode=i, numdays=365 * 1) + if sb: + buysell.append([i,sb]) + return buysell + +s = sched.scheduler(time.time, time.sleep) +class DateEncoder(json.JSONEncoder): + def default(self, obj): + if isinstance(obj, datetime): + return obj.__str__() + return json.JSONEncoder.default(self, obj) + + # sleep() +#@itchat.msg_register(itchat.content.TEXT) +def print_content(msg): + print msg['Text'] +def readmsg(msg): + return msg +def send_email(text): + #text = self.claw_content() + #open('b.txt', 'w').write(str(text[1]) + '\n' + str(text[0]) + '\n') + username = 'lsl_cug@126.com' # input("请输入账号:") + password = '123456lsl' # input("请输入密码:") + sender = username + # sender='' + receiver = ['1627041882@qq.com','760140853@qq.com'] # '760140853@qq.com','xxxxxxxxxx@qq.com','xxxxxxxxxx@126.com','994992333@qq.com','1847725033@qq.com','1847725033@qq.com','849281511@qq.com' + if sender =='': + username = str(raw_input("Please Input Sender Email Address,for example:xxxxxxxxxx@126.com \n")) + sender = username + password = str(raw_input("Please Input Sender Password \n")) + receiver.append(str(raw_input("Please Input Receiver Address,for example:xxxxxxxxxx@qq.com \n"))) + iRec = 1 + while iRec>0: + CmdRec = ''#raw_input("Whether you want to add another Receiver Address(default n)? \n") + if CmdRec == 'y' or CmdRec == 'yes': + iRec = 1 + receiver.append(str(raw_input("Please Input Receiver Address,for example:xxxxxxxxxx@qq.com \n"))) + else: + iRec = 0 + + ConfirmRec = ''#raw_input("Confirm? \n") + if ConfirmRec == 'n' or ConfirmRec == 'no': + print("What Do You Want? Maybe run this program again.\n") + exit(1) + else: + for rece in receiver: + print("OK \n"+ username + " sendto " + rece) + # 创建一个带附件的实例 + msg = MIMEMultipart() + # msg = MIMEText(str(text), 'plain', 'utf-8') + msg['From'] = formataddr(['user', sender]) # 括号里的对应发件人邮箱昵称、发件人邮箱账号 + msg['To'] = ",".join(receiver) # 括号里的对应收件人邮箱昵称、收件人邮箱账号 + + subject_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + subject = '基金信息' + msg['Subject'] = subject + + # 邮件正文内容 + # print str(text[0]) + realText = text#sendmsg #str(text) #'\n'+str(subject_time)+'：主人，有人来招人啦！^—^\n'+ '海投网：'+str(text[1])+'.'#+str(text[0])#'地大就业网招聘公告：'+str(text1[1])+'地大就业网gis等招聘信息：'+str(text2[1])+'.'#+str(text1[0])+'\n'+str(text2[1])+'\n'+str(text2[0]) + print realText + msg.attach(MIMEText(realText, 'plain', 'utf-8')) + + # 构造附件1，传送当前目录下的 test.txt 文件 + # att1 = MIMEApplication(open('b.txt', 'rb').read()) + # 这里的filename可以任意写，写什么名字，邮件中显示什么名字 + # att1.add_header('Content-Disposition', 'attachment', filename='hjx.txt') + # msg.attach(att1) + # # 构造附件1，传送当前目录下的 test.txt 文件 + # att2 = MIMEApplication(open('b1.txt', 'rb').read()) + # # 这里的filename可以任意写，写什么名字，邮件中显示什么名字 + # att2.add_header('Content-Disposition', 'attachment', filename='b1.txt') + # msg.attach(att2) + # att3 = MIMEApplication(open('b2.txt', 'rb').read()) + # # 这里的filename可以任意写，写什么名字，邮件中显示什么名字 + # att3.add_header('Content-Disposition', 'attachment', filename='b2.txt') + # msg.attach(att3) + smtpserver = 'smtp.126.com' + try: + smtp = smtplib.SMTP(smtpserver,25) + smtp.starttls() + smtp.login(username, password) + smtp.sendmail(sender, receiver, msg.as_string()) + smtp.quit() + print(u"邮件发送成功") + except smtplib.SMTPException, e: + print(u"Error: 无法发送邮件:" + str(e)) + smtp.quit() + try: + username = 'lishulincug@163.com' # input("请输入账号:") + password = '123456lsl' # input("请输入密码:") + sender = username + # sender='' + # receiver = [ '1627041882@qq.com','994992333@qq.com','1847725033@qq.com'] # 'xxxxxxxxxx@qq.com','xxxxxxxxxx@126.com','994992333@qq.com','1847725033@qq.com' + msg['From'] = formataddr(['lishulin', sender]) # 括号里的对应发件人邮箱昵称、发件人邮箱账号 + msg['To'] = ",".join(receiver) # 括号里的对应收件人邮箱昵称、收件人邮箱账号 + smtpserver = 'smtp.163.com' + smtp = smtplib.SMTP(smtpserver,25) + smtp.starttls() + smtp.login(username, password) + for jj in receiver: + smtp.sendmail(sender, jj, msg.as_string()) + time.sleep(35) + smtp.quit() + print(u"邮件发送成功") + except smtplib.SMTPException, e1: + print(u"Error: 无法发送邮件:"+str(e1)) + smtp.quit() + try: + username = '15623863340@sina.cn' # input("请输入账号:") + password = '133499' # input("请输入密码:") + sender = username + # sender='' + # receiver = [ '1627041882@qq.com','994992333@qq.com','1847725033@qq.com'] # 'xxxxxxxxxx@qq.com','xxxxxxxxxx@126.com','994992333@qq.com','1847725033@qq.com' + msg['From'] = formataddr(['15623863340@sina.cn', sender]) # 括号里的对应发件人邮箱昵称、发件人邮箱账号 + msg['To'] = ",".join(receiver) # 括号里的对应收件人邮箱昵称、收件人邮箱账号 + smtpserver = 'smtp.sina.cn' + smtp = smtplib.SMTP(smtpserver,25) + smtp.starttls() + smtp.login(username, password) + smtp.sendmail(sender, receiver, msg.as_string()) + smtp.quit() + print(u"邮件发送成功") + except smtplib.SMTPException, e2: + print(u"Error: 无法发送邮件:" + str(e2)) + smtp.quit() +def deb_print(): + now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + H=now[9:11] + M = now[12:14] + S = now[15:] + # print now + print H,M, S + return H,M, S + +def check_time(H, M,S): + strtoday1 = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d') + sdatetime = datetime.datetime.strptime(strtoday1, '%Y-%m-%d') + sdatetime.isoweekday() + #if sdatetime.isoweekday() == 7: + if(H == "14" and int(M) == 30 and S == "20")and(sdatetime.isoweekday() != 7)and(sdatetime.isoweekday() != 6):#(H == "14" and M == "08" and S == "10") or + # itchat.auto_login(hotReload=True) + # itchat.run + # curTime = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + # itchat.send(curTime, 'filehelper') + # itchat.send(str(curTime), toUserName='@9f545f6d5c3f89aa63956c3f386733232f7176569f71a26f477909c02828d735') + buysell = main_run(all_fund_list) + print(str(buysell)) + sendmsg=[] + # f = open("buysell.txt",'a') + for i in buysell: + # write_str = str(i) + '\n' + # f.write(write_str) + st = '' + if i[1]['buy'] != []: + st = ' 买 ' + for j in i[1]['buy']: + st += str(j) + ' ' + elif i[1]['sell'] != []: + st += ' 卖 ' + for k in i[1]['sell']: + st += str(k) + ' ' + if st != '': + sendmsg.append(str(i[0]) + st+(' http://fund.eastmoney.com/%s.html'%i[0][0]) +(' http://www.efunds.com.cn/html/fund/%s_fundinfo.htm '%i[0][0])) + send_email(sendmsg) + # itchat.send(str(i[0]) + st+(' http://fund.eastmoney.com/%s.html'%i[0][0]) +('-- http://www.efunds.com.cn/html/fund/%s_fundinfo.htm'%i[0][0]), 'filehelper') + # itchat.send(str(i[0]) + st, toUserName='@9f545f6d5c3f89aa63956c3f386733232f7176569f71a26f477909c02828d735') + s.enter(1, 1, check_time, deb_print()) + + +def main1(): + now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + H = now[9:11] + M = now[12:14] + S = now[15:] + code = [['002963', 'egold'], ['003321', 'eoil'], ['004744', 'eGEI'], ['110003', 'eSSE50'], ['110020', 'HS300'], + ['110031', 'eHSI'], ['161130', 'eNASDAQ100'], ['110028', 'anxinB'], ['110022', 'eConsumption '], + ['161125', 'SPX500']] + all_fund_list = main_zf(code) + sign=macdmain(code) + if (int(H)): # >= 19(H == "14" and M == "08" and S == "10") or + buysell1 = main_run(all_fund_list) + print(str(buysell1)) + sendmsg = '' + # f = open("buysell.txt",'a') + for j in sign: + sendmsg +=str(j[0])+','+str(j[1])+','+str(j[2][0])+','+str(j[2][1][0])+'\n' + for i in buysell1: + # write_str = str(i) + '\n' + # f.write(write_str) + st = ' ' + if i[1]['buy'] != []: + st = ' 买 ' + for j in i[1]['buy']: + for h in j: + st += h + str(j[h]) + ' ' + elif i[1]['sell'] != []: + st += ' 卖 ' + for k in i[1]['sell']: + for t in k: + st += t + str(k[t]) + ' ' + if st != ' ': + sendmsg += i[0][0] + ',' + i[0][1] + st + (' http://fund.eastmoney.com/%s.html' % i[0][0]) + ( + ' http://www.efunds.com.cn/html/fund/%s_fundinfo.htm' % i[0][0]) + '\n' + # print sendmsg + send_email(sendmsg) + while True: + now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + # date = datetime.datetime.strftime(datetime.datetime.now(), "%Y%m%d") + H = now[9:11] + M = now[12:14] + S = now[15:] + # print H,S,M + print 'Start:'+'13:05' +' and 14:30 '+'send to email.'+"\n" +'please wait:' + + # print time.localtime() + # print time.strftime("%y-%m-%d %H:%M:%S",time.localtime()) + # xiaozhao = Xiaozhao() + # xiaozhao.send_email() + s.enter(1, 1, check_time, deb_print()) + s.run() + +if __name__=='__main__': + # itchat.auto_login(hotReload=True) + # itchat.run + # + # curTime = now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + # itchat.send(curTime, 'filehelper') + # buysell = one_predict.main_run() + # f = open("buysell.txt",'a') + # for i in buysell: + # # write_str = str(i) + '\n' + # # f.write(write_str) + # st = '' + # if i[1]['buy'] != []: + # st = u'买入点' + # for j in i[1]['buy']: + # st += str(j) + ' ' + # elif i[1]['sell'] != []: + # st += u' 卖出点' + # for k in i[1]['sell']: + # st += str(k) + ' ' + # if st != '': + # itchat.send(str(i[0]) + st, 'filehelper') + # itchat.send(str(i[0]) + st,toUserName='@9f545f6d5c3f89aa63956c3f386733232f7176569f71a26f477909c02828d735') + all_fund_list = main_zf() + main1() + # f.close() + # itchat.send("@fil@%s" % 'buysell.txt', 'filehelper') + # for (name, fund_id) in ids.items(): + # url = 'http://fund.eastmoney.com/%s.html' % fund_id + # value = spider(url) + # sign = value[0] + # value_num = value[1:-1] + # name = name.decode('utf-8') + # buysell=one_predict.main_run() + # itchat.send(buysell , 'filehelper') + # if(sign == '-' and float(value_num)>2.5): + # content = u'%s跌幅很大，可以补仓'+sign+value_num + # itchat.send(content % name, 'filehelper') + # else: + # content = u'%s 不需要补仓'+sign+value_num + # itchat.send(content % name, 'filehelper') diff --git a/trade_process/efund_mail2_LOCAL_5352.py b/trade_process/efund_mail2_LOCAL_5352.py new file mode 100644 index 0000000..454e2e8 --- /dev/null +++ b/trade_process/efund_mail2_LOCAL_5352.py @@ -0,0 +1,579 @@ +#!/usr/bin/python2.7 +# -*- coding: utf-8 -*- +# from url_info import * +# from spider import * +#import itchat +#from sleep import * +# import datetime +# import one_predict +import json,time +import sched + +import datetime +import re +# import sys +import urllib2 +import smtplib +from email.mime.text import MIMEText +from email.mime.multipart import MIMEMultipart +from email.utils import formataddr +from email.mime.application import MIMEApplication +from fund_zf import main_zf +# from strategy.macd_back_test import macdmain +# from numpy import mean, ptp, var, std + +import requests +from bs4 import BeautifulSoup + + +def spider(url): + r = requests.get(url) + page = r.text + soup = BeautifulSoup(page, 'html.parser') + value = soup.find_all(id='gz_gszzl') + value = value[0].get_text().replace('%','') + value=value.encode('utf-8') + value1 = soup.find_all(id='gz_gsz') + value1 = value1[0].get_text().encode('utf-8') + try: + return [float(value1),float(value1),float(value)] + except ValueError as e: + return +# 是否已在list中 +def get_index(fund_code, all_fund_list): + fund_num = len(all_fund_list) + fund_index = 0 + while fund_index < fund_num: + if fund_code in all_fund_list[fund_index]: + break; + fund_index += 1 + return fund_index + + +# 获取基金类型 +def get_type(fund_code, all_fund_list): + fund_type = 'none' + for fund in all_fund_list: + if fund_code in fund: + fund_type = fund[3] + break + + return fund_type + +# 获取某一基金在某一日的累计净值数据 +def get_jingzhi(strfundcode, strdate,stredate): + try: + url = 'http://fund.eastmoney.com/f10/F10DataApi.aspx?type=lsjz&code=' + str(strfundcode) + '&page=1&per=1000&sdate=' + str(strdate) + '&edate=' + str(stredate) + print u'数据链接：'+url + '\n' + response = urllib2.urlopen(url) + except urllib2.HTTPError, e: + print e + urllib_error_tag = True + except StandardError, e: + print e + urllib_error_tag = True + else: + urllib_error_tag = False + + if urllib_error_tag == True: + return '-1' + + json_fund_value = response.read().decode('utf-8') + # print json_fund_value + + tr_re = re.compile(r'(.*?)') + item_re = re.compile( + r'''(\d{4}-\d{2}-\d{2})(.*?)(.*?)(.*?)(.*?)(.*?)''', + re.X) + + # 获取不到 返回0 + jingzhi ='-1' + result=[] + + for line in tr_re.findall(json_fund_value): + # print line + '\n' + match = item_re.match(line) + if match: + entry = match.groups() + entry[3].rstrip('%') + date = datetime.datetime.strptime(entry[0], '%Y-%m-%d') + # jingzhi = entry[2] + if entry[3]!='': + result.append([date, float(entry[1]),float(entry[2]), float(entry[3].rstrip('%'))]) + else: + result.append([date, float(entry[1]), float(entry[2]), 0]) + jingzhi1 = entry[1] + jingzhi2 = entry[2] + # print jingzhi2 + + if jingzhi2.strip() == '': + # 040028 + # 净值日期 每万份收益 7日年化收益率（%） 申购状态 赎回状态 分红送配 + # 2017-01-06 1.4414 暂停申购 暂停赎回 + # 2017-01-05 1.4369 暂停申购 暂停赎回 + jingzhi = '-1' + elif jingzhi2.find('%') > -1: + # 040003 + # 净值日期 每万份收益 7日年化收益率（%） 申购状态 赎回状态 分红送配 + # 2017-03-27 1.1149 3.9450% 限制大额申购 开放赎回 + # 2017-03-26* 2.2240 3.8970% 限制大额申购 开放赎回 + jingzhi = '-1' + elif float(jingzhi1) > float(jingzhi2): + # 502015 + # 净值日期 单位净值 累计净值 日增长率 申购状态 赎回状态 分红送配 + # 2017-03-27 0.6980 0.3785 -2.24% 场内买入 场内卖出 + # 2017-03-24 0.7140 0.3945 5.15% 场内买入 场内卖出 + jingzhi = entry[1] + else: + # + # 净值日期 单位净值 累计净值 日增长率 申购状态 赎回状态 分红送配 + # 2017-03-28 1.7720 1.7720 -0.23% 开放申购 开放赎回 + # 2017-03-27 1.7761 1.7761 -0.43% 开放申购 开放赎回 + jingzhi = entry[2] + return result + +#获取历史数据 +def get_histrydata(strfundcode,numdays): + # 当前日期 + strtoday = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d') + tdatetime = datetime.datetime.strptime(strtoday, '%Y-%m-%d') + print u'结束时间：' + strtoday + url = 'http://fund.eastmoney.com/%s.html' % strfundcode[0] + todayvalue = spider(url) + + # 昨天 + yestodaytime = tdatetime - datetime.timedelta(days=1) + yestoday = datetime.datetime.strftime(yestodaytime, '%Y-%m-%d') + + # 前年今日 + sdatetime = tdatetime - datetime.timedelta(days=numdays) + strsdate = datetime.datetime.strftime(sdatetime, '%Y-%m-%d') + print u'开始时间：' + strsdate + + # 1.1 起始时间 + strsdate = strsdate # sys.argv[1] + stredate = yestoday # sys.argv[2] + + # 今日零时 + # strtoday = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d') + # tdatetime = datetime.datetime.strptime(strtoday, '%Y-%m-%d') + # print tdatetime + + # 开始时间 如果是周六 周日 调整到周五 + # print strsdate + sdatetime = datetime.datetime.strptime(strsdate, '%Y-%m-%d') + sdatetime.isoweekday() + if sdatetime.isoweekday() == 7: + sdatetime = sdatetime + datetime.timedelta(days=-2) + elif sdatetime.isoweekday() == 6: + sdatetime = sdatetime + datetime.timedelta(days=-1) + + strsdate = datetime.datetime.strftime(sdatetime, '%Y-%m-%d') + # print strsdate + + # 结束时间 如果是周六 周日 调整到周五 + # print stredate + edatetime = datetime.datetime.strptime(stredate, '%Y-%m-%d') + edatetime.isoweekday() + if edatetime.isoweekday() == 7: + edatetime = edatetime + datetime.timedelta(days=-2) + elif edatetime.isoweekday() == 6: + edatetime = edatetime + datetime.timedelta(days=-1) + + stredate = datetime.datetime.strftime(edatetime, '%Y-%m-%d') + # print stredate + + # 判断时间段 今日净值要下午才出 一律不处理 + if edatetime <= sdatetime or tdatetime <= sdatetime or tdatetime <= edatetime: + print '判断时间段 今日净值要下午才出 一律不处理' + print 'date input error!\n' + + jingzhimin = get_jingzhi(strfundcode[0], strsdate, stredate) + if todayvalue != None: + jingzhimin.insert(0, [tdatetime, todayvalue[0], todayvalue[1], todayvalue[2]]) + return jingzhimin + +#计算交易策略 +def fenxi(strfundcode,numdays): + #获取历史数据 + jingzhimin=get_histrydata(strfundcode,numdays) + years = [x[0] for x in jingzhimin] + price = [x[1] for x in jingzhimin] + changeprice = [x[3]/x[1] for x in jingzhimin] + price_stre = [((x[1] - (sum(price)/len(price)))/(max(price)-min(price)))*(max(changeprice)-min(changeprice)) for x in jingzhimin] + # plt.plot(years, changeprice, 'g',label='change') + starty = [0 for x in jingzhimin] + jizhipoints=jizhi(jingzhimin) + celue_point=celue(jingzhimin,jizhipoints) + # change_fenxi(change5days) + return celue_point + +def stdDeviation(a): + l = len(a) + m = sum(a) / l + d = 0 + for i in a: d += (i - m) ** 2 + return (d * (1 / l)) ** 0.5 + +#统计 +# def caculate(data): +# # 极差 +# ptp(data) +# # 方差 +# var(data) +# # 标准差 +# std(data) +# # 变异系数 +# mean(data) / std(data) +# norm.ppf(0.05, mean(data), std(data)) + +#求极值 +def jizhi(funddata): + jizhipoints={} + alldata = [x[1] for x in funddata[::-2]] + jizhipoints['allmax']= [alldata.index(max(alldata)),max(alldata)] + jizhipoints['allmin']= [alldata.index(min(alldata)),min(alldata)] + data30days = [x[1] for x in funddata[:-30:-2]] + jizhipoints['30daysmax'] = [data30days.index(max(data30days)),max(data30days)] + jizhipoints['30daysmin'] = [data30days.index(min(data30days)),min(data30days)] + data180days = [x[1] for x in funddata[:-180:-2]] + jizhipoints['180daysmax'] = [data180days.index(max(data180days)), max(data180days)] + jizhipoints['180daysmin'] = [data180days.index(min(data180days)), min(data180days)] + data5days = [x[1] for x in funddata[:-5:-2]] + jizhipoints['5daysmax'] = [data5days.index(max(data5days)), max(data5days)] + jizhipoints['5daysmin'] = [data5days.index(min(data5days)), min(data5days)] + return jizhipoints + +#交易策略监测 +def celue(funddata,jizhipoints): + todaydata = funddata[0] + data5days = [x[1] for x in funddata[:6:1]] + change = [x[3] for x in funddata[:5:1]] + change7 = [x[3] for x in funddata[:7:1]] + change12days= [x[3] for x in funddata[:90:1]] + if stdDeviation(change12days)<0.3: + r=1.005;rmax=0.995 + elif stdDeviation(change12days) > 0.6: + r = 1.005;rmax = 0.995 + else: + r = 1.005;rmax = 0.995 + buy_points = [] + sell_point = [] + + #根据净值比较 + if(todaydata[1]<=r*jizhipoints['allmin'][1]): + buy_points.append({'today<1.02*allmin':str(todaydata[1])+str('<=')+str(r*jizhipoints['allmin'][1])}) + elif(todaydata[1]<=r*jizhipoints['180daysmin'][1]): + buy_points.append({'today<1.02*180min': str(todaydata[1])+str('<=')+str(r*jizhipoints['180daysmin'][1])}) + # elif(todaydata[1]<=r*jizhipoints['30daysmin'][1]): + # buy_points.append({'today<1.02*30min': str(todaydata[1] )+str('<=')+str( r*jizhipoints['30daysmin'][1])}) + # elif (todaydata[1] <= r*jizhipoints['5daysmin'][1]): + # buy_points['today<1.02*5min'] = str(todaydata[1] )+str('<=')+str( r*jizhipoints['5daysmin'][1]) + #---------------------------- + elif (todaydata[1] >= rmax*jizhipoints['allmax'][1]): + sell_point.append({'today>0.98*allmax': str(todaydata[1] )+str('>=')+str( rmax*jizhipoints['allmax'][1])}) + elif (todaydata[1] >= rmax*jizhipoints['180daysmax'][1]): + sell_point.append({'today>0.98*180max': str(todaydata[1] )+str('>=')+str( rmax*jizhipoints['180daysmax'][1])}) + # elif (todaydata[1] >= rmax*jizhipoints['30daysmax'][1]): + # sell_point.append({'today>0.9*30max': str(todaydata[1] )+str('<=')+str( rmax*jizhipoints['30daysmax'][1])}) + # elif (todaydata[1] >= rmax*jizhipoints['5daysmax'][1]): + # sell_point.append({'today>0.9*30max': str(todaydata[1] )+str('<=')+str( rmax*jizhipoints['5daysmax'][1])}) + + # def change_fenxi(change): + if(sum(change)/len(change)>=0.46): + sell_point.append({'mean5up':round(sum(change)/len(change),5)}) + elif(sum(change)/len(change)<=-0.46): + buy_points.append({'mean5up':round(sum(change)/len(change),5)}) + + big=[];small=[] + if len(change)>3: + for i in range(len(change)): + if (change[i]>0): + big.append(change[i]) + elif (change[i]<0): + small.append(change[i]) + if len(big)>=4 and change[0]<0.2 : + sell_point.append({'go up 4days':change7}) + # elif (change[0]=3): + # sell_point.append({u'go up 4days': big}) + if (len(small)>=4 and change[0]>0) or (len(small)>=3 and change[0]<-0.5 and change[1]<0.1): + buy_points.append({'go down 3days':change7}) + return {'sell':sell_point,'buy':buy_points} + +def main_run(all_fund_list): + code = [['002963', 'egold'], ['003321', 'eoil'], ['004744', 'eGEI'], ['110003', 'eSSE50'], ['110020', 'HS300'], + ['110031', 'eHSI'], ['161130', 'eNASDAQ100'], ['110028', 'anxinB'], ['110022', 'eConsumption '], + ['161125', 'SPX500']] + + buysell = [] + for i in code: + # save(strfundcode=i ,numdays=365*1) + sb=fenxi(strfundcode=i, numdays=365 * 1) + if sb: + buysell.append([i,sb]) + for i in all_fund_list[0:50]: + # save(strfundcode=i ,numdays=365*1) + sb=fenxi(strfundcode=i, numdays=365 * 1) + if sb: + buysell.append([i,sb]) + return buysell + +s = sched.scheduler(time.time, time.sleep) +class DateEncoder(json.JSONEncoder): + def default(self, obj): + if isinstance(obj, datetime): + return obj.__str__() + return json.JSONEncoder.default(self, obj) + + # sleep() +#@itchat.msg_register(itchat.content.TEXT) +def print_content(msg): + print msg['Text'] +def readmsg(msg): + return msg +def send_email(text): + #text = self.claw_content() + #open('b.txt', 'w').write(str(text[1]) + '\n' + str(text[0]) + '\n') + username = 'lsl_cug@126.com' # input("请输入账号:") + password = '123456lsl' # input("请输入密码:") + sender = username + # sender='' + receiver = ['1627041882@qq.com','760140853@qq.com'] # '760140853@qq.com','xxxxxxxxxx@qq.com','xxxxxxxxxx@126.com','994992333@qq.com','1847725033@qq.com','1847725033@qq.com','849281511@qq.com' + if sender =='': + username = str(raw_input("Please Input Sender Email Address,for example:xxxxxxxxxx@126.com \n")) + sender = username + password = str(raw_input("Please Input Sender Password \n")) + receiver.append(str(raw_input("Please Input Receiver Address,for example:xxxxxxxxxx@qq.com \n"))) + iRec = 1 + while iRec>0: + CmdRec = ''#raw_input("Whether you want to add another Receiver Address(default n)? \n") + if CmdRec == 'y' or CmdRec == 'yes': + iRec = 1 + receiver.append(str(raw_input("Please Input Receiver Address,for example:xxxxxxxxxx@qq.com \n"))) + else: + iRec = 0 + + ConfirmRec = ''#raw_input("Confirm? \n") + if ConfirmRec == 'n' or ConfirmRec == 'no': + print("What Do You Want? Maybe run this program again.\n") + exit(1) + else: + for rece in receiver: + print("OK \n"+ username + " sendto " + rece) + # 创建一个带附件的实例 + msg = MIMEMultipart() + # msg = MIMEText(str(text), 'plain', 'utf-8') + msg['From'] = formataddr(['user', sender]) # 括号里的对应发件人邮箱昵称、发件人邮箱账号 + msg['To'] = ",".join(receiver) # 括号里的对应收件人邮箱昵称、收件人邮箱账号 + + subject_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + subject = '基金信息' + msg['Subject'] = subject + + # 邮件正文内容 + # print str(text[0]) + realText = text#sendmsg #str(text) #'\n'+str(subject_time)+'：主人，有人来招人啦！^—^\n'+ '海投网：'+str(text[1])+'.'#+str(text[0])#'地大就业网招聘公告：'+str(text1[1])+'地大就业网gis等招聘信息：'+str(text2[1])+'.'#+str(text1[0])+'\n'+str(text2[1])+'\n'+str(text2[0]) + print realText + msg.attach(MIMEText(realText, 'plain', 'utf-8')) + + # 构造附件1，传送当前目录下的 test.txt 文件 + # att1 = MIMEApplication(open('b.txt', 'rb').read()) + # 这里的filename可以任意写，写什么名字，邮件中显示什么名字 + # att1.add_header('Content-Disposition', 'attachment', filename='hjx.txt') + # msg.attach(att1) + # # 构造附件1，传送当前目录下的 test.txt 文件 + # att2 = MIMEApplication(open('b1.txt', 'rb').read()) + # # 这里的filename可以任意写，写什么名字，邮件中显示什么名字 + # att2.add_header('Content-Disposition', 'attachment', filename='b1.txt') + # msg.attach(att2) + # att3 = MIMEApplication(open('b2.txt', 'rb').read()) + # # 这里的filename可以任意写，写什么名字，邮件中显示什么名字 + # att3.add_header('Content-Disposition', 'attachment', filename='b2.txt') + # msg.attach(att3) + smtpserver = 'smtp.126.com' + try: + smtp = smtplib.SMTP(smtpserver,25) + smtp.starttls() + smtp.login(username, password) + smtp.sendmail(sender, receiver, msg.as_string()) + smtp.quit() + print(u"邮件发送成功") + except smtplib.SMTPException, e: + print(u"Error: 无法发送邮件:" + str(e)) + smtp.quit() + try: + username = 'lishulincug@163.com' # input("请输入账号:") + password = '123456lsl' # input("请输入密码:") + sender = username + # sender='' + # receiver = [ '1627041882@qq.com','994992333@qq.com','1847725033@qq.com'] # 'xxxxxxxxxx@qq.com','xxxxxxxxxx@126.com','994992333@qq.com','1847725033@qq.com' + msg['From'] = formataddr(['lishulin', sender]) # 括号里的对应发件人邮箱昵称、发件人邮箱账号 + msg['To'] = ",".join(receiver) # 括号里的对应收件人邮箱昵称、收件人邮箱账号 + smtpserver = 'smtp.163.com' + smtp = smtplib.SMTP(smtpserver,25) + smtp.starttls() + smtp.login(username, password) + for jj in receiver: + smtp.sendmail(sender, jj, msg.as_string()) + time.sleep(35) + smtp.quit() + print(u"邮件发送成功") + except smtplib.SMTPException, e1: + print(u"Error: 无法发送邮件:"+str(e1)) + smtp.quit() + try: + username = '15623863340@sina.cn' # input("请输入账号:") + password = '133499' # input("请输入密码:") + sender = username + # sender='' + # receiver = [ '1627041882@qq.com','994992333@qq.com','1847725033@qq.com'] # 'xxxxxxxxxx@qq.com','xxxxxxxxxx@126.com','994992333@qq.com','1847725033@qq.com' + msg['From'] = formataddr(['15623863340@sina.cn', sender]) # 括号里的对应发件人邮箱昵称、发件人邮箱账号 + msg['To'] = ",".join(receiver) # 括号里的对应收件人邮箱昵称、收件人邮箱账号 + smtpserver = 'smtp.sina.cn' + smtp = smtplib.SMTP(smtpserver,25) + smtp.starttls() + smtp.login(username, password) + smtp.sendmail(sender, receiver, msg.as_string()) + smtp.quit() + print(u"邮件发送成功") + except smtplib.SMTPException, e2: + print(u"Error: 无法发送邮件:" + str(e2)) + smtp.quit() +def deb_print(): + now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + H=now[9:11] + M = now[12:14] + S = now[15:] + # print now + print H,M, S + return H,M, S + +def check_time(H, M,S): + strtoday1 = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d') + sdatetime = datetime.datetime.strptime(strtoday1, '%Y-%m-%d') + sdatetime.isoweekday() + #if sdatetime.isoweekday() == 7: + if(H == "14" and int(M) == 30 and S == "20")and(sdatetime.isoweekday() != 7)and(sdatetime.isoweekday() != 6):#(H == "14" and M == "08" and S == "10") or + # itchat.auto_login(hotReload=True) + # itchat.run + # curTime = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + # itchat.send(curTime, 'filehelper') + # itchat.send(str(curTime), toUserName='@9f545f6d5c3f89aa63956c3f386733232f7176569f71a26f477909c02828d735') + buysell = main_run(all_fund_list) + print(str(buysell)) + sendmsg=[] + # f = open("buysell.txt",'a') + for i in buysell: + # write_str = str(i) + '\n' + # f.write(write_str) + st = '' + if i[1]['buy'] != []: + st = ' 买 ' + for j in i[1]['buy']: + st += str(j) + ' ' + elif i[1]['sell'] != []: + st += ' 卖 ' + for k in i[1]['sell']: + st += str(k) + ' ' + if st != '': + sendmsg.append(str(i[0]) + st+(' http://fund.eastmoney.com/%s.html'%i[0][0]) +(' http://www.efunds.com.cn/html/fund/%s_fundinfo.htm '%i[0][0])) + send_email(sendmsg) + # itchat.send(str(i[0]) + st+(' http://fund.eastmoney.com/%s.html'%i[0][0]) +('-- http://www.efunds.com.cn/html/fund/%s_fundinfo.htm'%i[0][0]), 'filehelper') + # itchat.send(str(i[0]) + st, toUserName='@9f545f6d5c3f89aa63956c3f386733232f7176569f71a26f477909c02828d735') + s.enter(1, 1, check_time, deb_print()) + + +def main1(sign): + now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + H = now[9:11] + M = now[12:14] + S = now[15:] + code = [['002963', 'egold'], ['003321', 'eoil'], ['004744', 'eGEI'], ['110003', 'eSSE50'], ['110020', 'HS300'], + ['110031', 'eHSI'], ['161130', 'eNASDAQ100'], ['110028', 'anxinB'], ['110022', 'eConsumption '], + ['161125', 'SPX500']] + all_fund_list = main_zf() + #sign=macdmain(code) + if (int(H)): # >= 19(H == "14" and M == "08" and S == "10") or + buysell1 = main_run(all_fund_list) + print(str(buysell1)) + sendmsg = '' + # f = open("buysell.txt",'a') + for j in sign: + #sendmsg +=str(j[0])+','+str(j[1])+','+str(j[2][0])+','+str(j[2][1][0])+'\n' + sendmsg +=str(j[0])+','+str(j[1])+','+str(j[2][0])+','+str(j[2][1][0])+','+ (' http://fund.eastmoney.com/%s.html' % j[0][0])+'\n' + for i in buysell1: + # write_str = str(i) + '\n' + # f.write(write_str) + st = ' ' + if i[1]['buy'] != []: + st = ' 买 ' + for j in i[1]['buy']: + for h in j: + st += h + str(j[h]) + ' ' + elif i[1]['sell'] != []: + st += ' 卖 ' + for k in i[1]['sell']: + for t in k: + st += t + str(k[t]) + ' ' + if st != ' ': + sendmsg += i[0][0] + ',' + i[0][1] + st + (' http://fund.eastmoney.com/%s.html' % i[0][0]) + ( + ' http://www.efunds.com.cn/html/fund/%s_fundinfo.htm' % i[0][0]) + '\n' + # print sendmsg + send_email(sendmsg) + while True: + now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + # date = datetime.datetime.strftime(datetime.datetime.now(), "%Y%m%d") + H = now[9:11] + M = now[12:14] + S = now[15:] + # print H,S,M + print 'Start:'+'13:05' +' and 14:30 '+'send to email.'+"\n" +'please wait:' + + # print time.localtime() + # print time.strftime("%y-%m-%d %H:%M:%S",time.localtime()) + # xiaozhao = Xiaozhao() + # xiaozhao.send_email() + s.enter(1, 1, check_time, deb_print()) + s.run() + +if __name__=='__main__': + # itchat.auto_login(hotReload=True) + # itchat.run + # + # curTime = now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + # itchat.send(curTime, 'filehelper') + # buysell = one_predict.main_run() + # f = open("buysell.txt",'a') + # for i in buysell: + # # write_str = str(i) + '\n' + # # f.write(write_str) + # st = '' + # if i[1]['buy'] != []: + # st = u'买入点' + # for j in i[1]['buy']: + # st += str(j) + ' ' + # elif i[1]['sell'] != []: + # st += u' 卖出点' + # for k in i[1]['sell']: + # st += str(k) + ' ' + # if st != '': + # itchat.send(str(i[0]) + st, 'filehelper') + # itchat.send(str(i[0]) + st,toUserName='@9f545f6d5c3f89aa63956c3f386733232f7176569f71a26f477909c02828d735') + all_fund_list = main_zf() + main1() + # f.close() + # itchat.send("@fil@%s" % 'buysell.txt', 'filehelper') + # for (name, fund_id) in ids.items(): + # url = 'http://fund.eastmoney.com/%s.html' % fund_id + # value = spider(url) + # sign = value[0] + # value_num = value[1:-1] + # name = name.decode('utf-8') + # buysell=one_predict.main_run() + # itchat.send(buysell , 'filehelper') + # if(sign == '-' and float(value_num)>2.5): + # content = u'%s跌幅很大，可以补仓'+sign+value_num + # itchat.send(content % name, 'filehelper') + # else: + # content = u'%s 不需要补仓'+sign+value_num + # itchat.send(content % name, 'filehelper') diff --git a/trade_process/efund_mail2_REMOTE_5352.py b/trade_process/efund_mail2_REMOTE_5352.py new file mode 100644 index 0000000..a661f98 --- /dev/null +++ b/trade_process/efund_mail2_REMOTE_5352.py @@ -0,0 +1,578 @@ +#!/usr/bin/python2.7 +# -*- coding: utf-8 -*- +# from url_info import * +# from spider import * +#import itchat +#from sleep import * +# import datetime +# import one_predict +import json,time +import sched + +import datetime +import re +# import sys +import urllib2 +import smtplib +from email.mime.text import MIMEText +from email.mime.multipart import MIMEMultipart +from email.utils import formataddr +from email.mime.application import MIMEApplication +from fund_zf import main_zf +from trade_process.strategy.macd_back_test import macdmain +# from numpy import mean, ptp, var, std + +import requests +from bs4 import BeautifulSoup + + +def spider(url): + r = requests.get(url) + page = r.text + soup = BeautifulSoup(page, 'html.parser') + value = soup.find_all(id='gz_gszzl') + value = value[0].get_text().replace('%','') + value=value.encode('utf-8') + value1 = soup.find_all(id='gz_gsz') + value1 = value1[0].get_text().encode('utf-8') + try: + return [float(value1),float(value1),float(value)] + except ValueError as e: + return +# 是否已在list中 +def get_index(fund_code, all_fund_list): + fund_num = len(all_fund_list) + fund_index = 0 + while fund_index < fund_num: + if fund_code in all_fund_list[fund_index]: + break; + fund_index += 1 + return fund_index + + +# 获取基金类型 +def get_type(fund_code, all_fund_list): + fund_type = 'none' + for fund in all_fund_list: + if fund_code in fund: + fund_type = fund[3] + break + + return fund_type + +# 获取某一基金在某一日的累计净值数据 +def get_jingzhi(strfundcode, strdate,stredate): + try: + url = 'http://fund.eastmoney.com/f10/F10DataApi.aspx?type=lsjz&code=' + str(strfundcode) + '&page=1&per=1000&sdate=' + str(strdate) + '&edate=' + str(stredate) + print u'数据链接：'+url + '\n' + response = urllib2.urlopen(url) + except urllib2.HTTPError, e: + print e + urllib_error_tag = True + except StandardError, e: + print e + urllib_error_tag = True + else: + urllib_error_tag = False + + if urllib_error_tag == True: + return '-1' + + json_fund_value = response.read().decode('utf-8') + # print json_fund_value + + tr_re = re.compile(r'(.*?)') + item_re = re.compile( + r'''(\d{4}-\d{2}-\d{2})(.*?)(.*?)(.*?)(.*?)(.*?)''', + re.X) + + # 获取不到 返回0 + jingzhi ='-1' + result=[] + + for line in tr_re.findall(json_fund_value): + # print line + '\n' + match = item_re.match(line) + if match: + entry = match.groups() + entry[3].rstrip('%') + date = datetime.datetime.strptime(entry[0], '%Y-%m-%d') + # jingzhi = entry[2] + if entry[3]!='': + result.append([date, float(entry[1]),float(entry[2]), float(entry[3].rstrip('%'))]) + else: + result.append([date, float(entry[1]), float(entry[2]), 0]) + jingzhi1 = entry[1] + jingzhi2 = entry[2] + # print jingzhi2 + + if jingzhi2.strip() == '': + # 040028 + # 净值日期 每万份收益 7日年化收益率（%） 申购状态 赎回状态 分红送配 + # 2017-01-06 1.4414 暂停申购 暂停赎回 + # 2017-01-05 1.4369 暂停申购 暂停赎回 + jingzhi = '-1' + elif jingzhi2.find('%') > -1: + # 040003 + # 净值日期 每万份收益 7日年化收益率（%） 申购状态 赎回状态 分红送配 + # 2017-03-27 1.1149 3.9450% 限制大额申购 开放赎回 + # 2017-03-26* 2.2240 3.8970% 限制大额申购 开放赎回 + jingzhi = '-1' + elif float(jingzhi1) > float(jingzhi2): + # 502015 + # 净值日期 单位净值 累计净值 日增长率 申购状态 赎回状态 分红送配 + # 2017-03-27 0.6980 0.3785 -2.24% 场内买入 场内卖出 + # 2017-03-24 0.7140 0.3945 5.15% 场内买入 场内卖出 + jingzhi = entry[1] + else: + # + # 净值日期 单位净值 累计净值 日增长率 申购状态 赎回状态 分红送配 + # 2017-03-28 1.7720 1.7720 -0.23% 开放申购 开放赎回 + # 2017-03-27 1.7761 1.7761 -0.43% 开放申购 开放赎回 + jingzhi = entry[2] + return result + +#获取历史数据 +def get_histrydata(strfundcode,numdays): + # 当前日期 + strtoday = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d') + tdatetime = datetime.datetime.strptime(strtoday, '%Y-%m-%d') + print u'结束时间：' + strtoday + url = 'http://fund.eastmoney.com/%s.html' % strfundcode[0] + todayvalue = spider(url) + + # 昨天 + yestodaytime = tdatetime - datetime.timedelta(days=1) + yestoday = datetime.datetime.strftime(yestodaytime, '%Y-%m-%d') + + # 前年今日 + sdatetime = tdatetime - datetime.timedelta(days=numdays) + strsdate = datetime.datetime.strftime(sdatetime, '%Y-%m-%d') + print u'开始时间：' + strsdate + + # 1.1 起始时间 + strsdate = strsdate # sys.argv[1] + stredate = yestoday # sys.argv[2] + + # 今日零时 + # strtoday = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d') + # tdatetime = datetime.datetime.strptime(strtoday, '%Y-%m-%d') + # print tdatetime + + # 开始时间 如果是周六 周日 调整到周五 + # print strsdate + sdatetime = datetime.datetime.strptime(strsdate, '%Y-%m-%d') + sdatetime.isoweekday() + if sdatetime.isoweekday() == 7: + sdatetime = sdatetime + datetime.timedelta(days=-2) + elif sdatetime.isoweekday() == 6: + sdatetime = sdatetime + datetime.timedelta(days=-1) + + strsdate = datetime.datetime.strftime(sdatetime, '%Y-%m-%d') + # print strsdate + + # 结束时间 如果是周六 周日 调整到周五 + # print stredate + edatetime = datetime.datetime.strptime(stredate, '%Y-%m-%d') + edatetime.isoweekday() + if edatetime.isoweekday() == 7: + edatetime = edatetime + datetime.timedelta(days=-2) + elif edatetime.isoweekday() == 6: + edatetime = edatetime + datetime.timedelta(days=-1) + + stredate = datetime.datetime.strftime(edatetime, '%Y-%m-%d') + # print stredate + + # 判断时间段 今日净值要下午才出 一律不处理 + if edatetime <= sdatetime or tdatetime <= sdatetime or tdatetime <= edatetime: + print '判断时间段 今日净值要下午才出 一律不处理' + print 'date input error!\n' + + jingzhimin = get_jingzhi(strfundcode[0], strsdate, stredate) + if todayvalue != None: + jingzhimin.insert(0, [tdatetime, todayvalue[0], todayvalue[1], todayvalue[2]]) + return jingzhimin + +#计算交易策略 +def fenxi(strfundcode,numdays): + #获取历史数据 + jingzhimin=get_histrydata(strfundcode,numdays) + years = [x[0] for x in jingzhimin] + price = [x[1] for x in jingzhimin] + changeprice = [x[3]/x[1] for x in jingzhimin] + price_stre = [((x[1] - (sum(price)/len(price)))/(max(price)-min(price)))*(max(changeprice)-min(changeprice)) for x in jingzhimin] + # plt.plot(years, changeprice, 'g',label='change') + starty = [0 for x in jingzhimin] + jizhipoints=jizhi(jingzhimin) + celue_point=celue(jingzhimin,jizhipoints) + # change_fenxi(change5days) + return celue_point + +def stdDeviation(a): + l = len(a) + m = sum(a) / l + d = 0 + for i in a: d += (i - m) ** 2 + return (d * (1 / l)) ** 0.5 + +#统计 +# def caculate(data): +# # 极差 +# ptp(data) +# # 方差 +# var(data) +# # 标准差 +# std(data) +# # 变异系数 +# mean(data) / std(data) +# norm.ppf(0.05, mean(data), std(data)) + +#求极值 +def jizhi(funddata): + jizhipoints={} + alldata = [x[1] for x in funddata[::-2]] + jizhipoints['allmax']= [alldata.index(max(alldata)),max(alldata)] + jizhipoints['allmin']= [alldata.index(min(alldata)),min(alldata)] + data30days = [x[1] for x in funddata[:-30:-2]] + jizhipoints['30daysmax'] = [data30days.index(max(data30days)),max(data30days)] + jizhipoints['30daysmin'] = [data30days.index(min(data30days)),min(data30days)] + data180days = [x[1] for x in funddata[:-180:-2]] + jizhipoints['180daysmax'] = [data180days.index(max(data180days)), max(data180days)] + jizhipoints['180daysmin'] = [data180days.index(min(data180days)), min(data180days)] + data5days = [x[1] for x in funddata[:-5:-2]] + jizhipoints['5daysmax'] = [data5days.index(max(data5days)), max(data5days)] + jizhipoints['5daysmin'] = [data5days.index(min(data5days)), min(data5days)] + return jizhipoints + +#交易策略监测 +def celue(funddata,jizhipoints): + todaydata = funddata[0] + data5days = [x[1] for x in funddata[:6:1]] + change = [x[3] for x in funddata[:5:1]] + change7 = [x[3] for x in funddata[:7:1]] + change12days= [x[3] for x in funddata[:90:1]] + if stdDeviation(change12days)<0.3: + r=1.005;rmax=0.995 + elif stdDeviation(change12days) > 0.6: + r = 1.005;rmax = 0.995 + else: + r = 1.005;rmax = 0.995 + buy_points = [] + sell_point = [] + + #根据净值比较 + if(todaydata[1]<=r*jizhipoints['allmin'][1]): + buy_points.append({'today<1.02*allmin':str(todaydata[1])+str('<=')+str(r*jizhipoints['allmin'][1])}) + elif(todaydata[1]<=r*jizhipoints['180daysmin'][1]): + buy_points.append({'today<1.02*180min': str(todaydata[1])+str('<=')+str(r*jizhipoints['180daysmin'][1])}) + # elif(todaydata[1]<=r*jizhipoints['30daysmin'][1]): + # buy_points.append({'today<1.02*30min': str(todaydata[1] )+str('<=')+str( r*jizhipoints['30daysmin'][1])}) + # elif (todaydata[1] <= r*jizhipoints['5daysmin'][1]): + # buy_points['today<1.02*5min'] = str(todaydata[1] )+str('<=')+str( r*jizhipoints['5daysmin'][1]) + #---------------------------- + elif (todaydata[1] >= rmax*jizhipoints['allmax'][1]): + sell_point.append({'today>0.98*allmax': str(todaydata[1] )+str('>=')+str( rmax*jizhipoints['allmax'][1])}) + elif (todaydata[1] >= rmax*jizhipoints['180daysmax'][1]): + sell_point.append({'today>0.98*180max': str(todaydata[1] )+str('>=')+str( rmax*jizhipoints['180daysmax'][1])}) + # elif (todaydata[1] >= rmax*jizhipoints['30daysmax'][1]): + # sell_point.append({'today>0.9*30max': str(todaydata[1] )+str('<=')+str( rmax*jizhipoints['30daysmax'][1])}) + # elif (todaydata[1] >= rmax*jizhipoints['5daysmax'][1]): + # sell_point.append({'today>0.9*30max': str(todaydata[1] )+str('<=')+str( rmax*jizhipoints['5daysmax'][1])}) + + # def change_fenxi(change): + if(sum(change)/len(change)>=0.46): + sell_point.append({'mean5up':round(sum(change)/len(change),5)}) + elif(sum(change)/len(change)<=-0.46): + buy_points.append({'mean5up':round(sum(change)/len(change),5)}) + + big=[];small=[] + if len(change)>3: + for i in range(len(change)): + if (change[i]>0): + big.append(change[i]) + elif (change[i]<0): + small.append(change[i]) + if len(big)>=4 and change[0]<0.2 : + sell_point.append({'go up 4days':change7}) + # elif (change[0]=3): + # sell_point.append({u'go up 4days': big}) + if (len(small)>=4 and change[0]>0) or (len(small)>=3 and change[0]<-0.5 and change[1]<0.1): + buy_points.append({'go down 3days':change7}) + return {'sell':sell_point,'buy':buy_points} + +def main_run(all_fund_list): + code = [['002963', 'egold'], ['003321', 'eoil'], ['004744', 'eGEI'], ['110003', 'eSSE50'], ['110020', 'HS300'], + ['110031', 'eHSI'], ['161130', 'eNASDAQ100'], ['110028', 'anxinB'], ['110022', 'eConsumption '], + ['161125', 'SPX500']] + + buysell = [] + for i in code: + # save(strfundcode=i ,numdays=365*1) + sb=fenxi(strfundcode=i, numdays=365 * 1) + if sb: + buysell.append([i,sb]) + for i in all_fund_list[0:50]: + # save(strfundcode=i ,numdays=365*1) + sb=fenxi(strfundcode=i, numdays=365 * 1) + if sb: + buysell.append([i,sb]) + return buysell + +s = sched.scheduler(time.time, time.sleep) +class DateEncoder(json.JSONEncoder): + def default(self, obj): + if isinstance(obj, datetime): + return obj.__str__() + return json.JSONEncoder.default(self, obj) + + # sleep() +#@itchat.msg_register(itchat.content.TEXT) +def print_content(msg): + print msg['Text'] +def readmsg(msg): + return msg +def send_email(text): + #text = self.claw_content() + #open('b.txt', 'w').write(str(text[1]) + '\n' + str(text[0]) + '\n') + username = 'lsl_cug@126.com' # input("请输入账号:") + password = '123456lsl' # input("请输入密码:") + sender = username + # sender='' + receiver = ['1627041882@qq.com','760140853@qq.com'] # '760140853@qq.com','xxxxxxxxxx@qq.com','xxxxxxxxxx@126.com','994992333@qq.com','1847725033@qq.com','1847725033@qq.com','849281511@qq.com' + if sender =='': + username = str(raw_input("Please Input Sender Email Address,for example:xxxxxxxxxx@126.com \n")) + sender = username + password = str(raw_input("Please Input Sender Password \n")) + receiver.append(str(raw_input("Please Input Receiver Address,for example:xxxxxxxxxx@qq.com \n"))) + iRec = 1 + while iRec>0: + CmdRec = ''#raw_input("Whether you want to add another Receiver Address(default n)? \n") + if CmdRec == 'y' or CmdRec == 'yes': + iRec = 1 + receiver.append(str(raw_input("Please Input Receiver Address,for example:xxxxxxxxxx@qq.com \n"))) + else: + iRec = 0 + + ConfirmRec = ''#raw_input("Confirm? \n") + if ConfirmRec == 'n' or ConfirmRec == 'no': + print("What Do You Want? Maybe run this program again.\n") + exit(1) + else: + for rece in receiver: + print("OK \n"+ username + " sendto " + rece) + # 创建一个带附件的实例 + msg = MIMEMultipart() + # msg = MIMEText(str(text), 'plain', 'utf-8') + msg['From'] = formataddr(['user', sender]) # 括号里的对应发件人邮箱昵称、发件人邮箱账号 + msg['To'] = ",".join(receiver) # 括号里的对应收件人邮箱昵称、收件人邮箱账号 + + subject_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + subject = '基金信息' + msg['Subject'] = subject + + # 邮件正文内容 + # print str(text[0]) + realText = text#sendmsg #str(text) #'\n'+str(subject_time)+'：主人，有人来招人啦！^—^\n'+ '海投网：'+str(text[1])+'.'#+str(text[0])#'地大就业网招聘公告：'+str(text1[1])+'地大就业网gis等招聘信息：'+str(text2[1])+'.'#+str(text1[0])+'\n'+str(text2[1])+'\n'+str(text2[0]) + print realText + msg.attach(MIMEText(realText, 'plain', 'utf-8')) + + # 构造附件1，传送当前目录下的 test.txt 文件 + # att1 = MIMEApplication(open('b.txt', 'rb').read()) + # 这里的filename可以任意写，写什么名字，邮件中显示什么名字 + # att1.add_header('Content-Disposition', 'attachment', filename='hjx.txt') + # msg.attach(att1) + # # 构造附件1，传送当前目录下的 test.txt 文件 + # att2 = MIMEApplication(open('b1.txt', 'rb').read()) + # # 这里的filename可以任意写，写什么名字，邮件中显示什么名字 + # att2.add_header('Content-Disposition', 'attachment', filename='b1.txt') + # msg.attach(att2) + # att3 = MIMEApplication(open('b2.txt', 'rb').read()) + # # 这里的filename可以任意写，写什么名字，邮件中显示什么名字 + # att3.add_header('Content-Disposition', 'attachment', filename='b2.txt') + # msg.attach(att3) + smtpserver = 'smtp.126.com' + try: + smtp = smtplib.SMTP(smtpserver,25) + smtp.starttls() + smtp.login(username, password) + smtp.sendmail(sender, receiver, msg.as_string()) + smtp.quit() + print(u"邮件发送成功") + except smtplib.SMTPException, e: + print(u"Error: 无法发送邮件:" + str(e)) + smtp.quit() + try: + username = 'lishulincug@163.com' # input("请输入账号:") + password = '123456lsl' # input("请输入密码:") + sender = username + # sender='' + # receiver = [ '1627041882@qq.com','994992333@qq.com','1847725033@qq.com'] # 'xxxxxxxxxx@qq.com','xxxxxxxxxx@126.com','994992333@qq.com','1847725033@qq.com' + msg['From'] = formataddr(['lishulin', sender]) # 括号里的对应发件人邮箱昵称、发件人邮箱账号 + msg['To'] = ",".join(receiver) # 括号里的对应收件人邮箱昵称、收件人邮箱账号 + smtpserver = 'smtp.163.com' + smtp = smtplib.SMTP(smtpserver,25) + smtp.starttls() + smtp.login(username, password) + for jj in receiver: + smtp.sendmail(sender, jj, msg.as_string()) + time.sleep(35) + smtp.quit() + print(u"邮件发送成功") + except smtplib.SMTPException, e1: + print(u"Error: 无法发送邮件:"+str(e1)) + smtp.quit() + try: + username = '15623863340@sina.cn' # input("请输入账号:") + password = '133499' # input("请输入密码:") + sender = username + # sender='' + # receiver = [ '1627041882@qq.com','994992333@qq.com','1847725033@qq.com'] # 'xxxxxxxxxx@qq.com','xxxxxxxxxx@126.com','994992333@qq.com','1847725033@qq.com' + msg['From'] = formataddr(['15623863340@sina.cn', sender]) # 括号里的对应发件人邮箱昵称、发件人邮箱账号 + msg['To'] = ",".join(receiver) # 括号里的对应收件人邮箱昵称、收件人邮箱账号 + smtpserver = 'smtp.sina.cn' + smtp = smtplib.SMTP(smtpserver,25) + smtp.starttls() + smtp.login(username, password) + smtp.sendmail(sender, receiver, msg.as_string()) + smtp.quit() + print(u"邮件发送成功") + except smtplib.SMTPException, e2: + print(u"Error: 无法发送邮件:" + str(e2)) + smtp.quit() +def deb_print(): + now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + H=now[9:11] + M = now[12:14] + S = now[15:] + # print now + print H,M, S + return H,M, S + +def check_time(H, M,S): + strtoday1 = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d') + sdatetime = datetime.datetime.strptime(strtoday1, '%Y-%m-%d') + sdatetime.isoweekday() + #if sdatetime.isoweekday() == 7: + if(H == "14" and int(M) == 30 and S == "20")and(sdatetime.isoweekday() != 7)and(sdatetime.isoweekday() != 6):#(H == "14" and M == "08" and S == "10") or + # itchat.auto_login(hotReload=True) + # itchat.run + # curTime = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + # itchat.send(curTime, 'filehelper') + # itchat.send(str(curTime), toUserName='@9f545f6d5c3f89aa63956c3f386733232f7176569f71a26f477909c02828d735') + buysell = main_run(all_fund_list) + print(str(buysell)) + sendmsg=[] + # f = open("buysell.txt",'a') + for i in buysell: + # write_str = str(i) + '\n' + # f.write(write_str) + st = '' + if i[1]['buy'] != []: + st = ' 买 ' + for j in i[1]['buy']: + st += str(j) + ' ' + elif i[1]['sell'] != []: + st += ' 卖 ' + for k in i[1]['sell']: + st += str(k) + ' ' + if st != '': + sendmsg.append(str(i[0]) + st+(' http://fund.eastmoney.com/%s.html'%i[0][0]) +(' http://www.efunds.com.cn/html/fund/%s_fundinfo.htm '%i[0][0])) + send_email(sendmsg) + # itchat.send(str(i[0]) + st+(' http://fund.eastmoney.com/%s.html'%i[0][0]) +('-- http://www.efunds.com.cn/html/fund/%s_fundinfo.htm'%i[0][0]), 'filehelper') + # itchat.send(str(i[0]) + st, toUserName='@9f545f6d5c3f89aa63956c3f386733232f7176569f71a26f477909c02828d735') + s.enter(1, 1, check_time, deb_print()) + + +def main1(): + now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + H = now[9:11] + M = now[12:14] + S = now[15:] + code = [['002963', 'egold'], ['003321', 'eoil'], ['004744', 'eGEI'], ['110003', 'eSSE50'], ['110020', 'HS300'], + ['110031', 'eHSI'], ['161130', 'eNASDAQ100'], ['110028', 'anxinB'], ['110022', 'eConsumption '], + ['161125', 'SPX500']] + all_fund_list = main_zf(code) + sign=macdmain(code) + if (int(H)): # >= 19(H == "14" and M == "08" and S == "10") or + buysell1 = main_run(all_fund_list) + print(str(buysell1)) + sendmsg = '' + # f = open("buysell.txt",'a') + for j in sign: + sendmsg +=str(j[0])+','+str(j[1])+','+str(j[2][0])+','+str(j[2][1][0])+','+ (' http://fund.eastmoney.com/%s.html' % j[0][0])+'\n' + for i in buysell1: + # write_str = str(i) + '\n' + # f.write(write_str) + st = ' ' + if i[1]['buy'] != []: + st = ' 买 ' + for j in i[1]['buy']: + for h in j: + st += h + str(j[h]) + ' ' + elif i[1]['sell'] != []: + st += ' 卖 ' + for k in i[1]['sell']: + for t in k: + st += t + str(k[t]) + ' ' + if st != ' ': + sendmsg += i[0][0] + ',' + i[0][1] + st + (' http://fund.eastmoney.com/%s.html' % i[0][0]) + ( + ' http://www.efunds.com.cn/html/fund/%s_fundinfo.htm' % i[0][0]) + '\n' + # print sendmsg + send_email(sendmsg) + while True: + now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + # date = datetime.datetime.strftime(datetime.datetime.now(), "%Y%m%d") + H = now[9:11] + M = now[12:14] + S = now[15:] + # print H,S,M + print 'Start:'+'13:05' +' and 14:30 '+'send to email.'+"\n" +'please wait:' + + # print time.localtime() + # print time.strftime("%y-%m-%d %H:%M:%S",time.localtime()) + # xiaozhao = Xiaozhao() + # xiaozhao.send_email() + s.enter(1, 1, check_time, deb_print()) + s.run() + +if __name__=='__main__': + # itchat.auto_login(hotReload=True) + # itchat.run + # + # curTime = now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + # itchat.send(curTime, 'filehelper') + # buysell = one_predict.main_run() + # f = open("buysell.txt",'a') + # for i in buysell: + # # write_str = str(i) + '\n' + # # f.write(write_str) + # st = '' + # if i[1]['buy'] != []: + # st = u'买入点' + # for j in i[1]['buy']: + # st += str(j) + ' ' + # elif i[1]['sell'] != []: + # st += u' 卖出点' + # for k in i[1]['sell']: + # st += str(k) + ' ' + # if st != '': + # itchat.send(str(i[0]) + st, 'filehelper') + # itchat.send(str(i[0]) + st,toUserName='@9f545f6d5c3f89aa63956c3f386733232f7176569f71a26f477909c02828d735') + all_fund_list = main_zf() + main1() + # f.close() + # itchat.send("@fil@%s" % 'buysell.txt', 'filehelper') + # for (name, fund_id) in ids.items(): + # url = 'http://fund.eastmoney.com/%s.html' % fund_id + # value = spider(url) + # sign = value[0] + # value_num = value[1:-1] + # name = name.decode('utf-8') + # buysell=one_predict.main_run() + # itchat.send(buysell , 'filehelper') + # if(sign == '-' and float(value_num)>2.5): + # content = u'%s跌幅很大，可以补仓'+sign+value_num + # itchat.send(content % name, 'filehelper') + # else: + # content = u'%s 不需要补仓'+sign+value_num + # itchat.send(content % name, 'filehelper') diff --git a/trade_process/fibonacci.py b/trade_process/fibonacci.py index bca4f75..8589329 100644 --- a/trade_process/fibonacci.py +++ b/trade_process/fibonacci.py @@ -1,17 +1,17 @@ -#coding:utf8 - -import tushare as ts -import data_process.data_get as dg - -def fibonacci(): - df_gem = ts.get_gem_classified() #创业板 - - for ix, row in df_gem.iterrows(): - code = row['code'] - df_price = dg.get_stock_k_line(code, date_start='2015-01-04') - print df_price.head() - returns = df_price['close'].pct_change() - returns[0] = 0 - print returns - +#coding:utf8 + +import tushare as ts +import data_process.data_get as dg + +def fibonacci(): + df_gem = ts.get_gem_classified() #创业板 + + for ix, row in df_gem.iterrows(): + code = row['code'] + df_price = dg.get_stock_k_line(code, date_start='2015-01-04') + print df_price.head() + returns = df_price['close'].pct_change() + returns[0] = 0 + print returns + fibonacci() \ No newline at end of file diff --git a/trade_process/fund_zf.py b/trade_process/fund_zf.py new file mode 100644 index 0000000..34cd54e --- /dev/null +++ b/trade_process/fund_zf.py @@ -0,0 +1,313 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +import datetime +import glob +import json +import sys +import urllib2 +reload(sys) +sys.setdefaultencoding('utf-8') + + +# 是否已在list中 +def get_index(fund_code, all_fund_list): + fund_num = len(all_fund_list) + fund_index = 0 + while fund_index < fund_num: + if fund_code in all_fund_list[fund_index]: + break; + fund_index += 1 + + return fund_index + + +# 获取基金类型 +def get_type(fund_code, all_fund_list): + fund_type = 'none' + for fund in all_fund_list: + if fund_code in fund: + fund_type = fund[3] + break + + return fund_type + + +def main_zf(fund): + # 当前日期 + strtoday = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d') + tdatetime = datetime.datetime.strptime(strtoday, '%Y-%m-%d') + print strtoday + + # 过去20天日期 + s20datetime = tdatetime - datetime.timedelta(days=40) + strs20date = datetime.datetime.strftime(s20datetime, '%Y-%m-%d') + + # 去年今日 + sdatetime = tdatetime - datetime.timedelta(days=365) + strsdate = datetime.datetime.strftime(sdatetime, '%Y-%m-%d') + print strsdate + + # 获取所有基金列表 用于查询类型 + all_fund = [] + fundlist_files = glob.glob('./trade_process/fundlist-*.txt') + file_object = open(fundlist_files[0], 'r') + try: + all_funds_txt = file_object.read() + # print all_funds_txt + finally: + file_object.close() + + all_funds_txt = all_funds_txt[all_funds_txt.find('=') + 2:all_funds_txt.rfind(';')] + all_fund = json.loads(all_funds_txt.decode('utf-8')) + + # 1、 获取近 1 3 6 增长率top50 + + month_num = 1 + month_list = [1,36] + all_fund_list = [] + dx='1' + ft=['all','hh','qdii','zq','zs'] #基金类型 + ftk=ft[0] + for int_month in month_list: + try: + if fund =='gf' or fund =='efund': + if fund == 'gf': + # 广发排行 + fundcode='80000248' + elif fund == 'efund': + fundcode = '80000229' + if int_month/12 >= 1 : + url = 'http://fund.eastmoney.com/api/FundGuide.aspx?dt=0&sd=&ed=&cp='+fundcode+'&sc='+str(int_month)+'n&st=desc&pi=1&pn=80&zf=diy&sh=list&rnd=0' + elif int_month == 0.25: + url = 'http://fund.eastmoney.com/api/FundGuide.aspx?dt=0&sd=&ed=&cp='+fundcode+'&sc=z&st=asc&pi=1&pn=80&zf=diy&sh=list&rnd=' + else: + url = 'http://fund.eastmoney.com/api/FundGuide.aspx?dt=0&sd=&ed=&cp='+fundcode+'&sc='+str(int_month)+'y&st=desc&pi=1&pn=80&zf=diy&sh=list&rnd=' + # 易方达1周排行 + # elif fund == 'efund': + # if int_month / 12 >= 1: + # url = 'http://fund.eastmoney.com/api/FundGuide.aspx?dt=0&sd=&ed=&cp=80000229&sc=z&st=desc&pi=1&pn=80&zf=diy&sh=list&rnd=0.8037251392454077' + # elif int_month == 0.25: + # # 易方达6月排行 + # url = 'http://fund.eastmoney.com/api/FundGuide.aspx?dt=0&sd=&ed=&cp=80000229&sc=6y&st=desc&pi=1&pn=80&zf=diy&sh=list&rnd=0.2738232363825983' + # else: + # url = 'http://fund.eastmoney.com/api/FundGuide.aspx?dt=0&sd=&ed=&cp=80000229&sc=6y&st=desc&pi=1&pn=80&zf=diy&sh=list&rnd=0.2738232363825983' + else: + if int_month/12 >= 1: + # 1年增幅 + print ('get nearly %s year top 200 funds',int_month/12) + url = 'http://fund.eastmoney.com/data/rankhandler.aspx?op=ph&dt=kf&ft='+ftk+'&rs=1nzf,100&gs=0&sc='+str(int_month/12)+'nzf&st=desc&sd=' + \ + strsdate + '&ed=' + strs20date + '&qdii=&tabSubtype=,,,,,&pi=1&pn=200&dx='+dx + # elif int_month == 12: + # # 1年增幅 + # print 'get nearly 1 year top 200 funds' + # url = 'http://fund.eastmoney.com/data/rankhandler.aspx?op=ph&dt=kf&ft='+ftk+'&rs=1nzf,100&gs=0&sc=1nzf&st=desc&sd=' + \ + # strsdate + '&ed=' + strs20date + '&qdii=&tabSubtype=,,,,,&pi=1&pn=200&dx='+dx + # elif int_month == 36: + # # 1年增幅 + # print 'get nearly 3 year top 200 funds' + # url = 'http://fund.eastmoney.com/data/rankhandler.aspx?op=ph&dt=kf&ft='+ftk+'&rs=1nzf,100&gs=0&sc=3nzf&st=desc&sd=' + \ + # strsdate + '&ed=' + strs20date + '&qdii=&tabSubtype=,,,,,&pi=1&pn=200&dx='+dx + # elif int_month == 60: + # # 1年增幅 + # print 'get nearly 1 year top 200 funds' + # url = 'http://fund.eastmoney.com/data/rankhandler.aspx?op=ph&dt=kf&ft='+ftk+'&rs=1nzf,100&gs=0&sc=5nzf&st=desc&sd=' + \ + # strsdate + '&ed=' + strs20date + '&qdii=&tabSubtype=,,,,,&pi=1&pn=200&dx='+dx + elif int_month == 0.25: + # 1年增幅 + print 'get nearly 1 week down 200 funds' + url = 'http://fund.eastmoney.com/api/FundGuide.aspx?dt=0&sd=&ed=&sc=z&st=asc&pi=1&pn=300&zf=diy&sh=list&rnd=' + # url = 'http://fund.eastmoney.com/data/rankhandler.aspx?op=ph&dt=kf&ft='+ftk+'&rs=zzf,100&gs=0&sc=zzf&st=dasc&sd=' + \ + # strsdate + '&ed=' + strs20date + '&qdii=&tabSubtype=,,,,,&pi=1&pn=200&dx='+dx + elif int_month == 1: + print 'get nearly ' + str(int_month) + ' months down 200 funds' + url = 'http://fund.eastmoney.com/api/FundGuide.aspx?dt=0&sd=&ed=&sc=1y&st=desc&pi=1&pn=300&zf=diy&sh=list&rnd=' + # url = 'http://fund.eastmoney.com/data/rankhandler.aspx?op=ph&dt=kf&ft='+ftk+'&rs=' + str(int_month) + \ + # 'yzf,200&gs=0&sc=' + str(int_month) + 'yzf&st=dasc&sd=' + strsdate + '&ed=' + strs20date + \ + # '&qdii=&tabSubtype=,,,,,&pi=1&pn=200&dx=' + dx + else: + # 前 n 月增幅 + print 'get nearly ' + str(int_month) + ' months top 200 funds' + url = 'http://fund.eastmoney.com/data/rankhandler.aspx?op=ph&dt=kf&ft='+ftk+'&rs=' + str(int_month) + \ + 'yzf,200&gs=0&sc=' + str(int_month) + 'yzf&st=desc&sd=' + strsdate + '&ed=' + strs20date + \ + '&qdii=&tabSubtype=,,,,,&pi=1&pn=200&dx='+dx + print url + '\n' + + # if int_month == 24: + # 1年增幅 + # 过去20天日期 + s20datetime = tdatetime - datetime.timedelta(days=10) + strs20date = datetime.datetime.strftime(s20datetime, '%Y-%m-%d') + + # 去年今日 + sdatetime = s20datetime - datetime.timedelta(days=int_month*30) + strsdate = datetime.datetime.strftime(sdatetime, '%Y-%m-%d') + # print strsdate + # print 'get nearly ' + str(int_month) + ' months top 200 funds' + # url = 'http://fund.eastmoney.com/data/rankhandler.aspx?op=ph&dt=kf&ft=all,50&gs=0&st=desc&sd=' + \ + # strsdate + '&ed=' + strs20date + '&qdii=&tabSubtype=,,,,,&pi=1&pn=200&dx=1' + # print url + '\n' + response = urllib2.urlopen(url) + except urllib2.HTTPError, e: + print e + urllib_error_tag = True + except StandardError, e: + print e + urllib_error_tag = True + else: + urllib_error_tag = False + + if urllib_error_tag == True: + print 'error to get date,check network!\n' + sys.exit(1) + + # print response + all_rank_txt = response.read().decode('utf-8') + all_rank_txt = all_rank_txt[all_rank_txt.find('["'):all_rank_txt.rfind('"]') + 2] + # print all_rank_txt + # 编码问题 NND utf-8 不行啊 + all_rank_list = json.loads(all_rank_txt) + # print 'rank sum:' + str(len(all_rank_list)) + '\n\n' + + fund_rank = 1 + for rank_txt in all_rank_list: + # print rank_txt + '\n' + # 0 1 2 3 4 5 5 6 7 8 8 10 11 12 13 + # 单位 累计 日增 近1 近2 近3 今年 + # 基金代码 基金简称 日期 净值 净值 长率 周 近1月 近3月 近6月 近1年 年 年 来 成立来 手续费 + # 002425,金鹰保本混合C,JYBBHHC,2017-03-28,1.1120,1.6120,0,0.09,-0.4477,0.6335,0.5082,61.5167,,,0.09,61.5167,2016-03-07,1,61.5167,1.00%,0.10%,1,0.10%,1, + # 002441,德邦新添利债券C,DBXTLZQC,2017-03-28,1.1365,1.5165,0.0264,0.1409,0.4241,1.2923,1.1904,50.4788,,,1.1571,50.8371,2016-02-17,1,50.4788,,0.00%,,,, + # 共25项 + rank_list = rank_txt.split(',') + # print str(len(rank_list)) + '\n' + # 如果是第1个月 直接append + if 1 == month_num: + fund_list = [] + + fund_type = get_type(rank_list[0], all_fund) + + fund_list.append(rank_list[0]) + fund_list.append(rank_list[1] + '\t' + fund_type) + fund_list.append(str(fund_rank)) + + all_fund_list.append(fund_list) + else: + # 查找是否已在list中 + fund_num = len(all_fund_list) + fund_index = get_index(rank_list[0], all_fund_list) + if fund_index < fund_num: + # list中已存在 只append rank 和 rate + # print fund_code + '\t' + str(fund_index) + '\t' + str(all_fund_list[fund_index]) + all_fund_list[fund_index].append(str(fund_rank)) + else: + # 如果不存在 不仅需要将其加入list中 同样需要将其他几个 rank 和 rate 补上 + # 如果本fundcode在前几个文件中不存在 就是倒数第一 第100名吧 rank 默认100 rate 默认0 + # print fund_code + '\tnot found!' + fund_list = [] + # code name type + fund_type = get_type(rank_list[0], all_fund) + + fund_list.append(rank_list[0]) + fund_list.append(rank_list[1] + '\t' + fund_type) + # 补上 前几个文件的 rank 和 rate + for i in range(month_num - 1): + fund_list.append('100') + # 加上当前的rank 和 rate + fund_list.append(str(fund_rank)) + + # 将其加入列表 + all_fund_list.append(fund_list) + + # 处理下一个 + fund_rank += 1 + + # 还有 从第2个月开始 如果有fund 不在本文件中出现 就是倒数第一 第100名吧 还要将rank 和 rate 补上 擦 挺复杂 + if month_num > 1: + # 最长的len 为 2 + month_num + fund_len = 2 + month_num + fund_num = len(all_fund_list) + fund_index = 0 + while fund_index < fund_num: + if len(all_fund_list[fund_index]) < fund_len: + all_fund_list[fund_index].append('100') + + fund_index += 1 + + # 处理下一个月 + month_num += 1 + + print month_num + s1 = [] + for j in range(0, 3): + s='' + for i in range(0, len(all_fund_list[j])): + s += str(all_fund_list[j][i]) + ', ' + s1.append(s) + for j in range(0, 3): + print s1[j] + # print all_fund_list[0] + # print all_fund_list[1] + + # 2、计算平均排名 + fund_num = len(all_fund_list) + print fund_num + print '\n\n' + fund_index = 0 + while fund_index < fund_num: + sum_rank = 0 + # 有几个文件 就有几个排名 rank的index 2 3 4 5 + for i in range(month_num - 1): + sum_rank += int(all_fund_list[fund_index][2 + i]) + + # 计算平均排名 + avg_rank = float('%.2f' % (float(sum_rank) / (month_num - 1))) + + # 加入avg_rank + all_fund_list[fund_index].append(avg_rank) + + # 处理下一个 + fund_index += 1 + + # 3、排序 写文件 打印 + # avg_rank的index 1 + month_num + all_fund_list.sort(key=lambda fund: fund[1 + month_num]) + + s2=[] + for j in range(0,3): + s='' + for i in range(0,len(all_fund_list[j])): + s+= str(all_fund_list[j][i])+', ' + s2.append(s) + for j in range(0, 3): + print s2[j] + print '--------------------------------------' + file_object = open(u'基金综合排名结果.txt', 'w') + int_rank = 1 + try: + for fund_list in all_fund_list: + print str(int_rank) + '\t' + '\t'.join('{0}'.format(n) for n in fund_list) + file_object.write(str(int_rank) + '\t' + '\t'.join('{0}'.format(n) for n in fund_list) + '\n') + int_rank += 1 + finally: + file_object.close() + return all_fund_list + # sys.exit(0) +def getRankFund(): + # 全部基金排行 --基金导购 + url='http://fund.eastmoney.com/api/FundGuide.aspx?dt=0&sd=&ed=&sc=z&st=desc&pi=1&pn=200&zf=diy&sh=list&rnd=0.4893989337468809' + #广发排行 + gfUrl='http://fund.eastmoney.com/api/FundGuide.aspx?dt=0&sd=&ed=&cp=80000248&sc=z&st=desc&pi=1&pn=80&zf=diy&sh=list&rnd=0.15840037296002807' + # 易方达1周排行 + EfundUrl='http://fund.eastmoney.com/api/FundGuide.aspx?dt=0&sd=&ed=&cp=80000229&sc=z&st=desc&pi=1&pn=80&zf=diy&sh=list&rnd=0.8037251392454077' + #易方达6月排行 + EfundUrl6='http://fund.eastmoney.com/api/FundGuide.aspx?dt=0&sd=&ed=&cp=80000229&sc=6y&st=desc&pi=1&pn=80&zf=diy&sh=list&rnd=0.2738232363825983' +def test(): + month_num = 1 + + print month_num + +if __name__ == "__main__": + reload(sys) + sys.setdefaultencoding('utf-8') + # sys.setdefaultencoding('utf-8') + # test() + main_zf() diff --git a/trade_process/fund_zf.pyc b/trade_process/fund_zf.pyc new file mode 100644 index 0000000..ecd33a5 Binary files /dev/null and b/trade_process/fund_zf.pyc differ diff --git a/trade_process/fundlist-2017-03-27.txt b/trade_process/fundlist-2017-03-27.txt new file mode 100644 index 0000000..7532cf6 --- /dev/null +++ b/trade_process/fundlist-2017-03-27.txt @@ -0,0 +1 @@ +var r = 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\ No newline at end of file diff --git a/trade_process/kline_technical_index.py b/trade_process/kline_technical_index.py index 1128b3e..3c997e7 100644 --- a/trade_process/kline_technical_index.py +++ b/trade_process/kline_technical_index.py @@ -1,252 +1,252 @@ -#coding=utf-8 -""" - K线图 - 技术指标 - http://kekefund.com/2015/12/10/japan-candle-diagram-technique/ -""" - -import wrapcache -from tqdm import tqdm - -from data_process.data_get import get_all_stock_codes, get_stock_k_line - -@wrapcache.wrapcache(timeout=24*60*60) -def line_hammer_and_hang(code, df_code): - """ - 锤子线(底部反转) | 上吊线(顶部反转) - :param df_code: 一只股票代码的DataFrame - :return: - """ - - #print df_code[:3][['code', 'date','open','high','close','close','low']] - - dates_up = [] #锤子线 - dates_down = [] #上吊线 - for ix, row in df_code.iterrows(): - if ix - 3 < 0: - continue - - len_yingxian_down = min(row['close'], row['open']) - row['low'] #下影线长度 - len_yingxian_up = row['high'] - max(row['close'], row['open']) #上影线长度 - len_solid = abs(row['close'] - row['open']) # 实体线高度 - - if len_yingxian_down >= 2 * len_solid and len_yingxian_up < len_solid * 0.1: - trend = _judge_trend(df_code.ix[ix-3:ix+1]) - if trend == -1: # 下降趋势 - dates_up.append(str(row['date'])) - elif trend == 1: # 上升趋势 - dates_down.append(str(row['date'])) - - code_dict = {} - code_dict[code] = {'up': dates_up, 'down':dates_down} - - return code_dict - -def shape_devour(code, df_code): - """ - 吞没形态 - --- 看涨吞没形态 - --- 看跌吞没形态 - :param code: - :param df_code: - :return: - """ - - dates_up = [] #看涨 - dates_down = [] #看跌 - - for ix, row in df_code.iterrows(): - if ix - 3 < 0: - continue - - #判断趋势 - trend = _judge_trend(df_code.ix[ix-3:ix]) - - #先判断形态 - row_pre = df_code.ix[ix-1] - - # 1,看涨吞没形态 - if trend == -1 and \ - row_pre.close < row_pre.open and \ - row.close > row_pre.open and \ - row.open < row_pre.close: - print row_pre - print row - dates_up.append(str(row['date'])) - - # 2,看跌吞没形态 - if trend == 1 and \ - (row_pre.close > row_pre.open) and \ - row.close < row_pre.open and \ - row.open > row_pre.close: - dates_down.append(str(row['date'])) - - code_dict = {} - code_dict[code] = {'up': dates_up, 'down':dates_down} - #print code_dict - return code_dict - -def shape_dark_cloud_cover_top(code, df_code): - """ - 乌云盖顶形态(顶部反转) | 透刺形态(底部反转) - :param code: - :param df_code: - :return: - """ - - dates_up = [] #看涨 - dates_down = [] #看跌 - - for ix, row in df_code.iterrows(): - if ix - 5 < 0: - continue - - #判断趋势 - pct_changes = df_code.ix[ix-5:ix].close.pct_change() - - if len(pct_changes[pct_changes > 0]) >= 3: # 必须近期总体趋势是向上的 - - row_pre = df_code.ix[ix-1] - if row_pre.close > row_pre.open and row.close < row.open: # 前一天是坚挺的白色实体, 当前天是黑色实体 - if row.open > row_pre.high + (row_pre.close + row_pre.open) * 0.2: # 当前天的开盘价超过了(前一天的最高价 + 系数) - if row.close < (row_pre.open + row_pre.close)/2 and row.close > row_pre.open: # 当前天的收盘价下穿到前一天白色实体的50% - dates_down.append(str(row.date)) - - elif len(pct_changes[pct_changes < 0]) >= 3: # 必须近期总体趋势是向下的 - - row_pre = df_code.ix[ix-1] - if row_pre.close < row_pre.open and row.close > row.open: # 前一天是黑色实体, 当前天是白色实体 - if row.open < row_pre.low - (row_pre.close + row_pre.open) * 0.2: # 当前天的开盘价低于(前一天的最低价 - 系数) - if row.close > (row_pre.open + row_pre.close)/2 and row.close < row_pre.open: # 当前天的收盘价上穿到前一天白色实体的50% - dates_up.append(str(row.date)) - code_dict = {} - code_dict[code] = {'up': dates_up, 'down':dates_down} - #print code_dict - return code_dict - -def shape_morning_and_evening_star(code, df_code): - """ - 启明星形态(底部反转) | 黄昏星形态(顶部反转) - 含十字线形态 - :return: dict - """ - dates_up = [] #看涨 - dates_down = [] #看跌 - - len_df = len(df_code) - for ix, row in df_code[1:len_df-1].iterrows(): - - row_pre = df_code.ix[ix-1] - row_post = df_code.ix[ix+1] - - if abs(row.open - row.close)/row.open < 0.02: # 星线 或 十字线 - if row_pre.close < row_pre.open and (row_pre.open-row_pre.close)/row_pre.open > 0.03: #前一天是黑色实体 且跌幅>3% - if max(row.open, row.close) < min(row_pre.close, row_pre.open): #当前天的实体部分与前一天的实体形成价格跳空 - if row_post.close > row_post.open and row_post.close > row_pre.close: #后一天是白色实体 且收盘价向上推进到前一天的黑色实体之内 - dates_up.append(str(row.date)) - - elif row_pre.close > row_pre.open and (row_pre.close-row_pre.open)/row_pre.open > 0.03: #前一天是白色实体 且涨幅>3% - if min(row.open, row.close) > max(row_pre.close, row_pre.open): #当前天的实体部分与前一天的实体形成价格跳空 - if row_post.close < row_post.open and row_post.close < row_pre.close: #后一天是黑色实体 且收盘价向下推进到前一天的白色实体之内 - dates_down.append(str(row.date)) - - - code_dict = {} - code_dict[code] = {'up': dates_up, 'down':dates_down} - #print code_dict - return code_dict - -def shape_meteor_and_invertedhammer_star(code, df_code): - """ - 流星形态 | 倒锤子线 - -- 非主要反转信号 - :return: dict - """ - dates_up = [] #看涨 - dates_down = [] #看跌 - - len_df = len(df_code) - for ix, row in df_code[5:len_df-1].iterrows(): - - row_pre = df_code.ix[ix-1] - row_post = df_code.ix[ix+1] - pct_changes = df_code.ix[ix-5:ix].close.pct_change() - - #定义流星: 1,流星实体部分较小; 2,白色或黑色皆可#流星上影线较长; 3,下影线几乎没有(长度小于实体的1/10) - meteor_star = abs(row.open - row.close)/row.open < 0.02 and \ - row.high-max(row.open, row.close) > abs(row.open - row.close) * 2 and \ - abs(row.low-min(row.open, row.close)) < abs(row.open - row.close) * 0.1 - if meteor_star: - #流星 - if len(pct_changes[pct_changes > 0]) >= 3: # 必须近期总体趋势是向上的 - if min(row.open, row.close) > max(row_pre.close, row_pre.open): # 当前天的实体部分与前一天的实体形成价格跳空 - if max(row_post.open, row_post.close) < min(row.close, row.open) or \ - (row_post.open - row_post.close)/row_post.open > 0.03: # 后一天向下跳空 或 跌幅>3% - dates_down.append(str(row.date)) - #倒锤子线 - if len(pct_changes[pct_changes < 0]) >= 3: # 必须近期总体趋势是向下的 - if max(row.open, row.close) < min(row_pre.close, row_pre.open): # 当前天的实体部分与前一天的实体形成价格跳空 - if min(row_post.open, row_post.close) > max(row.close, row.open) or \ - (row_post.close - row_post.open)/row_post.open > 0.03: # 后一天向上跳空 或 涨幅>3% - dates_up.append(str(row.date)) - - - code_dict = {} - code_dict[code] = {'up': dates_up, 'down':dates_down} - #print code_dict - return code_dict - -# 辅助函数 -def _judge_trend(df_code): - """ - 判断收盘价趋势 - :param df_code: - :return: 1:上升; -1:下降; 0:震荡 - """ - pct_changes = df_code.close.pct_change() - - if len(pct_changes[pct_changes > 0]) == 0: # 下降趋势 - return -1 - elif len(pct_changes[pct_changes < 0]) == 0: # 上升趋势 - return 1 - else: - return 0 - -def run(): - codes = list(get_all_stock_codes()) - codes.reverse() - codes = ['000725'] - result_list = [] - for code in tqdm(codes): - df = get_stock_k_line(code, date_start='2016-01-01') - - code_dict = line_hammer_and_hang(code, df) - result_list.append(code_dict) - - code_dict = shape_dark_cloud_cover_top(code, df) - result_list.append(code_dict) - - code_dict = shape_devour(code, df) - result_list.append(code_dict) - - code_dict = shape_morning_and_evening_star(code, df) - result_list.append(code_dict) - - code_dict = shape_meteor_and_invertedhammer_star(code, df) - result_list.append(code_dict) - - - - for result in result_list: - for code, d in result.items(): - if d['up']: - print code, " up:", d['up'] - - for result in result_list: - for code, d in result.items(): - if d['down']: - print code, " down:", d['down'] - - print 'end' -if __name__ == "__main__": - run() - +#coding=utf-8 +""" + K线图 - 技术指标 + http://kekefund.com/2015/12/10/japan-candle-diagram-technique/ +""" + +import wrapcache +from tqdm import tqdm + +from data_process.data_get import get_all_stock_codes, get_stock_k_line + +@wrapcache.wrapcache(timeout=24*60*60) +def line_hammer_and_hang(code, df_code): + """ + 锤子线(底部反转) | 上吊线(顶部反转) + :param df_code: 一只股票代码的DataFrame + :return: + """ + + #print df_code[:3][['code', 'date','open','high','close','close','low']] + + dates_up = [] #锤子线 + dates_down = [] #上吊线 + for ix, row in df_code.iterrows(): + if ix - 3 < 0: + continue + + len_yingxian_down = min(row['close'], row['open']) - row['low'] #下影线长度 + len_yingxian_up = row['high'] - max(row['close'], row['open']) #上影线长度 + len_solid = abs(row['close'] - row['open']) # 实体线高度 + + if len_yingxian_down >= 2 * len_solid and len_yingxian_up < len_solid * 0.1: + trend = _judge_trend(df_code.ix[ix-3:ix+1]) + if trend == -1: # 下降趋势 + dates_up.append(str(row['date'])) + elif trend == 1: # 上升趋势 + dates_down.append(str(row['date'])) + + code_dict = {} + code_dict[code] = {'up': dates_up, 'down':dates_down} + + return code_dict + +def shape_devour(code, df_code): + """ + 吞没形态 + --- 看涨吞没形态 + --- 看跌吞没形态 + :param code: + :param df_code: + :return: + """ + + dates_up = [] #看涨 + dates_down = [] #看跌 + + for ix, row in df_code.iterrows(): + if ix - 3 < 0: + continue + + #判断趋势 + trend = _judge_trend(df_code.ix[ix-3:ix]) + + #先判断形态 + row_pre = df_code.ix[ix-1] + + # 1,看涨吞没形态 + if trend == -1 and \ + row_pre.close < row_pre.open and \ + row.close > row_pre.open and \ + row.open < row_pre.close: + print row_pre + print row + dates_up.append(str(row['date'])) + + # 2,看跌吞没形态 + if trend == 1 and \ + (row_pre.close > row_pre.open) and \ + row.close < row_pre.open and \ + row.open > row_pre.close: + dates_down.append(str(row['date'])) + + code_dict = {} + code_dict[code] = {'up': dates_up, 'down':dates_down} + #print code_dict + return code_dict + +def shape_dark_cloud_cover_top(code, df_code): + """ + 乌云盖顶形态(顶部反转) | 透刺形态(底部反转) + :param code: + :param df_code: + :return: + """ + + dates_up = [] #看涨 + dates_down = [] #看跌 + + for ix, row in df_code.iterrows(): + if ix - 5 < 0: + continue + + #判断趋势 + pct_changes = df_code.ix[ix-5:ix].close.pct_change() + + if len(pct_changes[pct_changes > 0]) >= 3: # 必须近期总体趋势是向上的 + + row_pre = df_code.ix[ix-1] + if row_pre.close > row_pre.open and row.close < row.open: # 前一天是坚挺的白色实体, 当前天是黑色实体 + if row.open > row_pre.high + (row_pre.close + row_pre.open) * 0.2: # 当前天的开盘价超过了(前一天的最高价 + 系数) + if row.close < (row_pre.open + row_pre.close)/2 and row.close > row_pre.open: # 当前天的收盘价下穿到前一天白色实体的50% + dates_down.append(str(row.date)) + + elif len(pct_changes[pct_changes < 0]) >= 3: # 必须近期总体趋势是向下的 + + row_pre = df_code.ix[ix-1] + if row_pre.close < row_pre.open and row.close > row.open: # 前一天是黑色实体, 当前天是白色实体 + if row.open < row_pre.low - (row_pre.close + row_pre.open) * 0.2: # 当前天的开盘价低于(前一天的最低价 - 系数) + if row.close > (row_pre.open + row_pre.close)/2 and row.close < row_pre.open: # 当前天的收盘价上穿到前一天白色实体的50% + dates_up.append(str(row.date)) + code_dict = {} + code_dict[code] = {'up': dates_up, 'down':dates_down} + #print code_dict + return code_dict + +def shape_morning_and_evening_star(code, df_code): + """ + 启明星形态(底部反转) | 黄昏星形态(顶部反转) + 含十字线形态 + :return: dict + """ + dates_up = [] #看涨 + dates_down = [] #看跌 + + len_df = len(df_code) + for ix, row in df_code[1:len_df-1].iterrows(): + + row_pre = df_code.ix[ix-1] + row_post = df_code.ix[ix+1] + + if abs(row.open - row.close)/row.open < 0.02: # 星线 或 十字线 + if row_pre.close < row_pre.open and (row_pre.open-row_pre.close)/row_pre.open > 0.03: #前一天是黑色实体 且跌幅>3% + if max(row.open, row.close) < min(row_pre.close, row_pre.open): #当前天的实体部分与前一天的实体形成价格跳空 + if row_post.close > row_post.open and row_post.close > row_pre.close: #后一天是白色实体 且收盘价向上推进到前一天的黑色实体之内 + dates_up.append(str(row.date)) + + elif row_pre.close > row_pre.open and (row_pre.close-row_pre.open)/row_pre.open > 0.03: #前一天是白色实体 且涨幅>3% + if min(row.open, row.close) > max(row_pre.close, row_pre.open): #当前天的实体部分与前一天的实体形成价格跳空 + if row_post.close < row_post.open and row_post.close < row_pre.close: #后一天是黑色实体 且收盘价向下推进到前一天的白色实体之内 + dates_down.append(str(row.date)) + + + code_dict = {} + code_dict[code] = {'up': dates_up, 'down':dates_down} + #print code_dict + return code_dict + +def shape_meteor_and_invertedhammer_star(code, df_code): + """ + 流星形态 | 倒锤子线 + -- 非主要反转信号 + :return: dict + """ + dates_up = [] #看涨 + dates_down = [] #看跌 + + len_df = len(df_code) + for ix, row in df_code[5:len_df-1].iterrows(): + + row_pre = df_code.ix[ix-1] + row_post = df_code.ix[ix+1] + pct_changes = df_code.ix[ix-5:ix].close.pct_change() + + #定义流星: 1,流星实体部分较小; 2,白色或黑色皆可#流星上影线较长; 3,下影线几乎没有(长度小于实体的1/10) + meteor_star = abs(row.open - row.close)/row.open < 0.02 and \ + row.high-max(row.open, row.close) > abs(row.open - row.close) * 2 and \ + abs(row.low-min(row.open, row.close)) < abs(row.open - row.close) * 0.1 + if meteor_star: + #流星 + if len(pct_changes[pct_changes > 0]) >= 3: # 必须近期总体趋势是向上的 + if min(row.open, row.close) > max(row_pre.close, row_pre.open): # 当前天的实体部分与前一天的实体形成价格跳空 + if max(row_post.open, row_post.close) < min(row.close, row.open) or \ + (row_post.open - row_post.close)/row_post.open > 0.03: # 后一天向下跳空 或 跌幅>3% + dates_down.append(str(row.date)) + #倒锤子线 + if len(pct_changes[pct_changes < 0]) >= 3: # 必须近期总体趋势是向下的 + if max(row.open, row.close) < min(row_pre.close, row_pre.open): # 当前天的实体部分与前一天的实体形成价格跳空 + if min(row_post.open, row_post.close) > max(row.close, row.open) or \ + (row_post.close - row_post.open)/row_post.open > 0.03: # 后一天向上跳空 或 涨幅>3% + dates_up.append(str(row.date)) + + + code_dict = {} + code_dict[code] = {'up': dates_up, 'down':dates_down} + #print code_dict + return code_dict + +# 辅助函数 +def _judge_trend(df_code): + """ + 判断收盘价趋势 + :param df_code: + :return: 1:上升; -1:下降; 0:震荡 + """ + pct_changes = df_code.close.pct_change() + + if len(pct_changes[pct_changes > 0]) == 0: # 下降趋势 + return -1 + elif len(pct_changes[pct_changes < 0]) == 0: # 上升趋势 + return 1 + else: + return 0 + +def run(): + codes = list(get_all_stock_codes()) + codes.reverse() + codes = ['000725'] + result_list = [] + for code in tqdm(codes): + df = get_stock_k_line(code, date_start='2016-01-01') + + code_dict = line_hammer_and_hang(code, df) + result_list.append(code_dict) + + code_dict = shape_dark_cloud_cover_top(code, df) + result_list.append(code_dict) + + code_dict = shape_devour(code, df) + result_list.append(code_dict) + + code_dict = shape_morning_and_evening_star(code, df) + result_list.append(code_dict) + + code_dict = shape_meteor_and_invertedhammer_star(code, df) + result_list.append(code_dict) + + + + for result in result_list: + for code, d in result.items(): + if d['up']: + print code, " up:", d['up'] + + for result in result_list: + for code, d in result.items(): + if d['down']: + print code, " down:", d['down'] + + print 'end' +if __name__ == "__main__": + run() + diff --git a/trade_process/live_trade.py b/trade_process/live_trade.py index c4626b3..b8851d8 100644 --- a/trade_process/live_trade.py +++ b/trade_process/live_trade.py @@ -1,152 +1,139 @@ -#!/usr/local/bin/python3 -#coding=utf-8 - -import sys -import platform -from apscheduler.schedulers.blocking import BlockingScheduler -import logging -logging.basicConfig() - -if platform.system() == 'Windows': - sys.path.append('../') -else: - sys.path.append('/Users/cbb/Documents/pythonspace/stock-master/') - -import ctypes -from ConfigParser import ConfigParser - -import data_process.online_data as OnlineData -from trade_process.strategy.macd_live_test import MAStrategy -from util.stockutil import fn_timer as fn_timer_ -from data_process.data_get import * -from data_process.Stock import Stock -import util.stockutil as util -from util.codeConvert import GetNowTime -from util.send_mail import send_email_163 -from init import * -from strategy.stop_loss import stop_loss_by_price - - - - - -#stock_list =['600893'] - - - - -def main(): - - #获取实时股价 - try: - stockList = OnlineData.getAllChinaStock() - except Exception, e: - errorLogger.logger.error(encode_wrap('获取实时估价失败! ') + str(e)) - return - - #止盈止损判断 - judgements = stop_loss_by_price() - if judgements: - for judgement in judgements: - infoLogger.logger.info(encode_wrap('卖出:{}'.format(judgement[0]))) - - - cf = ConfigParser() - cf.read(config_file_path) - threshold_buy = cf.get('trade_threshold', 'Threshold_Buy_Count') - threshold_sale = cf.get('trade_threshold', 'Threshold_Sale_Count') - infoLogger.logger.info(encode_wrap('阈值: Buy(%s) ,Sale(%s)' %(threshold_buy, threshold_sale))) - - # 监听股票列表 - stock_list = ['600000','600048', '600011', '002600', '002505', '000725', '000783', '300315', '002167', '601001',\ - '600893', '000020', '600111'] - - print '>>>>>Calcute ...' - stock_buy_list, stock_sale_list = live_mult_stock(stockList) - if len(stock_buy_list) == 0 and len([stock for stock in stock_sale_list if stock.code in stock_list]) == 0: - infoLogger.logger.info(encode_wrap('没有合适的买卖机会，请耐心等待')) - return - - str_all = '{}\n\n\n买入\n'.format(GetNowTime()) - - infoLogger.logger.info(encode_wrap('{}\n\n买入'.format(GetNowTime()))) - for stock in stock_buy_list: - infoLogger.logger.info('Buy now! ' + stock.str_print()) - str_all = str_all + stock.str_print() + '\n' - - #print encode_wrap('\n卖出:')f - print '\n' - infoLogger.logger.info(encode_wrap('卖出')) - str_all = '\n\n\n' + str_all + '卖出\n' - for stock in stock_sale_list: - # if stock.code in stock_list: - infoLogger.logger.info('Sale now! ' + stock.str_print()) - #print '>' * 3, 'Sale now!', encode_wrap(stock.name), stock.code, stock.current, (float(stock.current)-float(stock.close))/float(stock.close)*100, '%' - str_all = str_all + stock.str_print() + '\n' - - send_email_163(subject='MA Strategy Results', content=str_all) - - -@fn_timer_ -def live_mult_stock(stockClassList): -# pool = ThreadPool(processes=4) -# pool.map(live_single_stock, stockClassList) -# pool.close() -# pool.join() - - stock_buy_list = [] - stock_sale_list = [] - for stock in stockClassList: - try: - live_single_stock(stock) - if stock.signal > 0: - stock_buy_list.append(stock) - elif stock.signal < 0: - stock_sale_list.append(stock) - except Exception, e: - errorLogger.logger.error((str(e))) - return stock_buy_list, stock_sale_list - -def live_single_stock(stock): - try: - # 多线程提醒实时买卖 - if float(stock.current) == 0.0: - #print '>>>', stock.name,'>>>停牌!', stock.close, stock.time - return 0, '' - - #取最近三个月的收盘价 - d_90_day = datetime.date.today() + datetime.timedelta(days=-90) - date_90_day = d_90_day.strftime("%Y-%m-%y") - df = get_stock_k_line(stock.code, date_start=date_90_day) - if len(df) < AVR_LONG: - return Stock() - - cf = ConfigParser() - cf.read(config_file_path) - threshold_buy = cf.get('trade_threshold', 'Threshold_Buy_Count') - threshold_sale = cf.get('trade_threshold', 'Threshold_Sale_Count') - - maStrategy = MAStrategy(code=stock.code, trade= stock.current, df_close=df) - signal = maStrategy.select_Time_Mix(int(threshold_buy), int(threshold_sale)) - # if signal > 0: - # print '>' * 5, 'Buy now!', stock.name, stock.current, (float(stock.current)-float(stock.close))/float(stock.close)*100, '%' - - stock.signal = signal - return stock - - except Exception as e: - print encode_wrap(stock.name), str(e) - stock = Stock() - return stock - - - - -if __name__ == "__main__": - print ">>live trade begin" - main() - - sched = BlockingScheduler() - sched.add_job(main, 'cron', day_of_week='0-4', hour='9-12,13-15', minute='*/5') - - print ">>live trade end" +#!/usr/local/bin/python3 +#coding=utf-8 + +import sys +import platform +from apscheduler.schedulers.blocking import BlockingScheduler +import logging +import ctypes +from ConfigParser import ConfigParser + +import data_process.online_data as OnlineData +from trade_process.strategy.macd_live_test import MAStrategy +from util.stockutil import fn_timer as fn_timer_ +from data_process.data_get import * +from data_process.Stock import Stock +import util.stockutil as util +from util.codeConvert import GetNowTime +from util.send_mail import send_email_163 +from init import * +from strategy.stop_loss import stop_loss_by_price + +logging.basicConfig() +# import errorLogger +if platform.system() == 'Windows': + sys.path.append('../') +else: + sys.path.append('/Users/cbb/Documents/pythonspace/stock-master/') + +#stock_list =['600893'] +def main(): + + #获取实时股价 + try: + stockList = OnlineData.getAllChinaStock() + except Exception, e: + errorLogger.logger.error(encode_wrap('获取实时估价失败! ') + str(e)) + return + + #止盈止损判断 + judgements = stop_loss_by_price() + if judgements: + for judgement in judgements: + infoLogger.logger.info(encode_wrap('卖出:{}'.format(judgement[0]))) + + cf = ConfigParser() + cf.read(config_file_path) + threshold_buy = cf.get('trade_threshold', 'Threshold_Buy_Count') + threshold_sale = cf.get('trade_threshold', 'Threshold_Sale_Count') + infoLogger.logger.info(encode_wrap('阈值: Buy(%s) ,Sale(%s)' %(threshold_buy, threshold_sale))) + + # 监听股票列表 + stock_list = ['600000','600048', '600011', '002600', '002505', '000725', '000783', '300315', '002167', '601001',\ + '600893', '000020', '600111'] + + print '>>>>>Calcute ...' + stock_buy_list, stock_sale_list = live_mult_stock(stockList) + if len(stock_buy_list) == 0 and len([stock for stock in stock_sale_list if stock.code in stock_list]) == 0: + infoLogger.logger.info(encode_wrap('没有合适的买卖机会，请耐心等待')) + return + + str_all = '{}\n\n\n买入\n'.format(GetNowTime()) + + infoLogger.logger.info(encode_wrap('{}\n\n买入'.format(GetNowTime()))) + for stock in stock_buy_list: + infoLogger.logger.info('Buy now! ' + stock.str_print()) + str_all = str_all + stock.str_print() + '\n' + + #print encode_wrap('\n卖出:')f + print '\n' + infoLogger.logger.info(encode_wrap('卖出')) + str_all = '\n\n\n' + str_all + '卖出\n' + for stock in stock_sale_list: + # if stock.code in stock_list: + infoLogger.logger.info('Sale now! ' + stock.str_print()) + #print '>' * 3, 'Sale now!', encode_wrap(stock.name), stock.code, stock.current, (float(stock.current)-float(stock.close))/float(stock.close)*100, '%' + str_all = str_all + stock.str_print() + '\n' + + send_email_163(subject='MA Strategy Results', content=str_all) + +@fn_timer_ +def live_mult_stock(stockClassList): +# pool = ThreadPool(processes=4) +# pool.map(live_single_stock, stockClassList) +# pool.close() +# pool.join() + + stock_buy_list = [] + stock_sale_list = [] + for stock in stockClassList: + try: + live_single_stock(stock) + if stock.signal > 0: + stock_buy_list.append(stock) + elif stock.signal < 0: + stock_sale_list.append(stock) + except Exception, e: + errorLogger.logger.error((str(e))) + return stock_buy_list, stock_sale_list + +def live_single_stock(stock): + try: + # 多线程提醒实时买卖 + if float(stock.current) == 0.0: + #print '>>>', stock.name,'>>>停牌!', stock.close, stock.time + return 0, '' + + #取最近三个月的收盘价 + d_90_day = datetime.date.today() + datetime.timedelta(days=-90) + date_90_day = d_90_day.strftime("%Y-%m-%y") + df = get_stock_k_line(stock.code, date_start=date_90_day) + if len(df) < AVR_LONG: + return Stock() + + cf = ConfigParser() + cf.read(config_file_path) + threshold_buy = cf.get('trade_threshold', 'Threshold_Buy_Count') + threshold_sale = cf.get('trade_threshold', 'Threshold_Sale_Count') + + maStrategy = MAStrategy(code=stock.code, trade= stock.current, df_close=df) + signal = maStrategy.select_Time_Mix(int(threshold_buy), int(threshold_sale)) + # if signal > 0: + # print '>' * 5, 'Buy now!', stock.name, stock.current, (float(stock.current)-float(stock.close))/float(stock.close)*100, '%' + + stock.signal = signal + return stock + + except Exception as e: + print encode_wrap(stock.name), str(e) + stock = Stock() + return stock + +if __name__ == "__main__": + print ">>live trade begin" + main() + + sched = BlockingScheduler() + sched.add_job(main, 'cron', day_of_week='0-4', hour='9-12,13-15', minute='*/5') + + print ">>live trade end" diff --git a/trade_process/requirements.txt b/trade_process/requirements.txt new file mode 100644 index 0000000..c324d3f --- /dev/null +++ b/trade_process/requirements.txt @@ -0,0 +1,10 @@ +anyjson==0.3.3 +beautifulsoup4==4.6.0 +bs4==0.0.1 +request==0.0.13 +requests==2.14.2 +requests-file==1.4.2 +requests-ftp==0.3.13 +six==1.11.0 +win-unicode-console==0.5 +xlrd==0.9.3 diff --git a/trade_process/select_stock_center.py b/trade_process/select_stock_center.py index b019249..9f6ff48 100644 --- a/trade_process/select_stock_center.py +++ b/trade_process/select_stock_center.py @@ -1,75 +1,75 @@ -#!/usr/local/bin/python3 -#coding=utf-8 -#量化选股中心 - -from multiprocessing.dummy import Pool as ThreadPool - -from data_process.data_get import * -import trade_process.strategy.macd_live_test as MACD_LIVE_TEST -from util.stockutil import getSixDigitalStockCode - -stock_buy_list = [] -stock_sale_list = [] - -def select_stock_from_all_stocks(): - print '>>>开始筛选所有A股' - try: - df = pd.read_csv(cm.DownloadDir + cm.TABLE_STOCKS_BASIC + '.csv') - code_all_list = df['code'].get_values(); - #过滤停牌的股票 - code_list = code_all_list - - pool = ThreadPool(processes=20) - pool.map(evaluation_stock, code_list) - pool.close() - pool.join() - - except Exception as e: - print str(e) - - print '>>>结束筛选所有A股' - - -def evaluation_stock(code): - # 以上一交易日的收盘价为判断点 - - try: - #print 'evaluation', getSixDigitalStockCode(code), 'begin' - date_end = datetime.date.today() + datetime.timedelta(-1) - df = get_stock_k_line(code) - if df is None: - return - else: - nearest_date = df.head(1)['date'] - #print nearest_date - if str(nearest_date.values[0]) != str(date_end): - return - if len(df.index) > 30: - - maStrategy = MACD_LIVE_TEST.MAStrategy(df=df) - signal = maStrategy.select_Time_Mix() - if signal > 0: - #print stock.name, stock.current, (float(stock.current)-float(stock.close))/float(stock.close)*100, '%' - print '>' * 5, 'Buy now!', getSixDigitalStockCode(code) - stock_buy_list.append(getSixDigitalStockCode(code)) - elif signal < 0: - #print stock.name, stock.current, (float(stock.current)-float(stock.close))/float(stock.close)*100, '%' - print '>' * 5, 'Sale now!', getSixDigitalStockCode(code) - stock_sale_list.append(getSixDigitalStockCode(code)) - except Exception as e: - print str(e) - -if __name__ == "__main__": - select_stock_from_all_stocks() - #evaluation_stock('000001') - - - print '买入:' - for code in stock_buy_list: - stockInfo = get_stock_info(code) - print 'Buy:', code, stockInfo['name'] - - print '\n卖出:' - for code in stock_sale_list: - stockInfo = get_stock_info(code) +#!/usr/local/bin/python3 +#coding=utf-8 +#量化选股中心 + +from multiprocessing.dummy import Pool as ThreadPool + +from data_process.data_get import * +import trade_process.strategy.macd_live_test as MACD_LIVE_TEST +from util.stockutil import getSixDigitalStockCode + +stock_buy_list = [] +stock_sale_list = [] + +def select_stock_from_all_stocks(): + print '>>>开始筛选所有A股' + try: + df = pd.read_csv(cm.DownloadDir + cm.TABLE_STOCKS_BASIC + '.csv') + code_all_list = df['code'].get_values(); + #过滤停牌的股票 + code_list = code_all_list + + pool = ThreadPool(processes=20) + pool.map(evaluation_stock, code_list) + pool.close() + pool.join() + + except Exception as e: + print str(e) + + print '>>>结束筛选所有A股' + + +def evaluation_stock(code): + # 以上一交易日的收盘价为判断点 + + try: + #print 'evaluation', getSixDigitalStockCode(code), 'begin' + date_end = datetime.date.today() + datetime.timedelta(-1) + df = get_stock_k_line(code) + if df is None: + return + else: + nearest_date = df.head(1)['date'] + #print nearest_date + if str(nearest_date.values[0]) != str(date_end): + return + if len(df.index) > 30: + + maStrategy = MACD_LIVE_TEST.MAStrategy(df=df) + signal = maStrategy.select_Time_Mix() + if signal > 0: + #print stock.name, stock.current, (float(stock.current)-float(stock.close))/float(stock.close)*100, '%' + print '>' * 5, 'Buy now!', getSixDigitalStockCode(code) + stock_buy_list.append(getSixDigitalStockCode(code)) + elif signal < 0: + #print stock.name, stock.current, (float(stock.current)-float(stock.close))/float(stock.close)*100, '%' + print '>' * 5, 'Sale now!', getSixDigitalStockCode(code) + stock_sale_list.append(getSixDigitalStockCode(code)) + except Exception as e: + print str(e) + +if __name__ == "__main__": + select_stock_from_all_stocks() + #evaluation_stock('000001') + + + print '买入:' + for code in stock_buy_list: + stockInfo = get_stock_info(code) + print 'Buy:', code, stockInfo['name'] + + print '\n卖出:' + for code in stock_sale_list: + stockInfo = get_stock_info(code) print 'Sale:', code, stockInfo['name'] \ No newline at end of file diff --git a/trade_process/stockdata/HS300.jpg b/trade_process/stockdata/HS300.jpg new file mode 100644 index 0000000..2e1fea7 Binary files /dev/null and b/trade_process/stockdata/HS300.jpg differ diff --git a/trade_process/stockdata/SPX500.jpg b/trade_process/stockdata/SPX500.jpg new file mode 100644 index 0000000..cdd3260 Binary files /dev/null and b/trade_process/stockdata/SPX500.jpg differ diff --git a/trade_process/stockdata/anxinB.jpg b/trade_process/stockdata/anxinB.jpg new file mode 100644 index 0000000..2e1bc59 Binary files /dev/null and b/trade_process/stockdata/anxinB.jpg differ diff --git a/trade_process/stockdata/eConsumption .jpg b/trade_process/stockdata/eConsumption .jpg new file mode 100644 index 0000000..66f089b Binary files /dev/null and b/trade_process/stockdata/eConsumption .jpg differ diff --git a/trade_process/stockdata/eGEI.jpg b/trade_process/stockdata/eGEI.jpg new file mode 100644 index 0000000..d87879a Binary files /dev/null and b/trade_process/stockdata/eGEI.jpg differ diff --git a/trade_process/stockdata/eHSI.jpg b/trade_process/stockdata/eHSI.jpg new file mode 100644 index 0000000..2c97e56 Binary files /dev/null and b/trade_process/stockdata/eHSI.jpg differ diff --git a/trade_process/stockdata/eNASDAQ100.jpg b/trade_process/stockdata/eNASDAQ100.jpg new file mode 100644 index 0000000..b4f667f Binary files /dev/null and b/trade_process/stockdata/eNASDAQ100.jpg differ diff --git a/trade_process/stockdata/eSSE50.jpg b/trade_process/stockdata/eSSE50.jpg new file mode 100644 index 0000000..63ec60d Binary files /dev/null and b/trade_process/stockdata/eSSE50.jpg differ diff --git a/trade_process/stockdata/egold.jpg b/trade_process/stockdata/egold.jpg new file mode 100644 index 0000000..f5ca692 Binary files /dev/null and b/trade_process/stockdata/egold.jpg differ diff --git a/trade_process/stockdata/eoil.jpg b/trade_process/stockdata/eoil.jpg new file mode 100644 index 0000000..3208ec3 Binary files /dev/null and b/trade_process/stockdata/eoil.jpg differ diff --git a/trade_process/strategy/BooLbands.py b/trade_process/strategy/BooLbands.py new file mode 100644 index 0000000..15d237b --- /dev/null +++ b/trade_process/strategy/BooLbands.py @@ -0,0 +1,203 @@ +# coding=utf-8 +from __future__ import division + +import os +import pandas as pd +import warnings + +warnings.filterwarnings("ignore") + + +# 计算复权价格 +def cal_right_price(input_stock_data, type='前复权'): + """ + :param input_stock_data: 标准股票数据，需要'收盘价', '涨跌幅' + :param type: 确定是前复权还是后复权，分别为'后复权'，'前复权' + :return: 新增一列'后复权价'/'前复权价'的stock_data + """ + # 计算收盘复权价 + stock_data = input_stock_data.copy() + num = {'后复权': 0, '前复权': -1} + price1 = stock_data['close'].iloc[num[type]] + stock_data['复权价_temp'] = (stock_data['change'] + 1.0).cumprod() + price2 = stock_data['复权价_temp'].iloc[num[type]] + stock_data['复权价'] = stock_data['复权价_temp'] * (price1 / price2) + stock_data.pop('复权价_temp') + + # 计算开盘复权价 + stock_data['复权价_开盘'] = stock_data['复权价'] / (stock_data['close'] / stock_data['open']) + stock_data['复权价_最高'] = stock_data['复权价'] / (stock_data['close'] / stock_data['high']) + stock_data['复权价_最低'] = stock_data['复权价'] / (stock_data['close'] / stock_data['low']) + + return stock_data[['复权价_开盘', '复权价', '复权价_最高', '复权价_最低']] + + +# 获取指定股票对应的数据并按日期升序排序 +def get_stock_data(stock_code): + """ + :param stock_code: 股票代码 + :return: 返回股票数据集（代码，日期，开盘价，收盘价，涨跌幅） + """ + # 此处为存放csv文件的本地路径，请自行改正地址 + stock_data = pd.read_csv('e:/data/stock data/' + str(stock_code) + '.csv', parse_dates=['date'], index_col='date') + stock_data = stock_data[['code', 'open', 'close', 'high', 'low', 'change']] + stock_data.sort_index(inplace=True) + + # 计算复权价 + stock_data[['open', 'close', 'high', 'low']] = cal_right_price(stock_data, type='后复权') + + stock_data = stock_data['2005-01-01':] + + return stock_data + + +# 计算布林带指标并得到信号和仓位 +def bands(stock_data, n=14): + + df = stock_data.copy() + + # 计算布林带的中轨线、上轨线和下轨线 + df['tp'] = (df['high'] + df['low'] + df['close']) / 3 + + df['middle'] = pd.rolling_mean(df['tp'], n) + df['sd'] = pd.rolling_std(df['tp'], n) + + df['up'] = df['middle'] + 2 * df['sd'] + df['down'] = df['middle'] - 2 * df['sd'] + + df.dropna(inplace=True) + + # 当收盘价上穿上轨线，买入，信号为1 + df.ix[df['close'] > df['up'], 'signal'] = 1 + # 当收盘价下穿下轨线，卖空，信号为-1 + df.ix[df['close'] < df['down'], 'signal'] = -1 + + df['signal'].fillna(method='ffill', inplace=True) + + # =====计算每天的仓位 + df.ix[0, 'position'] = 0 + # 出现买入信号而且第二天开盘没有涨停 + df.ix[(df['signal'].shift(1) == 1) & (df['open'] < df['close'].shift(1) * 1.097), 'position'] = 1 + # 出现卖出信号而且第二天开盘没有跌停 + df.ix[(df['signal'].shift(1) == -1) & (df['open'] > df['close'].shift(1) * 0.903), 'position'] = 0 + + df['position'].fillna(method='ffill', inplace=True) + + return df + + +# 根据每日仓位计算总资产的日收益率 +def account(df, slippage=1.0 / 1000, commision_rate=2.0 / 1000): + """ + :param df: 股票账户数据集 + :param slippage: 买卖滑点 默认为1.0 / 1000 + :param commision_rate: 手续费 默认为2.0 / 1000 + :return: 返回账户资产的日收益率和日累计收益率的数据集 + """ + df.ix[0, 'capital_rtn'] = 0 + # 当加仓时,计算当天资金曲线涨幅capital_rtn.capital_rtn = 昨天的position在今天涨幅 + 今天开盘新买入的position在今天的涨幅(扣除手续费) + df.ix[df['position'] > df['position'].shift(1), 'capital_rtn'] = (df['close'] / df['open'] - 1) * ( + 1 - slippage - commision_rate) * (df['position'] - df['position'].shift(1)) + df['change'] * df[ + 'position'].shift(1) + # 当减仓时,计算当天资金曲线涨幅capital_rtn.capital_rtn = 今天开盘卖出的positipn在今天的涨幅(扣除手续费) + 还剩的position在今天的涨幅 + df.ix[df['position'] < df['position'].shift(1), 'capital_rtn'] = (df['open'] / df['close'].shift(1) - 1) * ( + 1 - slippage - commision_rate) * (df['position'].shift(1) - df['position']) + df['change'] * df['position'] + # 当仓位不变时,当天的capital_rtn是当天的change * position + df.ix[df['position'] == df['position'].shift(1), 'capital_rtn'] = df['change'] * df['position'] + + return df + + +# 计算年化收益率函数 +def annual_return(date_line, capital_line): + """ + :param date_line: 日期序列 + :param capital_line: 账户价值序列 + :return: 输出在回测期间的年化收益率 + """ + # 将数据序列合并成dataframe并按日期排序 + df = pd.DataFrame({'date': date_line, 'capital': capital_line}) + + # 计算年化收益率 + annual = (df['capital'].iloc[-1] / df['capital'].iloc[0]) ** (250 / len(df)) - 1 + + return annual + + +# 计算最大回撤函数 +def max_drawdown(date_line, capital_line): + """ + :param date_line: 日期序列 + :param capital_line: 账户价值序列 + :return: 输出最大回撤及开始日期和结束日期 + """ + # 将数据序列合并为一个dataframe并按日期排序 + df = pd.DataFrame({'date': date_line, 'capital': capital_line}) + + df['max2here'] = pd.expanding_max(df['capital']) # 计算当日之前的账户最大价值 + df['dd2here'] = df['capital'] / df['max2here'] - 1 # 计算当日的回撤 + # 计算最大回撤和结束时间 + temp = df.sort_values(by='dd2here').iloc[0][['date', 'dd2here']] + max_dd = temp['dd2here'] + + return max_dd + + +# 遍历数据文件夹中所有股票文件的文件名，得到股票代码列表 +stock_code_list = [] +# 此处为股票数据文件的本地路径，请自行修改 +for root, dirs, files in os.walk('e:/data/stock data'): + if files: + for f in files: + if '.csv' in f: + stock_code_list.append(f.split('.csv')[0]) + + +for code in stock_code_list[2000:]: + + stock_data = get_stock_data(code) + + # 剔除上市不到1年半的股票 + if len(stock_data) < 360: + continue + + re = pd.DataFrame(columns=['code', 'start', 'param', 'stock_rtn', 'stock_md', 'strategy_rtn', + 'strategy_md', 'excessive_rtn']) + i = 0 + + for p in range(10, 31, 2): + + df = bands(stock_data, n=p) + # 计算策略每天涨幅 + df = account(df, slippage=0, commision_rate=0) + # 计算资金曲线 + df['capital'] = (df['capital_rtn'] + 1).cumprod() + + # =====根据资金曲线,计算相关评价指标 + df = df['2006-01-01':] + date_line = list(df.index) + capital_line = list(df['capital']) + stock_line = list(df['close']) + # 股票的年化收益 + stock_rtn = annual_return(date_line, stock_line) + # 策略的年化收益 + strategy_rtn = annual_return(date_line, capital_line) + # 股票最大回撤 + stock_md = max_drawdown(date_line, stock_line) + # 策略最大回撤 + strategy_md = max_drawdown(date_line, capital_line) + + re.loc[i, 'code'] = df['code'].iloc[0] + re.loc[i, 'start'] = df.index[0].strftime('%Y-%m-%d') + re.loc[i, 'param'] = p + re.loc[i, 'stock_rtn'] = stock_rtn + re.loc[i, 'stock_md'] = stock_md + re.loc[i, 'strategy_rtn'] = strategy_rtn + re.loc[i, 'strategy_md'] = strategy_md + re.loc[i, 'excessive_rtn'] = strategy_rtn - stock_rtn + + i += 1 + + re.sort_values(by='excessive_rtn', ascending=False, inplace=True) + + re.iloc[0:1, :].to_csv('d:/results5.csv', mode='a', header=None, index=False) diff --git a/trade_process/strategy/__init__.py b/trade_process/strategy/__init__.py index 65d9a01..4c80e14 100644 --- a/trade_process/strategy/__init__.py +++ b/trade_process/strategy/__init__.py @@ -1,4 +1,4 @@ -from trade_process.strategy.macd_back_test import * -from trade_process.strategy.macd_live_test import * -from trade_process.strategy.strategy_macd import * - +# from trade_process.strategy.macd_back_test import * +# from trade_process.strategy.macd_live_test import * +# from trade_process.strategy.strategy_macd import * + diff --git a/trade_process/strategy/__init__.pyc b/trade_process/strategy/__init__.pyc new file mode 100644 index 0000000..a0e2343 Binary files /dev/null and b/trade_process/strategy/__init__.pyc differ diff --git a/trade_process/strategy/macd_back_test.py b/trade_process/strategy/macd_back_test.py index 2be2045..2d707b8 100644 --- a/trade_process/strategy/macd_back_test.py +++ b/trade_process/strategy/macd_back_test.py @@ -1,609 +1,864 @@ -#!/usr/local/bin/python -#coding=utf-8 - -#回测均线策略 - -import pandas as pd -import matplotlib.pyplot as plt -import os -import numpy as np - -AVR_SHORT = 12 -AVR_LONG = 40 - -SIGNAL_BUY = 1 #买 -SIGNAL_SALE = -1 #卖 -SIGNAL_DEFAULT = 0 - - -# 导入csv股票数据，写5日、20日、60日移动平均线 -def processEMA(stockCsvPath, stockCsvNewPath): - #导入数据，stockCsvPath为在电脑中的路径 - stock_data = pd.read_csv(stockCsvPath) - - # 将数据按照交易日期从远到近排序 - stock_data.sort('Date', inplace=True) - - #=====================计算移动平均线 - - # 分别计算5日、20日、60日移动平均线 - ma_list = [5, 20, 60] - - # 计算简单算术移动平均线MA - 注意：stock_data['close']为股票每条的收盘价 - for ma in ma_list: - stock_data['MA_' + str(ma)] = pd.rolling_mean(stock_data['Adj Close'], ma) - - # 计算指数平滑移动平均线EMA - for ma in ma_list: - stock_data['EMA_' + str(ma)] = pd.ewma(stock_data['Adj Close'], span=ma) - - # 将数据按照交易日期从近到远排序 - stock_data.sort('Date', ascending=False, inplace=True) - - stock_data['DIF'] = stock_data['EMA_'+str(ma_list[0])] - stock_data['EMA_'+str(ma_list[-1])] - stock_data['DEA_' + str(10)] = pd.ewma(stock_data['DIF'], span=10) - - # =================================== 将算好的数据输出到csv文件，这里请填写输出文件在您电脑的路径 - stock_data.to_csv(stockCsvNewPath, index=False) - -# 自适应均线 -def self_adaptive_ma(stock_data): - # 将数据按照交易日期从远到近排序 - stock_data.sort('Date', inplace=True) - - close_price = stock_data['Adj Close'].get_values() - high_price = stock_data['High'].get_values() - low_price = stock_data['Low'].get_values() - longDay = 100 - - print len(close_price) - if len(close_price) < 100: - return - - print close_price[-10:-1] - direction = abs(close_price[-1] - close_price[-10]) - volatility = sum(abs(close_price[i+1]-close_price[i]) for i in range(-9,0)) - ER = abs(direction/volatility) - fastSC = 2.0/(2.0+1) - slowSC = 2.0/(30.0+1) - sSC = ER * (fastSC-slowSC) + slowSC - constaint = sSC*sSC - - #EMA 100 - ema_close_100 = pd.ewma(close_price, span=longDay) - ema_high_100 = pd.ewma(high_price, span=longDay) - ema_low_100 = pd.ewma(low_price, span=longDay) - - amaClose = ema_close_100[-1] + constaint * (close_price[-1] - ema_close_100[-1]) - amaHigh = ema_high_100[-1] + constaint * (high_price[-1] - ema_high_100[-1]) - amaLow = ema_low_100[-1] + constaint * (low_price[-1] - ema_low_100[-1]) - print ema_close_100[-1], ema_high_100[-1], ema_low_100[-1] - - BKPRICE = 0.0 - SKPRICE = float('Inf') - status = SIGNAL_DEFAULT - print high_price[-1], low_price[-1], close_price[-1] - if low_price[-1] > amaHigh: - status = SIGNAL_BUY - elif close_price[-1] < amaClose or close_price[-1] <= 0.995 * BKPRICE: - status = SIGNAL_BUY - elif high_price[-1] < amaLow: - status = SIGNAL_SALE - elif close_price[-1] > amaClose or close_price[-1] >= 1.005 * SKPRICE: - status = SIGNAL_SALE - - return status - - -# MA指标择时 (回测） -def select_Time_MA(stock_data, stockName): - - start_money = 100000000 - now_count = 0 - now_money = start_money - - # 将数据按照交易日期从远到近排序 - stock_data.sort('Date', inplace=True) - - close_price = stock_data['Adj Close'].get_values() - - #EMA - ma_list = [AVR_SHORT, AVR_LONG] - ema_close_short = pd.ewma(close_price, span=ma_list[0]) - ema_close_long = pd.ewma(close_price, span=ma_list[1]) - - signals = [0]*(ma_list[1]+1) - tradeTimes = 0 - bBuySignal = True - for t in range(ma_list[1]+1, len(close_price)): - - signal = SIGNAL_DEFAULT - - if ema_close_short[t] > ema_close_short[t-1] and ema_close_short[t] > ema_close_long[t] \ - and ema_close_short[t-1] < ema_close_long[t-1]: - if bBuySignal: - signal = SIGNAL_BUY - now_count = (int)(start_money / close_price[t] /100)*100 - now_money = start_money - now_count * close_price[t] - #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money - bBuySignal = False - elif ema_close_long[t] < ema_close_long[t-1] and ema_close_short[t] < ema_close_long[t] \ - and ema_close_short[t-1] > ema_close_long[t-1]: - if bBuySignal == False: - signal = SIGNAL_SALE - now_money += now_count * close_price[t] - now_count = 0 - #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money - bBuySignal = True - signals.append(signal) - if signal != 0: - #print 't:', t, ' signal:', signal - tradeTimes += 1 - - - print stockName, u"收益率：", (now_money+now_count * close_price[-1]-start_money)/start_money*100, '%\t' \ - u"交易次数", tradeTimes, u" 最新市值：", now_money+now_count * close_price[-1] - stock_data['SIGNAL_MA'] = signals - -# fig = plt.figure(facecolor='white') -# left, width = 0.1, 0.8 -# rect1 = [left, 0.5, width, 0.4] -# rect2 = [left, 0.1, width, 0.3] -# -# axescolor = '#f6f6f6' # the axes background color -# ax1 = fig.add_axes(rect1, axisbg=axescolor) #left, bottom, width, height -# ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1) -# -# ax1.plot(range(len(close_price)), close_price, color="black", linewidth=1) -# ax1.grid() -# ax2.plot(range(len(ema_close_long)), ema_close_long,label='', color="red", linewidth=1) -# ax2.plot(range(len(ema_close_short)), ema_close_short,label='', color="blue", linewidth=1) -# ax2.grid() - #plt.show() - - # 将数据按照交易日期从近到远排序 - #stock_data.sort('date', ascending=False, inplace=True) - -# MACD指标择时 (回测） -def select_Time_MACD(stock_data, stockName): - - start_money = 100000000 - now_count = 0 - now_money = start_money - - # 将数据按照交易日期从远到近排序 - stock_data.sort('Date', inplace=True) - - close_price = stock_data['Adj Close'].get_values() - - #EMA - ma_list = [AVR_SHORT, AVR_LONG] - ma_dea = 10 - ema_close_short = pd.ewma(close_price, span=ma_list[0]) - ema_close_long = pd.ewma(close_price, span=ma_list[1]) - - - dif_price = ema_close_short - ema_close_long - dea_price = pd.ewma(dif_price, span=ma_dea) - macd_price = 2 * (dif_price - dea_price) - - signals = [0]*(ma_list[1]+1) - tradeTimes = 0 - bBuySignal = True - for t in range(ma_list[1]+1, len(close_price)): - - signal = SIGNAL_DEFAULT - - if dif_price[t] > dif_price[t-1] and dif_price[t] > dea_price[t] \ - and dif_price[t-1] < dea_price[t-1] and dea_price[t] > 0: - if bBuySignal: - signal = SIGNAL_BUY - now_count = (int)(now_money / close_price[t] /100)*100 - now_money = start_money - now_count * close_price[t] - #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money - bBuySignal = False - elif dif_price[t] < dif_price[t-1] and dif_price[t] < dea_price[t] \ - and dif_price[t-1] > dea_price[t-1] and dif_price[t] < 0: - if bBuySignal == False: - signal = SIGNAL_SALE - now_money += now_count * close_price[t] - now_count = 0 - #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money - bBuySignal = True - signals.append(signal) - if signal != 0: - #print 't:', t, ' signal:', signal - tradeTimes += 1 - - - - print stockName, u"收益率：", (now_money+now_count * close_price[-1]-start_money)/start_money*100, '%\t' \ - u"交易次数", tradeTimes, u" 最新市值：", now_money+now_count * close_price[-1] - stock_data['SIGNAL_MACD'] = signals - -# fig = plt.figure(facecolor='white') -# left, width = 0.1, 0.8 -# rect1 = [left, 0.5, width, 0.4] -# rect2 = [left, 0.1, width, 0.3] -# -# axescolor = '#f6f6f6' # the axes background color -# ax1 = fig.add_axes(rect1, axisbg=axescolor) #left, bottom, width, height -# ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1) -# -# ax1.plot(range(len(close_price)), close_price, color="black", linewidth=1) -# ax1.grid() -# ax2.plot(range(len(dif_price)), ema_close_long,label='', color="red", linewidth=1) -# ax2.plot(range(len(dea_price)), ema_close_short,label='', color="blue", linewidth=1) -# ax2.grid() - #plt.show() - - # 将数据按照交易日期从近到远排序 - #stock_data.sort('date', ascending=False, inplace=True) - - return dif_price, dea_price, macd_price - -# DMA指标择时 (回测） -def select_Time_DMA(stock_data, stockName): - - start_money = 100000000 - now_count = 0 - now_money = start_money - - # 将数据按照交易日期从远到近排序 - stock_data.sort('Date', inplace=True) - - close_price = stock_data['Adj Close'].get_values() - - #MA - ma_list = [AVR_SHORT, AVR_LONG] - ma_dea = 10 - ma_close_short = pd.rolling_mean(close_price, ma_list[0]) - ma_close_long = pd.rolling_mean(close_price, ma_list[1]) - - - dma_price = ma_close_short - ma_close_long - ama_price = pd.rolling_mean(dma_price, ma_dea) - - signals = [0]*(ma_list[1]+1) - tradeTimes = 0 - bBuySignal = True - for t in range(ma_list[1]+1, len(close_price)): - - signal = SIGNAL_DEFAULT - - if dma_price[t] > dma_price[t-1] and dma_price[t] > ama_price[t] \ - and dma_price[t-1] < ama_price[t-1]: - if bBuySignal: - signal = SIGNAL_BUY - now_count = (int)(now_money / close_price[t] /100)*100 - now_money = start_money - now_count * close_price[t] - #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money - bBuySignal = False - elif dma_price[t] < dma_price[t-1] and dma_price[t] < ama_price[t] \ - and dma_price[t-1] > ama_price[t-1]: - if bBuySignal == False: - signal = SIGNAL_SALE - now_money += now_count * close_price[t] - now_count = 0 - #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money - bBuySignal = True - signals.append(signal) - if signal != 0: - #print 't:', t, ' signal:', signal - tradeTimes += 1 - - - - print stockName, u"收益率：", (now_money+now_count * close_price[-1]-start_money)/start_money*100, '%\t' \ - u"交易次数", tradeTimes, u" 最新市值：", now_money+now_count * close_price[-1] - stock_data['SIGNAL_DMA'] = signals - -# fig = plt.figure(facecolor='white') -# left, width = 0.1, 0.8 -# rect1 = [left, 0.5, width, 0.4] -# rect2 = [left, 0.1, width, 0.3] -# -# axescolor = '#f6f6f6' # the axes background color -# ax1 = fig.add_axes(rect1, axisbg=axescolor) #left, bottom, width, height -# ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1) -# -# ax1.plot(range(len(close_price)), close_price, color="black", linewidth=1) -# ax1.grid() -# ax2.plot(range(len(dma_price)), dma_price,label='', color="red", linewidth=1) -# ax2.plot(range(len(ama_price)), ama_price,label='', color="blue", linewidth=1) -# ax2.grid() - #plt.show() - - # 将数据按照交易日期从近到远排序 - #stock_data.sort('date', ascending=False, inplace=True) - -# DMA指标择时 (回测） -def select_Time_TRIX(stock_data, stockName): - - start_money = 100000000 - now_count = 0 - now_money = start_money - - # 将数据按照交易日期从远到近排序 - stock_data.sort('Date', inplace=True) - - close_price = stock_data['Adj Close'].get_values() - - #EMA - ma_list = [AVR_SHORT, AVR_SHORT] #N,M - - ema_close = pd.ewma(close_price, span=ma_list[0]) - ema_close = pd.ewma(ema_close, span=ma_list[0]) - tr_close = pd.ewma(ema_close, span=ma_list[0]) - - trixsList = [0] - for i in range(1, len(tr_close)): - #print tr_close[i], tr_close[i-1] - trix = (tr_close[i]-tr_close[i-1])/tr_close[i-1]*100 - trixsList.append(trix) - trixs = np.array(trixsList) - maxtrix = pd.rolling_mean(trixs, ma_list[1]) - - - signals = [0]*(ma_list[1]+1) - tradeTimes = 0 - bBuySignal = True - for t in range(ma_list[1]+1, len(close_price)): - - signal = SIGNAL_DEFAULT - - if trixs[t] > trixs[t-1] and trixs[t] > maxtrix[t] \ - and trixs[t-1] < maxtrix[t-1]: - if bBuySignal: - signal = SIGNAL_BUY - now_count = (int)(now_money / close_price[t] /100)*100 - now_money = start_money - now_count * close_price[t] - #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money - bBuySignal = False - elif trixs[t] < trixs[t-1] and trixs[t] < maxtrix[t] \ - and trixs[t-1] > maxtrix[t-1]: - if bBuySignal == False: - signal = SIGNAL_SALE - now_money += now_count * close_price[t] - now_count = 0 - #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money - bBuySignal = True - signals.append(signal) - if signal != 0: - #print 't:', t, ' signal:', signal - tradeTimes += 1 - - - - print stockName, u"收益率：", (now_money+now_count * close_price[-1]-start_money)/start_money*100, '%\t' \ - u"交易次数", tradeTimes, u" 最新市值：", now_money+now_count * close_price[-1] - stock_data['SIGNAL_TRIX'] = signals - - -# 组合择时指标 (回测） -def select_Time_Mix(stock_data, stockName): - - start_money = 100000000 - now_count = 0 - now_money = start_money - - # 综合策略 - signals = [] - tradeTimes = 0 - bBuySignal = True - - close_price = stock_data['Adj Close'].get_values() - s_ma = stock_data['SIGNAL_MA'].get_values() - s_macd = stock_data['SIGNAL_MACD'].get_values() - s_dma = stock_data['SIGNAL_DMA'].get_values() - s_trix = stock_data['SIGNAL_TRIX'].get_values() - - for i in range(len(s_ma)): - - signal = SIGNAL_DEFAULT - - up = 0; - down = 0; - if s_ma[i] == 1: - up += 1 - elif s_ma[i] == -1: - down += 1 - - if s_macd[i] == 1: - up += 1 - elif s_macd[i] == -1: - down += 1 - - if s_dma[i] == 1: - up += 1 - elif s_dma[i] == -1: - down += 1 - - if s_trix[i] == 1: - up += 1 - elif s_trix[i] == -1: - down += 1 - - - if up >= 3: - if bBuySignal: - signal = SIGNAL_BUY - now_count = (int)(now_money / close_price[i] /100)*100 - now_money = start_money - now_count * close_price[i] - #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money - bBuySignal = False - elif down <= -3: - if bBuySignal == False: - signal = SIGNAL_SALE - now_money += now_count * close_price[i] - now_count = 0 - #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money - bBuySignal = True - - signals.append(signal) - if signal != 0: - #print 't:', t, ' signal:', signal - tradeTimes += 1 - - print stockName, u"收益率：", (now_money+now_count * close_price[-1]-start_money)/start_money*100, '%\t' \ - u"交易次数", tradeTimes, u" 最新市值：", now_money+now_count * close_price[-1] - stock_data['SIGNAL_MIX'] = signals - -# AMA指标择时 -def select_Time_AMA(stock_data, stockName): - - percentage = 0.1 - - start_money = 100000000 - now_count = 0 - now_money = 0 - one_hand = 10000 - - # 将数据按照交易日期从远到近排序 - stock_data.sort('Date', inplace=True) - - close_price = stock_data['Adj Close'].get_values() - - # 指数平滑序列 - containts = [0]*10 - for i in range(10, len(close_price)): - sub_price = close_price[i-10:i] - constaint = getConstaint(sub_price) - containts.append(constaint); - - ama_price = [close_price[0]] - for i in range(1, len(close_price)): - ama = containts[i-1] * close_price[i-1] + (1-containts[i-1])*ama_price[i-1] - ama_price.append(ama) - - signals = [0]*21 - tradeTimes = 0 - - record_buy = 0 - record_sale = [] - - # 从20天以后开始判断买卖点 - for i in range(21, len(ama_price)): - - signal = SIGNAL_DEFAULT - - #print np.array(ama_price[i-19:i+1]) - np.array(ama_price[i-20:i]) - threshold = percentage * np.std(np.array(ama_price[i-19:i+1]) - np.array(ama_price[i-20:i])) # 过滤器 - - if ama_price[i] - np.min(ama_price[i-5:i]) > threshold: - signal = SIGNAL_BUY - now_count += one_hand - record_buy += one_hand * close_price[i] - #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money - elif np.max(ama_price[i-5:i]) - ama_price[i] > threshold: - signal = SIGNAL_SALE - if now_count > one_hand: - now_count -= one_hand - record_sale = one_hand * close_price[i] - #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money - signals.append(signal) - if signal != 0: - #print 't:', t, ' signal:', signal - tradeTimes += 1 - - # 成本 - print u'盈利', record_sale + now_count * close_price[-1] - record_buy - -# print stockName, u"收益率：", (now_money+now_count * close_price[-1]-start_money)/start_money*100, '%\t' \ -# u"交易次数", tradeTimes, u" 最新市值：", now_money+now_count * close_price[-1] - stock_data['SIGNAL_AMA'] = signals - - return ama_price - -# 获取平方平滑系数 -def getConstaint(prices): - direction = abs(prices[-1] - prices[0]) - volatility = sum(abs(prices[i+1]-prices[i]) for i in range(len(prices)-1)) - ER = abs(direction/volatility) - fastSC = 2.0/(2.0+1) - slowSC = 2.0/(30.0+1) - sSC = ER * (fastSC-slowSC) + slowSC - constaint = sSC*sSC - return constaint - -# 选择一种均线策略 -def getMAStrategy(stockCsvPath, stockName, strategyName='MACD'): - if os.path.exists(stockCsvPath) == False: - return - stock_data = pd.read_csv(stockCsvPath) - if strategyName == 'MACD': - return select_Time_MACD(stock_data, stockName) - elif strategyName == 'MA': - return select_Time_MA(stock_data, stockName) - elif strategyName == 'DMA': - return select_Time_DMA(stock_data, stockName) - elif strategyName == 'TRIX': - return select_Time_TRIX(stock_data, stockName) - elif strategyName == 'AMA': - return select_Time_AMA(stock_data, stockName) - -# 执行策略 -def run(stockCsvPath, stockName): - if os.path.exists(stockCsvPath) == False: - return - #stockCsvNewPath = stockName + '_macd.csv' - #processEMA(stockCsvPath, stockCsvNewPath) - stock_data = pd.read_csv(stockCsvPath) - #self_adaptive_ma(stock_data) - - print u'>>>>>>>>>>>>> MA 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' - select_Time_MA(stock_data, stockName) - - print u'>>>>>>>>>>>>> MACD 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' - select_Time_MACD(stock_data, stockName) - - print u'>>>>>>>>>>>>> DMA 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' - select_Time_DMA(stock_data, stockName) - - print u'>>>>>>>>>>>>> TRIX 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' - select_Time_TRIX(stock_data, stockName) - - print u'>>>>>>>>>>>>> 组合策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' - select_Time_Mix(stock_data, stockName) - - print u'>>>>>>>>>>>>> AMA策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' - select_Time_AMA(stock_data, stockName) - - print '\n' - -if __name__ == "__main__": - print "main begin" - stockList=['000725','000783','002167','002505','002600','300315','600000','600011','600048','601001'] - #stockList = ['000725'] - for stockName in stockList: -# if stockName != '002600': -# continue - stockCsvPath = os.path.pardir +"\\stockdata\\" + stockName + '.csv' - #stockCsvPath = stockName + '.csv' - if os.path.exists(stockCsvPath) == False: - continue - #stockCsvNewPath = stockName + '_macd.csv' - #processEMA(stockCsvPath, stockCsvNewPath) - stock_data = pd.read_csv(stockCsvPath) - #self_adaptive_ma(stock_data) - - print u'>>>>>>>>>>>>> MA 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' - select_Time_MA(stock_data, stockName) - - print u'>>>>>>>>>>>>> MACD 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' - select_Time_MACD(stock_data, stockName) - - print u'>>>>>>>>>>>>> DMA 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' - select_Time_DMA(stock_data, stockName) - - print u'>>>>>>>>>>>>> TRIX 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' - select_Time_TRIX(stock_data, stockName) - - print u'>>>>>>>>>>>>> 组合策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' - select_Time_Mix(stock_data, stockName) - - print u'>>>>>>>>>>>>> AMA策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' - select_Time_AMA(stock_data, stockName) - - print '\n' - print "main end" \ No newline at end of file +#!/usr/local/bin/python +# -*- coding: UTF-8 -*- + +#回测均线策略 + +import pandas as pd +import matplotlib.pyplot as plt +import os,sys +import numpy as np +from data_process.download_stock import downloadAllHistoryAShareData +# from trade_process import efund_mail2 +from pyevolve import G1DList, GSimpleGA, Selectors, Scaling +from pyevolve import Mutators, Initializators,GAllele +from data_process.dataDownloadByDongfangcf import downlaodDataBycode +reload(sys) +sys.setdefaultencoding('utf-8') +# import sys +# reload( sys ) +# sys.setdefaultencoding('gbk') +# AVR_SHORT = 12 +# AVR_LONG = 40 + +SIGNAL_BUY = 1 #买 +SIGNAL_SALE = -1 #卖 +SIGNAL_DEFAULT = 0 + + +# 导入csv股票数据，写5日、20日、60日移动平均线 +def processEMA(stockCsvPath, stockCsvNewPath): + #导入数据，stockCsvPath为在电脑中的路径 + stock_data = pd.read_csv(stockCsvPath) + + # 将数据按照交易日期从远到近排序 + stock_data.sort('Date', inplace=True) + + #=====================计算移动平均线 + + # 分别计算5日、20日、60日移动平均线 + ma_list = [5, 20, 60] + + # 计算简单算术移动平均线MA - 注意：stock_data['close']为股票每条的收盘价 + for ma in ma_list: + stock_data['MA_' + str(ma)] = pd.rolling_mean(stock_data['Adj Close'], ma) + + # 计算指数平滑移动平均线EMA + for ma in ma_list: + stock_data['EMA_' + str(ma)] = pd.ewma(stock_data['Adj Close'], span=ma) + + # 将数据按照交易日期从近到远排序 + stock_data.sort('Date', ascending=False, inplace=True) + + stock_data['DIF'] = stock_data['EMA_'+str(ma_list[0])] - stock_data['EMA_'+str(ma_list[-1])] + stock_data['DEA_' + str(10)] = pd.ewma(stock_data['DIF'], span=10) + + # =================================== 将算好的数据输出到csv文件，这里请填写输出文件在您电脑的路径 + stock_data.to_csv(stockCsvNewPath, index=False) + +# 自适应均线 +def self_adaptive_ma(stock_data,Avg): + AVR_SHORT = Avg[0] + AVR_LONG = Avg[1] + # 将数据按照交易日期从远到近排序 + # stock_data.sort('Date', inplace=True) + + close_price = stock_data['Adj Close'].get_values() + high_price = stock_data['High'].get_values() + low_price = stock_data['Low'].get_values() + longDay = 100 + + print len(close_price) + if len(close_price) < 100: + return + + print close_price[-10:-1] + direction = abs(close_price[-1] - close_price[-10]) + volatility = sum(abs(close_price[i+1]-close_price[i]) for i in range(-9,0)) + ER = abs(direction/volatility) + fastSC = 2.0/(2.0+1) + slowSC = 2.0/(30.0+1) + sSC = ER * (fastSC-slowSC) + slowSC + constaint = sSC*sSC + + #EMA 100 + ema_close_100 = pd.ewma(close_price, span=longDay) + ema_high_100 = pd.ewma(high_price, span=longDay) + ema_low_100 = pd.ewma(low_price, span=longDay) + + amaClose = ema_close_100[-1] + constaint * (close_price[-1] - ema_close_100[-1]) + amaHigh = ema_high_100[-1] + constaint * (high_price[-1] - ema_high_100[-1]) + amaLow = ema_low_100[-1] + constaint * (low_price[-1] - ema_low_100[-1]) + print ema_close_100[-1], ema_high_100[-1], ema_low_100[-1] + + BKPRICE = 0.0 + SKPRICE = float('Inf') + status = SIGNAL_DEFAULT + print high_price[-1], low_price[-1], close_price[-1] + if low_price[-1] > amaHigh: + status = SIGNAL_BUY + elif close_price[-1] < amaClose or close_price[-1] <= 0.995 * BKPRICE: + status = SIGNAL_BUY + elif high_price[-1] < amaLow: + status = SIGNAL_SALE + elif close_price[-1] > amaClose or close_price[-1] >= 1.005 * SKPRICE: + status = SIGNAL_SALE + + return status + + +# MA指标择时 (回测） +def select_Time_MA(stock_data, stockName,*Avg): + AVR_SHORT = Avg[0] + AVR_LONG = Avg[1] + AVR_vLONG = Avg[2] + start_money = 100000000 + now_count = 0 + now_money = start_money + + # 将数据按照交易日期从远到近排序 + # stock_data.sort('Date', inplace=True) + close_price = stock_data['Adj Close'].get_values() + + #EMA + ma_list = [AVR_SHORT, AVR_LONG,AVR_vLONG] + ema_close_short = pd.ewma(close_price, span=ma_list[0]) + ema_close_long = pd.ewma(close_price, span=ma_list[1]) + ema_close_vlong = pd.ewma(close_price, span=ma_list[2]) + + signals = [0]*(ma_list[1]+1) + tradeTimes = 0 + bBuySignal = True + currentSignal=[] + for t in range(ma_list[1]+1, len(close_price)): + signal = SIGNAL_DEFAULT + if ema_close_short[t] > ema_close_short[t-1] and ema_close_short[t] >ema_close_vlong[t] \ + and ema_close_short[t-1] < ema_close_vlong[t-1]: + if bBuySignal: + signal = SIGNAL_BUY + now_count = (int)(start_money / close_price[t] /100)*100 + now_money = start_money - now_count * close_price[t] + #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money + bBuySignal = False + # elif ema_close_long[t] < ema_close_long[t-1] and ema_close_short[t] < ema_close_long[t] \ + # and ema_close_short[t-1] > ema_close_long[t-1]: + elif ema_close_short[t] < ema_close_short[t - 1] and ema_close_short[t] < ema_close_long[t] \ + and ema_close_short[t - 1] > ema_close_long[t - 1]: # + if bBuySignal == False: + signal = SIGNAL_SALE + now_money += now_count * close_price[t] + now_count = 0 + #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money + bBuySignal = True + signals.append(signal) + if signal != 0: + # print 't:', str(stock_data.ix[t, 'Date']), ' signal:', signal + tradeTimes += 1 + if (t <= len(close_price)) and (t >= (len(close_price) - 4)): + currentSignal.append(['t:', str(stock_data.ix[t, 'Date']), ' signal:', signal]) + getmony=(now_money + now_count * close_price[-1] - start_money) / start_money + over=(getmony -(close_price[-1]-close_price[ma_list[1]+1])/close_price[ma_list[1]+1])*100 + print stockName[0],stockName[1], u"收益率：", getmony*100, '%\t'+ u'超收：',over, '%\t'\ + u"交易次数", tradeTimes, u" 最新市值：", now_money+now_count * close_price[-1] ,str(Avg) + stock_data['SIGNAL_MA'] = signals + return [(now_money+now_count * close_price[-1]-start_money)/start_money*100,currentSignal,tradeTimes,over] +# fig = plt.figure(facecolor='white') +# left, width = 0.1, 0.8 +# rect1 = [left, 0.5, width, 0.4] +# rect2 = [left, 0.1, width, 0.3] +# +# axescolor = '#f6f6f6' # the axes background color +# ax1 = fig.add_axes(rect1, axisbg=axescolor) #left, bottom, width, height +# ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1) +# +# ax1.plot(range(len(close_price)), close_price, color="black", linewidth=1) +# ax1.grid() +# ax2.plot(range(len(ema_close_long)), ema_close_long,label='', color="red", linewidth=1) +# ax2.plot(range(len(ema_close_short)), ema_close_short,label='', color="blue", linewidth=1) +# ax2.grid() + #plt.show() + + # 将数据按照交易日期从近到远排序 + #stock_data.sort('date', ascending=False, inplace=True) + +# MACD指标择时 (回测） +def select_Time_MACD(stock_data, stockName,*Avg): + AVR_SHORT = Avg[0] + AVR_LONG = Avg[1] + AVR_vLONG = Avg[2] + start_money = 100000000 + now_count = 0 + now_money = start_money + + # 将数据按照交易日期从远到近排序 + # stock_data.sort('Date', inplace=True) + close_price = stock_data['Adj Close'].get_values() + + #EMA + ma_list = [AVR_SHORT, AVR_LONG,AVR_vLONG] + ma_dea = Avg[2] #10 默认9 + ema_close_short = pd.ewma(close_price, span=ma_list[0]) + ema_close_long = pd.ewma(close_price, span=ma_list[1]) + ema_close_vlong = pd.ewma(close_price, span=ma_list[2]) + + dif_price = ema_close_short - ema_close_long + dea_price = pd.ewma(dif_price, span=ma_dea) + macd_price = 2 * (dif_price - dea_price) + + signals = [0]*(ma_list[1]+1) + tradeTimes = 0 + bBuySignal = True + currentSignal=[] + for t in range(ma_list[1]+1, len(close_price)): + signal = SIGNAL_DEFAULT + if dif_price[t] > dif_price[t-1] and dif_price[t] > dea_price[t] \ + and dif_price[t-1] < dea_price[t-1] and dea_price[t] > 0: + if bBuySignal: + signal = SIGNAL_BUY + now_count = (int)(now_money / close_price[t] /100)*100 + now_money = start_money - now_count * close_price[t] + #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money + bBuySignal = False + elif dif_price[t] < dif_price[t-1] and dif_price[t] < dea_price[t] \ + and dif_price[t-1] > dea_price[t-1] and dif_price[t] < 0: + if bBuySignal == False: + signal = SIGNAL_SALE + now_money += now_count * close_price[t] + now_count = 0 + #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money + bBuySignal = True + signals.append(signal) + if signal != 0: + #print 't:', str(stock_data.ix[t, 'Date']), ' signal:', signal + tradeTimes += 1 + if (t<=len(close_price))and(t>=(len(close_price)-4)): + currentSignal.append(['t:', str(stock_data.ix[t, 'Date']), ' signal:', signal]) + getmony = (now_money + now_count * close_price[-1] - start_money) / start_money + over = (getmony - (close_price[-1] - close_price[ma_list[1]+1]) / close_price[ma_list[1]+1]) * 100 + print stockName[0],stockName[1], u"收益率：", getmony*100, '%\t'+ u'超收：',over,'%\t' \ + u"交易次数", tradeTimes, u" 最新市值：", now_money+now_count * close_price[-1] ,str(Avg) + stock_data['SIGNAL_MACD'] = signals + return [(now_money+now_count * close_price[-1]-start_money)/start_money*100,currentSignal,tradeTimes,over] +# fig = plt.figure(facecolor='white') +# left, width = 0.1, 0.8 +# rect1 = [left, 0.5, width, 0.4] +# rect2 = [left, 0.1, width, 0.3] +# +# axescolor = '#f6f6f6' # the axes background color +# ax1 = fig.add_axes(rect1, axisbg=axescolor) #left, bottom, width, height +# ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1) +# +# ax1.plot(range(len(close_price)), close_price, color="black", linewidth=1) +# ax1.grid() +# ax2.plot(range(len(dif_price)), ema_close_long,label='', color="red", linewidth=1) +# ax2.plot(range(len(dea_price)), ema_close_short,label='', color="blue", linewidth=1) +# ax2.grid() + #plt.show() + + # 将数据按照交易日期从近到远排序 + #stock_data.sort('date', ascending=False, inplace=True) + + # return dif_price, dea_price, macd_price + +# DMA指标择时 (回测） +def select_Time_DMA(stock_data, stockName,*Avg): + AVR_SHORT = Avg[0] + AVR_LONG = Avg[1] + AVR_vLONG = Avg[2] + start_money = 100000000 + now_count = 0 + now_money = start_money + + # 将数据按照交易日期从远到近排序 + # stock_data.sort('Date', inplace=True) + close_price = stock_data['Adj Close'].get_values() + + #MA + ma_list = [AVR_SHORT, AVR_LONG,AVR_vLONG] + ma_dea = Avg[2]#10 + ma_close_short = pd.rolling_mean(close_price, ma_list[0]) + ma_close_long = pd.rolling_mean(close_price, ma_list[1]) + ma_close_vlong = pd.rolling_mean(close_price, ma_list[2]) + + dma_price = ma_close_short - ma_close_long + ama_price = pd.rolling_mean(dma_price, ma_dea) + + signals = [0]*(ma_list[1]+1) + tradeTimes = 0 + bBuySignal = True + currentSignal=[] + for t in range(ma_list[1]+1, len(close_price)): + + signal = SIGNAL_DEFAULT + + if dma_price[t] > dma_price[t-1] and dma_price[t] > ama_price[t] \ + and dma_price[t-1] < ama_price[t-1]: + if bBuySignal: + signal = SIGNAL_BUY + now_count = (int)(now_money / close_price[t] /100)*100 + now_money = start_money - now_count * close_price[t] + #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money + bBuySignal = False + elif dma_price[t] < dma_price[t-1] and dma_price[t] < ama_price[t] \ + and dma_price[t-1] > ama_price[t-1]: + if bBuySignal == False: + signal = SIGNAL_SALE + now_money += now_count * close_price[t] + now_count = 0 + #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money + bBuySignal = True + signals.append(signal) + if signal != 0: + #print 't:', str(stock_data.ix[t, 'Date']), ' signal:', signal + tradeTimes += 1 + if (t <= len(close_price)) and (t >= (len(close_price) - 4)): + currentSignal.append(['t:', str(stock_data.ix[t, 'Date']), ' signal:', signal]) + + print stockName[0],stockName[1], u"收益率：", (now_money+now_count * close_price[-1]-start_money)/start_money*100, '%\t' \ + u"交易次数", tradeTimes, u" 最新市值：", now_money+now_count * close_price[-1] ,str(Avg) + stock_data['SIGNAL_DMA'] = signals + return [(now_money + now_count * close_price[-1] - start_money) / start_money * 100,currentSignal,tradeTimes] +# fig = plt.figure(facecolor='white') +# left, width = 0.1, 0.8 +# rect1 = [left, 0.5, width, 0.4] +# rect2 = [left, 0.1, width, 0.3] +# +# axescolor = '#f6f6f6' # the axes background color +# ax1 = fig.add_axes(rect1, axisbg=axescolor) #left, bottom, width, height +# ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1) +# +# ax1.plot(range(len(close_price)), close_price, color="black", linewidth=1) +# ax1.grid() +# ax2.plot(range(len(dma_price)), dma_price,label='', color="red", linewidth=1) +# ax2.plot(range(len(ama_price)), ama_price,label='', color="blue", linewidth=1) +# ax2.grid() + #plt.show() + + # 将数据按照交易日期从近到远排序 + #stock_data.sort('date', ascending=False, inplace=True) + +# DMA指标择时 (回测） +def select_Time_TRIX(stock_data, stockName,*Avg): + AVR_SHORT = Avg[0] + AVR_LONG = Avg[1] + start_money = 100000000 + now_count = 0 + now_money = start_money + + # 将数据按照交易日期从远到近排序 + # stock_data.sort('Date', inplace=True) + close_price = stock_data['Adj Close'].get_values() + + #EMA + ma_list = [AVR_SHORT, AVR_SHORT] #N,M + + ema_close = pd.ewma(close_price, span=ma_list[0]) + ema_close = pd.ewma(ema_close, span=ma_list[0]) + tr_close = pd.ewma(ema_close, span=ma_list[0]) + + trixsList = [0] + for i in range(1, len(tr_close)): + #print tr_close[i], tr_close[i-1] + trix = (tr_close[i]-tr_close[i-1])/tr_close[i-1]*100 + trixsList.append(trix) + trixs = np.array(trixsList) + maxtrix = pd.rolling_mean(trixs, ma_list[1]) + + signals = [0]*(ma_list[1]+1) + tradeTimes = 0 + bBuySignal = True + currentSignal=[] + for t in range(ma_list[1]+1, len(close_price)): + + signal = SIGNAL_DEFAULT + if trixs[t] > trixs[t-1] and trixs[t] > maxtrix[t] \ + and trixs[t-1] < maxtrix[t-1]: + if bBuySignal: + signal = SIGNAL_BUY + now_count = (int)(now_money / close_price[t] /100)*100 + now_money = start_money - now_count * close_price[t] + #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money + bBuySignal = False + elif trixs[t] < trixs[t-1] and trixs[t] < maxtrix[t] \ + and trixs[t-1] > maxtrix[t-1]: + if bBuySignal == False: + signal = SIGNAL_SALE + now_money += now_count * close_price[t] + now_count = 0 + #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money + bBuySignal = True + signals.append(signal) + if signal != 0: + #print 't:', str(stock_data.ix[t, 'Date']), ' signal:', signal + tradeTimes += 1 + if (t <= len(close_price)) and (t >= (len(close_price) - 4)): + currentSignal.append(['t:', str(stock_data.ix[t, 'Date']), ' signal:', signal]) + + print stockName[0],stockName[1], u"收益率：", (now_money+now_count * close_price[-1]-start_money)/start_money*100, '%\t' \ + u"交易次数", tradeTimes, u" 最新市值：", now_money+now_count * close_price[-1] ,str(Avg) + stock_data['SIGNAL_TRIX'] = signals + return [(now_money + now_count * close_price[-1] - start_money) / start_money * 100,currentSignal,tradeTimes] + +# 组合择时指标 (回测） +def select_Time_Mix(stock_data, stockName,*Avg): + AVR_SHORT = Avg[0] + AVR_LONG = Avg[1] + start_money = 100000000 + now_count = 0 + now_money = start_money + + # 综合策略 + signals = [] + tradeTimes = 0 + bBuySignal = True + + close_price = stock_data['Adj Close'].get_values() + s_ma = stock_data['SIGNAL_MA'].get_values() + s_macd = stock_data['SIGNAL_MACD'].get_values() + s_dma = stock_data['SIGNAL_DMA'].get_values() + s_trix = stock_data['SIGNAL_TRIX'].get_values() + + for i in range(len(s_ma)): + + signal = SIGNAL_DEFAULT + + up = 0; + down = 0; + if s_ma[i] == 1: + up += 1 + elif s_ma[i] == -1: + down += 1 + + if s_macd[i] == 1: + up += 1 + elif s_macd[i] == -1: + down += 1 + + if s_dma[i] == 1: + up += 1 + elif s_dma[i] == -1: + down += 1 + + if s_trix[i] == 1: + up += 1 + elif s_trix[i] == -1: + down += 1 + + if up >= 3: + if bBuySignal: + signal = SIGNAL_BUY + now_count = (int)(now_money / close_price[i] /100)*100 + now_money = start_money - now_count * close_price[i] + #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money + bBuySignal = False + elif down <= -3: + if bBuySignal == False: + signal = SIGNAL_SALE + now_money += now_count * close_price[i] + now_count = 0 + #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money + bBuySignal = True + + signals.append(signal) + if signal != 0: + #print 't:', t, ' signal:', signal + tradeTimes += 1 + + print stockName[0],stockName[1], u"收益率：", (now_money+now_count * close_price[-1]-start_money)/start_money*100, '%\t' \ + u"交易次数", tradeTimes, u" 最新市值：", now_money+now_count * close_price[-1] + stock_data['SIGNAL_MIX'] = signals + return (now_money + now_count * close_price[-1] - start_money) / start_money * 100 +# AMA指标择时 +def select_Time_AMA(stock_data, stockName,*Avg): + AVR_SHORT = Avg[0] + AVR_LONG = Avg[1] + percentage = 0.1 + + start_money = 100000000 + now_count = 0 + now_money = 0 + one_hand = 10000 + + # 将数据按照交易日期从远到近排序 + # stock_data.sort('Date', inplace=True) + + close_price = stock_data['Adj Close'].get_values() + + # 指数平滑序列 + containts = [0]*10 + for i in range(10, len(close_price)): + sub_price = close_price[i-10:i] + constaint = getConstaint(sub_price) + containts.append(constaint); + + ama_price = [close_price[0]] + for i in range(1, len(close_price)): + ama = containts[i-1] * close_price[i-1] + (1-containts[i-1])*ama_price[i-1] + ama_price.append(ama) + + signals = [0]*21 + tradeTimes = 0 + + record_buy = 0 + record_sale = [] + + # 从20天以后开始判断买卖点 + for i in range(21, len(ama_price)): + signal = SIGNAL_DEFAULT + #print np.array(ama_price[i-19:i+1]) - np.array(ama_price[i-20:i]) + threshold = percentage * np.std(np.array(ama_price[i-19:i+1]) - np.array(ama_price[i-20:i])) # 过滤器 + + if ama_price[i] - np.min(ama_price[i-5:i]) > threshold: + signal = SIGNAL_BUY + now_count += one_hand + record_buy += one_hand * close_price[i] + #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money + elif np.max(ama_price[i-5:i]) - ama_price[i] > threshold: + signal = SIGNAL_SALE + if now_count > one_hand: + now_count -= one_hand + record_sale = one_hand * close_price[i] + #print u"股票价格/持股数/剩余金额：",close_price[t], '/', now_count, '/', now_money + signals.append(signal) + if signal != 0: + # print 't:', str(stock_data.ix[i, 'Date']), ' signal:', signal + tradeTimes += 1 + + # 成本 + # print stockName[0],stockName[1],u'盈利', record_sale + now_count * close_price[-1] - record_buy + + print stockName[0],stockName[1], u"收益率：", (now_money+now_count * close_price[-1]-start_money)/start_money*100, '%\t' \ + u"交易次数", tradeTimes, u" 最新市值：", now_money+now_count * close_price[-1] ,str(Avg) + stock_data['SIGNAL_AMA'] = signals + # return ama_price + return (now_money + now_count * close_price[-1] - start_money) / start_money * 100 + +# 获取平方平滑系数 +def getConstaint(prices): + direction = abs(prices[-1] - prices[0]) + volatility = sum(abs(prices[i+1]-prices[i]) for i in range(len(prices)-1)) + ER = abs(direction/volatility) + fastSC = 2.0/(2.0+1) + slowSC = 2.0/(30.0+1) + sSC = ER * (fastSC-slowSC) + slowSC + constaint = sSC*sSC + return constaint + +# 选择一种均线策略 +def getMAStrategy(stockCsvPath, stockName, strategyName='MACD'): + if os.path.exists(stockCsvPath) == False: + return + stock_data = pd.read_csv(stockCsvPath) + if strategyName == 'MACD': + return select_Time_MACD(stock_data, stockName) + elif strategyName == 'MA': + return select_Time_MA(stock_data, stockName) + elif strategyName == 'DMA': + return select_Time_DMA(stock_data, stockName) + elif strategyName == 'TRIX': + return select_Time_TRIX(stock_data, stockName) + elif strategyName == 'AMA': + return select_Time_AMA(stock_data, stockName) + +def Grid_Constructor(numline,numParm,data): + alleles = GAllele.GAlleles() + for i in range(0, numline*numParm): + alleles.add(GAllele.GAlleleRange(data[i][0], data[i][1])) + return alleles + +def func(fitness,strategyName,stock_data, stockName): + def fitness_function(chromosome): + score = 0.0 + if fitness == 'abs': + if strategyName == 'MACD': + score = select_Time_MACD(stock_data, stockName, *chromosome)[0] + elif strategyName == 'MA': + score = select_Time_MA(stock_data, stockName, *chromosome)[0] + elif strategyName == 'DMA': + score = select_Time_DMA(stock_data, stockName, *chromosome)[0] + elif strategyName == 'TRIX': + score = select_Time_TRIX(stock_data, stockName, *chromosome)[0] + elif strategyName == 'AMA': + score = select_Time_AMA(stock_data, stockName, *chromosome) + return score + return fitness_function +def cal_opt(numParm,fintness,strategyName,func,stock_data, stockName): + if (strategyName == 'MACD' or strategyName == 'DMA')and 'Low' in stock_data.columns: + datarange = [[8, 15], [21, 31], [6, 12]] + elif (strategyName == 'MACD' or strategyName == 'DMA')and 'Low' not in stock_data.columns: + datarange = [[3, 20], [21, 41], [5, 20]] + else: + datarange=[[2,5],[6,19],[7,60]] + datarange=datarange[:numParm] + genome = G1DList.G1DList(numParm) + genome.evaluator.set(func(fintness,strategyName,stock_data, stockName)) + # genome.setParams(allele=Grid_Constructor(numline,data1)) + genome.setParams(allele=Grid_Constructor(1,numParm,datarange)) + genome.initializator.set(Initializators.G1DListInitializatorAllele) + genome.mutator.set(Mutators.G1DListMutatorAllele) + + ga = GSimpleGA.GSimpleGA(genome, seed=400) + ga.setPopulationSize(40) + ga.setGenerations(40) + ga.setCrossoverRate(0.8) + ga.setMutationRate(0.18) + ga.selector.set(Selectors.GRankSelector) + # ga.terminationCriteria.set(GSimpleGA.FitnessStatsCriteria) + # Change the scaling method + pop = ga.getPopulation() + pop.scaleMethod.set(Scaling.SigmaTruncScaling) + ga.evolve(freq_stats=10) + best = ga.bestIndividual() + return [best.genomeList,round(best.score,5)] +# 执行策略 +def run(stockCsvPath, stockName): + if os.path.exists(stockCsvPath) == False: + return + stockCsvNewPath = stockName + '_macd.csv' + processEMA(stockCsvPath, stockCsvNewPath) + stock_data = pd.read_csv(stockCsvPath) + self_adaptive_ma(stock_data) + + print u'>>>>>>>>>>>>> MA 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' + select_Time_MA(stock_data, stockName) + + print u'>>>>>>>>>>>>> MACD 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' + select_Time_MACD(stock_data, stockName) + + print u'>>>>>>>>>>>>> DMA 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' + select_Time_DMA(stock_data, stockName) + + print u'>>>>>>>>>>>>> TRIX 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' + select_Time_TRIX(stock_data, stockName) + + print u'>>>>>>>>>>>>> 组合策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' + select_Time_Mix(stock_data, stockName) + + print u'>>>>>>>>>>>>> AMA策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' + select_Time_AMA(stock_data, stockName) + + print '\n' +def match(select_method,stockName,lines): + contmatch=[] + for line in lines: + if line != '\n': + # print str(line.split('|')) + if(stockName[0] == line.split('|')[1])and(select_method == line.split('|')[0]): + contmatch.append([line.split('|')[0],line.split('|')[3]]) + return contmatch +def macdmain(stockName,fundlist): + print "main begin" + # stockList = ['000725', '000783', '002167', '002505', '002600', '300315', '600000', '600011', '600048', '601001'] + # downloadAllHistoryAShareData(stockList) + # stockList = ['000725'] + buysell=[] + # for stockName in stockList: + # if stockName != '002600': + # continue + # stockCsvPath = os.path.pardir + "\\stockdata\\" + stockName + '.csv' + # stockCsvPath = stockName + '.csv' + # if os.path.exists(stockCsvPath) == False: + # continue + # stockCsvNewPath = stockName + '_macd.csv' + # processEMA(stockCsvPath, stockCsvNewPath) + # stock_data = pd.read_csv(stockCsvPath) + stockTxtNewPath = "./stockdata/"+ 'GAOpt100.txt' + # fundlist = efund_mail2.get_histrydata(stockName, 180) + stock_data = pd.DataFrame(fundlist[:200][::-1], columns=['Date', 'Adj Close', 'countclose', 'change']) + # self_adaptive_ma(stock_data) + #period_type = 'w' + #stock_data = stock_data.resample(period_type,how='last') + #stock_data = + + Avg1=([3,8,40]) + # select_method=['MA','MACD','DMA','TRIX','AMA'] + select_method = ['MA','MACD'] + AvgParm=[] + cal='' + for i in select_method: + file_to_read=open(stockTxtNewPath, 'r') + lines = file_to_read.readlines() # 整行读取数据 + if not lines: + break + pass + else: + contmatch=match(i,stockName,lines) + if (cal=='new')and(len(contmatch)==0): + best=cal_opt(3, 'abs', i, func, stock_data, stockName) + # AvgParm.append(best) + f = open(stockTxtNewPath, 'a') + f.write(str(i)+'|') + f.write(str(stockName[0])+'|'+stockName[1].encode('gb2312')+'|') + f.write(str(best[0]).split('[')[1].split(']')[0]+'|'+str(best[1])) + f.write('\n') + f.close() + Avg = best[0] + if len(contmatch)!=0: + Avg = [int(contmatch[0][1].split(',')[0]),int(contmatch[0][1].split(',')[1]),int(contmatch[0][1].split(',')[2])] + elif (len(contmatch)==0)and(i != 'MA'): + Avg = ([12, 26, 9]) + elif (len(contmatch)==0)and(i == 'MA'): + Avg = ([3, 8, 44]) + result=None + if(i=='MA'): + print u'-- MA 策略 --' + result=select_Time_MA(stock_data, stockName,*Avg) + if(len(result[1])): + buysell.append([stockName,i,result]) + if (i == 'MACD'): + print u'-- MACD 策略 --' + result=select_Time_MACD(stock_data, stockName, *Avg) + if (len(result[1])): + buysell.append([stockName, i, result]) + if (i == 'DMA'): + print u'-- DMA 策略 --' + result=select_Time_DMA(stock_data, stockName,*Avg) + if (len(result[1])): + buysell.append([stockName, i, result]) + if (i == 'TRIX'): + print u'-- TRIX 策略 -->' + result=select_Time_TRIX(stock_data, stockName,*Avg) + if (len(result[1])): + buysell.append([stockName, i, result]) + + # print u'-- 组合策略 --' + # select_Time_Mix(stock_data, stockName,*Avg) + # + # print u'-- AMA策略 --' + # select_Time_AMA(stock_data, stockName,*Avg) + # print '\n' + print "main end" + return buysell +def mainStock(): + file = {'stockcodewangyiShangz.txt': 0, + 'stockcodewangyiShenz.txt': 1} # 'stockcodewangyiqdii.txt':'','stockcodewangyidetail.txt':'' + buysell=[] + stockTxtNewPath = "./stockdata/" + 'GAOpt100.txt' + stockTxtNewPath = "./stockdata/" + 'GAOpt100.txt' + for jj in file: + CodeList = [] + stockTxtNewPath1 = "./stockdata/" + jj #r'E:\03IT\GitHub\stock-2' + + file_to_read = open(stockTxtNewPath1, 'r') + lines = file_to_read.readlines() # 整行读取数据 + if not lines: + pass + else: + for line in lines: + if line != '\n' and line != ' ': + if jj == 'stockcodewangyidetail.txt': + icode = line.split(' ') + else: + icode = line.split('\t') + CodeList.append([icode[1],icode[2].decode('gbk')]) + # 抓取数据并保存到本地csv文件 + for stockName in CodeList: + if not ' ' in stockName[0]: + # stockCsvPath = r'E:\03IT\GitHub\stock-2' + "\\stockdata\\dongfangcf\\" + stockName[0] + '.csv' + stockCsvPath=downlaodDataBycode(stockName[0],jj) + # stockCsvPath = stockName + '.csv' + if os.path.exists(stockCsvPath) == False: + continue + else: + stockCsvNewPath = stockName[0] + '.csv' + # processEMA(stockCsvPath, stockCsvNewPath) + stock_data = pd.read_csv(stockCsvPath,encoding="gb2312") + if len(stock_data)<3: + continue + else: + columns1 = ['Date', 'Adj Close', 'High', 'Low'] + stock_data = pd.DataFrame(stock_data.loc[:,[u'日期', u'收盘价', u'最高价', u'最低价']], columns=['日期', '收盘价', '最高价', '最低价']) + stock_data.columns=columns1 + stock_data['Date'] = pd.to_datetime(stock_data['Date']) + stock_data=pd.DataFrame(stock_data.values[::-1],columns=columns1) + select_method = ['MA', 'MACD'] + cal = '' + for i in select_method: + file_to_read = open(stockTxtNewPath, 'r') + lines = file_to_read.readlines() # 整行读取数据 + if not lines: + contmatch =[] + else: + contmatch = match(i, stockName, lines) + if (cal == 'new') or (len(contmatch) == 0): + best = cal_opt(3, 'abs', i, func, stock_data, stockName) + # AvgParm.append(best) + f = open(stockTxtNewPath, 'a') + f.write(str(i) + '|') + f.write(str(stockName[0]) + '|' + stockName[1].encode('gbk') + '|') + f.write(str(best[0]).split('[')[1].split(']')[0] + '|' + str(best[1])) + f.write('\n') + f.close() + Avg = ([2, 8, 40])#best[0] + if len(contmatch) != 0: + Avg = [int(contmatch[0][1].split(',')[0]), int(contmatch[0][1].split(',')[1]), + int(contmatch[0][1].split(',')[2])] + elif (len(contmatch) == 0) and (i != 'MA'): + Avg = ([12, 26, 9]) + elif (len(contmatch) == 0) and (i == 'MA'): + Avg = ([2, 8, 40]) + result = None + if (i == 'MA'): + print u'-- MA 策略 --' + result = select_Time_MA(stock_data, stockName, *Avg) + # self_adaptive_ma(stock_data, Avg) + if (len(result[1])): + buysell.append([stockName, i, result]) + if (i == 'MACD'): + print u'-- MACD 策略 --' + result = select_Time_MACD(stock_data, stockName, *Avg) + if (len(result[1])): + buysell.append([stockName, i, result]) + if (i == 'DMA'): + print u'-- DMA 策略 --' + result = select_Time_DMA(stock_data, stockName, *Avg) + if (len(result[1])): + buysell.append([stockName, i, result]) + if (i == 'TRIX'): + print u'-- TRIX 策略 -->' + result = select_Time_TRIX(stock_data, stockName, *Avg) + if (len(result[1])): + buysell.append([stockName, i, result]) + + # print u'-- 组合策略 --' + # select_Time_Mix(stock_data, stockName,*Avg) + # + # print u'-- AMA策略 --' + # select_Time_AMA(stock_data, stockName,*Avg) + print '\n' + print "main end" + return buysell +if __name__ == "__main__": + print "main begin" + buysellstock= mainStock() + print buysellstock + stockList=['000725','000783','002167','002505','002600','300315','600000','600011','600048','601001'] + # downloadAllHistoryAShareData(stockList) + for stockName in stockList: + stockCsvPath = os.path.pardir +"\\stockdata\\" + stockName + '.csv' + #stockCsvPath = stockName + '.csv' + if os.path.exists(stockCsvPath) == False: + continue + #stockCsvNewPath = stockName + '_macd.csv' + #processEMA(stockCsvPath, stockCsvNewPath) + stock_data = pd.read_csv(stockCsvPath) + #self_adaptive_ma(stock_data) + Avg=[5,10,40] + print u'-- MA 策略 --' + select_Time_MA(stock_data, stockName,Avg) + + print u'-- MACD 策略 --' + select_Time_MACD(stock_data, stockName,Avg) + + print u'-- DMA 策略 --' + select_Time_DMA(stock_data, stockName,Avg) + + print u'-- TRIX 策略 -->' + select_Time_TRIX(stock_data, stockName,Avg) + + print u'-- 组合策略 --' + select_Time_Mix(stock_data, stockName,Avg) + + print u'-- AMA策略 --' + select_Time_AMA(stock_data, stockName,Avg) + + print '\n' + print "main end" diff --git a/trade_process/strategy/macd_back_test.pyc b/trade_process/strategy/macd_back_test.pyc new file mode 100644 index 0000000..0fbd226 Binary files /dev/null and b/trade_process/strategy/macd_back_test.pyc differ diff --git a/trade_process/strategy/macd_live_test.py b/trade_process/strategy/macd_live_test.py index 1199873..2100f27 100644 --- a/trade_process/strategy/macd_live_test.py +++ b/trade_process/strategy/macd_live_test.py @@ -1,252 +1,288 @@ -#!/usr/local/bin/python -#coding=utf-8 - -#实时均线策略 - -import pandas as pd -import numpy as np -from data_process.data_calcute import calcute_ma - -from init import * -from util.codeConvert import GetNowDate -from copy import deepcopy - - -class MAStrategy: - - # df: DataFrame - def __init__(self, code, trade, df_close): - """ - :param code: 股票代码 - :param trade: 实时股价, float - :param df_close: 收盘价序列,已排序, columns=['date', 'close'] - """ - - self.AVR_SHORT = AVR_SHORT - self.AVR_LONG = AVR_LONG - self.MA_DEA = 10 - - self.COL_MA_S = 'ma_{}'.format(self.AVR_SHORT) - self.COL_MA_L = 'ma_{}'.format(self.AVR_LONG) - self.COL_EMA_S = 'ema_{}'.format(self.AVR_SHORT) - self.COL_EMA_L = 'ema_{}'.format(self.AVR_LONG) - - - #方式二 - # 将数据按照交易日期从远到近排序 - # df.sort(columns='date', inplace=True) - - df_close = calcute_ma(df_close, AVR_SHORT, AVR_LONG) - - if trade: #有实时股价 - df_now = deepcopy(df_close.tail(1)) - df_now['date'] = GetNowDate() - df_now['close'] = trade - df_close = pd.concat([df_close, df_now], ignore_index=True) - # print df_close.tail() - - #计算当前日期的ma - lastest_ma_short = sum(df_close['close'][-self.AVR_SHORT:])/self.AVR_SHORT - lastest_ma_long = sum(df_close['close'][-self.AVR_LONG:])/self.AVR_LONG - - df_last = df_close[-1:] - df_last[self.COL_MA_S] = lastest_ma_short - df_last[self.COL_MA_L] = lastest_ma_long - - - #计算当前的ema - lastest_ema_short = df_close[self.COL_EMA_S].get_values()[-2] * (self.AVR_SHORT-1)/(self.AVR_SHORT+1) + trade * 2 /(self.AVR_SHORT+1) - lastest_ema_long = df_close[self.COL_EMA_L].get_values()[-2] * (self.AVR_LONG-1)/(self.AVR_LONG+1) + trade * 2 /(self.AVR_LONG+1) - - df_last[self.COL_EMA_S] = lastest_ema_short - df_last[self.COL_EMA_L] = lastest_ema_long - - self.df_close = df_close - - # print self.df_close.head() - # print self.df_close.tail() - - - - # 组合择时指标 (实时） - def select_Time_Mix(self, conditionBuy = 2, conditonSale = 2): - - # 综合策略 - signalMA = self.select_Time_MA() - signalMACD = self.select_Time_MACD() - signalDMA = self.select_Time_DMA() - signalTRIX = self.select_Time_TRIX() - signalAMA = self.select_Time_AMA() - - # 买入信号的总数 - buyTotal = (abs(signalMA)+signalMA)/2 + (abs(signalMACD)+signalMACD)/2 + \ - (abs(signalDMA)+signalDMA)/2 + (abs(signalTRIX)+signalTRIX)/2 + (abs(signalAMA)+signalAMA)/2 - - # 卖出信号的总数 - saleTotal = (-abs(signalMA)+signalMA)/2 + (-abs(signalMACD)+signalMACD)/2 + \ - (-abs(signalDMA)+signalDMA)/2 + (-abs(signalTRIX)+signalTRIX)/2 + (-abs(signalAMA)+signalAMA)/2 - - signal = SIGNAL_DEFAULT - if buyTotal+saleTotal >= conditionBuy: - signal = SIGNAL_BUY - elif buyTotal+saleTotal <= -conditonSale: - signal = SIGNAL_SALE - - return signal - - # MA指标择时 - def select_Time_MA(self): - - #DMA - dma_close_short = self.df_close[self.COL_MA_S].get_values() - dma_close_long = self.df_close[self.COL_MA_L].get_values() - - - signal = SIGNAL_DEFAULT - - if dma_close_short[-1] > dma_close_short[-2] and dma_close_short[-1] > dma_close_long[-1] \ - and dma_close_short[-2] < dma_close_long[-2]: - signal = SIGNAL_BUY - elif dma_close_long[-1] < dma_close_long[-2] and dma_close_short[-1] < dma_close_long[-1] \ - and dma_close_short[-2] > dma_close_long[-2]: - signal = SIGNAL_SALE - - return signal - - - # MACD指标择时 - def select_Time_MACD(self): - - #EMA - # print self.df_close.tail() - ema_close_short = self.df_close[self.COL_EMA_S].get_values() - ema_close_long = self.df_close[self.COL_EMA_L].get_values() - - - dif_price = ema_close_short - ema_close_long - dea_price = pd.ewma(dif_price, span=self.MA_DEA) - macd_price = 2 * (dif_price - dea_price) - - signal = SIGNAL_DEFAULT - - if dif_price[-1] > dif_price[-2] and dif_price[-1] > dea_price[-2] \ - and dif_price[-2] < dea_price[-2] and dea_price[-1] > 0: - signal = SIGNAL_BUY - elif dif_price[-1] < dif_price[-2] and dif_price[-1] < dea_price[-1] \ - and dif_price[-2] > dea_price[-2] and dif_price[-1] < 0: - signal = SIGNAL_SALE - return signal - - # DMA指标择时 (回测） - def select_Time_DMA(self): - - # DMA - ma_close_short = self.df_close[self.COL_MA_S].get_values() - ma_close_long = self.df_close[self.COL_MA_L].get_values() - - # #MA - # ma_list = [self.AVR_SHORT, self.AVR_LONG] - # ma_dea = 10 - # - # if ma_list[0] == self.AVR_SHORT and ma_list[1] == self.AVR_LONG: - # ma_close_short = self.ma_short - # ma_close_long = self.ma_long - # else: - # ma_close_short = pd.rolling_mean(self.close_price, ma_list[0]) - # ma_close_long = pd.rolling_mean(self.close_price, ma_list[1]) - - dma_price = ma_close_short - ma_close_long - ama_price = pd.rolling_mean(dma_price, self.MA_DEA) - - signal = SIGNAL_DEFAULT - - if dma_price[-1] > dma_price[-2] and dma_price[-1] > ama_price[-1] \ - and dma_price[-2] < ama_price[-2]: - signal = SIGNAL_BUY - elif dma_price[-1] < dma_price[-2] and dma_price[-1] < ama_price[-1] \ - and dma_price[-2] > ama_price[-2]: - signal = SIGNAL_SALE - return signal - - - # TRIX指标择时 (回测） - def select_Time_TRIX(self): - - #EMA - ema_close_short = self.df_close[self.COL_EMA_S].get_values() - ema_ema_close_short = pd.ewma(ema_close_short, span=self.AVR_SHORT) - tr_close = pd.ewma(ema_ema_close_short, span=self.AVR_SHORT) - - # ma_list = [self.AVR_SHORT, self.AVR_SHORT] #N,M - # - # if ma_list[0] == self.AVR_SHORT: - # ema_close = self.ema_short - # else: - # ema_close = pd.ewma(self.close_price, span=ma_list[0]) - # ema_close = pd.ewma(ema_close, span=ma_list[0]) - # tr_close = pd.ewma(ema_close, span=ma_list[0]) - - trixsList = [0] - for i in range(1, len(tr_close)): - #print tr_close[i], tr_close[i-1] - trix = (tr_close[i]-tr_close[i-1])/tr_close[i-1]*100 - trixsList.append(trix) - trixs = np.array(trixsList) - maxtrix = pd.rolling_mean(trixs, self.AVR_LONG) - - signal = SIGNAL_DEFAULT - - if trixs[-1] > trixs[-2] and trixs[-1] > maxtrix[-1] \ - and trixs[-2] < maxtrix[-2]: - signal = SIGNAL_BUY - elif trixs[-1] < trixs[-2] and trixs[-1] < maxtrix[-1] \ - and trixs[-2] > maxtrix[-2]: - signal = SIGNAL_SALE - return signal - - - - - # AMA指标择时 - def select_Time_AMA(self): - - percentage = 0.1 - - close_price = self.df_close['close'].get_values() - - # 指数平滑序列 - containts = [0]*10 - for i in range(10, len(close_price)): - sub_price = close_price[i-10:i] - constaint = self._getConstaint(sub_price) - containts.append(constaint) - - ama_price = [float(close_price[0])] - for i in range(1, len(close_price)): - ama = containts[i-1] * float(close_price[i-1]) + (1-containts[i-1])*ama_price[i-1] - ama_price.append(float(ama)) - - #print np.array(ama_price[i-19:i+1]) - np.array(ama_price[i-20:i]) - threshold = percentage * np.std(np.array(ama_price[i-19:i+1]) - np.array(ama_price[i-20:i])) # 过滤器 - - signal = SIGNAL_DEFAULT - if ama_price[-1] - np.min(ama_price[-6:-1]) > threshold: - signal = SIGNAL_BUY - elif np.max(ama_price[-6:-1]) - ama_price[-1] > threshold: - signal = SIGNAL_SALE - - return signal - - # 获取平方平滑系数 - def _getConstaint(self, prices): - direction = abs(float(prices[-1]) - float(prices[0])) - volatility = sum(abs(float(prices[i+1])-float(prices[i])) for i in range(len(prices)-1)) - if volatility == 0.0: - return 0 - ER = abs(direction/volatility) - fastSC = 2.0/(2.0+1) - slowSC = 2.0/(30.0+1) - sSC = ER * (fastSC-slowSC) + slowSC - constaint = sSC*sSC - return constaint - +#!/usr/local/bin/python +#coding=utf-8 + +#实时均线策略 + +import pandas as pd +import numpy as np +from data_process.data_calcute import calcute_ma +from trade_process import efund_mail2 +from init import * +from util.codeConvert import GetNowDate +from copy import deepcopy + + +class MAStrategy: + + # df: DataFrame + def __init__(self, code, trade, df_close): + """ + :param code: 股票代码 + :param trade: 实时股价, float + :param df_close: 收盘价序列,已排序, columns=['date', 'close'] + """ + + self.AVR_SHORT = AVR_SHORT + self.AVR_LONG = AVR_LONG + self.MA_DEA = 10 + + self.COL_MA_S = 'ma_{}'.format(self.AVR_SHORT) + self.COL_MA_L = 'ma_{}'.format(self.AVR_LONG) + self.COL_EMA_S = 'ema_{}'.format(self.AVR_SHORT) + self.COL_EMA_L = 'ema_{}'.format(self.AVR_LONG) + + + #方式二 + # 将数据按照交易日期从远到近排序 + # df.sort(columns='date', inplace=True) + + df_close = calcute_ma(df_close, AVR_SHORT, AVR_LONG) + + if trade: #有实时股价 + df_now = deepcopy(df_close.tail(1)) + df_now['date'] = GetNowDate() + df_now['close'] = trade + df_close = pd.concat([df_close, df_now], ignore_index=True) + # print df_close.tail() + + #计算当前日期的ma + lastest_ma_short = sum(df_close['close'][-self.AVR_SHORT:])/self.AVR_SHORT + lastest_ma_long = sum(df_close['close'][-self.AVR_LONG:])/self.AVR_LONG + + df_last = df_close[-1:] + df_last[self.COL_MA_S] = lastest_ma_short + df_last[self.COL_MA_L] = lastest_ma_long + + + #计算当前的ema + lastest_ema_short = df_close[self.COL_EMA_S].get_values()[-2] * (self.AVR_SHORT-1)/(self.AVR_SHORT+1) + trade * 2 /(self.AVR_SHORT+1) + lastest_ema_long = df_close[self.COL_EMA_L].get_values()[-2] * (self.AVR_LONG-1)/(self.AVR_LONG+1) + trade * 2 /(self.AVR_LONG+1) + + df_last[self.COL_EMA_S] = lastest_ema_short + df_last[self.COL_EMA_L] = lastest_ema_long + + self.df_close = df_close + + # print self.df_close.head() + # print self.df_close.tail() + + + + # 组合择时指标 (实时） + def select_Time_Mix(self, conditionBuy = 2, conditonSale = 2): + + # 综合策略 + signalMA = self.select_Time_MA() + signalMACD = self.select_Time_MACD() + signalDMA = self.select_Time_DMA() + signalTRIX = self.select_Time_TRIX() + signalAMA = self.select_Time_AMA() + + # 买入信号的总数 + buyTotal = (abs(signalMA)+signalMA)/2 + (abs(signalMACD)+signalMACD)/2 + \ + (abs(signalDMA)+signalDMA)/2 + (abs(signalTRIX)+signalTRIX)/2 + (abs(signalAMA)+signalAMA)/2 + + # 卖出信号的总数 + saleTotal = (-abs(signalMA)+signalMA)/2 + (-abs(signalMACD)+signalMACD)/2 + \ + (-abs(signalDMA)+signalDMA)/2 + (-abs(signalTRIX)+signalTRIX)/2 + (-abs(signalAMA)+signalAMA)/2 + + signal = SIGNAL_DEFAULT + if buyTotal+saleTotal >= conditionBuy: + signal = SIGNAL_BUY + elif buyTotal+saleTotal <= -conditonSale: + signal = SIGNAL_SALE + + return signal + + # MA指标择时 + def select_Time_MA(self): + + #DMA + dma_close_short = self.df_close[self.COL_MA_S].get_values() + dma_close_long = self.df_close[self.COL_MA_L].get_values() + + + signal = SIGNAL_DEFAULT + + if dma_close_short[-1] > dma_close_short[-2] and dma_close_short[-1] > dma_close_long[-1] \ + and dma_close_short[-2] < dma_close_long[-2]: + signal = SIGNAL_BUY + elif dma_close_long[-1] < dma_close_long[-2] and dma_close_short[-1] < dma_close_long[-1] \ + and dma_close_short[-2] > dma_close_long[-2]: + signal = SIGNAL_SALE + + return signal + + + # MACD指标择时 + def select_Time_MACD(self): + + #EMA + # print self.df_close.tail() + ema_close_short = self.df_close[self.COL_EMA_S].get_values() + ema_close_long = self.df_close[self.COL_EMA_L].get_values() + + + dif_price = ema_close_short - ema_close_long + dea_price = pd.ewma(dif_price, span=self.MA_DEA) + macd_price = 2 * (dif_price - dea_price) + + signal = SIGNAL_DEFAULT + + if dif_price[-1] > dif_price[-2] and dif_price[-1] > dea_price[-2] \ + and dif_price[-2] < dea_price[-2] and dea_price[-1] > 0: + signal = SIGNAL_BUY + elif dif_price[-1] < dif_price[-2] and dif_price[-1] < dea_price[-1] \ + and dif_price[-2] > dea_price[-2] and dif_price[-1] < 0: + signal = SIGNAL_SALE + return signal + + # DMA指标择时 (回测） + def select_Time_DMA(self): + + # DMA + ma_close_short = self.df_close[self.COL_MA_S].get_values() + ma_close_long = self.df_close[self.COL_MA_L].get_values() + + # #MA + # ma_list = [self.AVR_SHORT, self.AVR_LONG] + # ma_dea = 10 + # + # if ma_list[0] == self.AVR_SHORT and ma_list[1] == self.AVR_LONG: + # ma_close_short = self.ma_short + # ma_close_long = self.ma_long + # else: + # ma_close_short = pd.rolling_mean(self.close_price, ma_list[0]) + # ma_close_long = pd.rolling_mean(self.close_price, ma_list[1]) + + dma_price = ma_close_short - ma_close_long + ama_price = pd.rolling_mean(dma_price, self.MA_DEA) + + signal = SIGNAL_DEFAULT + + if dma_price[-1] > dma_price[-2] and dma_price[-1] > ama_price[-1] \ + and dma_price[-2] < ama_price[-2]: + signal = SIGNAL_BUY + elif dma_price[-1] < dma_price[-2] and dma_price[-1] < ama_price[-1] \ + and dma_price[-2] > ama_price[-2]: + signal = SIGNAL_SALE + return signal + + # TRIX指标择时 (回测） + def select_Time_TRIX(self): + + #EMA + ema_close_short = self.df_close[self.COL_EMA_S].get_values() + ema_ema_close_short = pd.ewma(ema_close_short, span=self.AVR_SHORT) + tr_close = pd.ewma(ema_ema_close_short, span=self.AVR_SHORT) + + # ma_list = [self.AVR_SHORT, self.AVR_SHORT] #N,M + # + # if ma_list[0] == self.AVR_SHORT: + # ema_close = self.ema_short + # else: + # ema_close = pd.ewma(self.close_price, span=ma_list[0]) + # ema_close = pd.ewma(ema_close, span=ma_list[0]) + # tr_close = pd.ewma(ema_close, span=ma_list[0]) + + trixsList = [0] + for i in range(1, len(tr_close)): + #print tr_close[i], tr_close[i-1] + trix = (tr_close[i]-tr_close[i-1])/tr_close[i-1]*100 + trixsList.append(trix) + trixs = np.array(trixsList) + maxtrix = pd.rolling_mean(trixs, self.AVR_LONG) + + signal = SIGNAL_DEFAULT + + if trixs[-1] > trixs[-2] and trixs[-1] > maxtrix[-1] \ + and trixs[-2] < maxtrix[-2]: + signal = SIGNAL_BUY + elif trixs[-1] < trixs[-2] and trixs[-1] < maxtrix[-1] \ + and trixs[-2] > maxtrix[-2]: + signal = SIGNAL_SALE + return signal + + # AMA指标择时 + def select_Time_AMA(self): + + percentage = 0.1 + + close_price = self.df_close['close'].get_values() + + # 指数平滑序列 + containts = [0]*10 + for i in range(10, len(close_price)): + sub_price = close_price[i-10:i] + constaint = self._getConstaint(sub_price) + containts.append(constaint) + + ama_price = [float(close_price[0])] + for i in range(1, len(close_price)): + ama = containts[i-1] * float(close_price[i-1]) + (1-containts[i-1])*ama_price[i-1] + ama_price.append(float(ama)) + + #print np.array(ama_price[i-19:i+1]) - np.array(ama_price[i-20:i]) + threshold = percentage * np.std(np.array(ama_price[i-19:i+1]) - np.array(ama_price[i-20:i])) # 过滤器 + + signal = SIGNAL_DEFAULT + if ama_price[-1] - np.min(ama_price[-6:-1]) > threshold: + signal = SIGNAL_BUY + elif np.max(ama_price[-6:-1]) - ama_price[-1] > threshold: + signal = SIGNAL_SALE + + return signal + + # 获取平方平滑系数 + def _getConstaint(self, prices): + direction = abs(float(prices[-1]) - float(prices[0])) + volatility = sum(abs(float(prices[i+1])-float(prices[i])) for i in range(len(prices)-1)) + if volatility == 0.0: + return 0 + ER = abs(direction/volatility) + fastSC = 2.0/(2.0+1) + slowSC = 2.0/(30.0+1) + sSC = ER * (fastSC-slowSC) + slowSC + constaint = sSC*sSC + return constaint + +def macd_live(code, df=None): + fundlist = efund_mail2.get_histrydata(code, 365) + df = pd.DataFrame(fundlist[::-1], columns=['date', 'close', 'countclose', 'change']) + url = 'http://fund.eastmoney.com/%s.html' % code[0] + todayvalue = efund_mail2.spider(url) + if (todayvalue != None): + MAStrategyPos = MAStrategy(code[0],todayvalue[0],df) + print str(code) + print u'-- MA 策略 --' + print MAStrategyPos.select_Time_MA() + + print u'-- MACD 策略 --' + print MAStrategyPos.select_Time_MACD() + + print u'-- DMA 策略 --' + print MAStrategyPos.select_Time_DMA() + + print u'-- TRIX 策略 -->' + print MAStrategyPos.select_Time_TRIX() + + print u'-- 组合策略 --' + print MAStrategyPos.select_Time_Mix() + + print u'-- AMA策略 --' + print MAStrategyPos.select_Time_AMA() + + print '\n' + print "main end" + +def macd_live_main(code): + code = [['002963', 'egold'], ['003321', 'eoil'], ['004744', 'eGEI'], ['110003', 'eSSE50'], ['110020', 'HS300'], + ['110031', 'eHSI'], ['161130', 'eNASDAQ100'], ['110028', 'anxinB'], ['110022', 'eConsumption '], + ['161125', 'SPX500']] + buysell = [] + for i in code: + # save(strfundcode=i ,numdays=365*1) + macd_live(i) + +if __name__ == '__main__': + macd_live_main(1) diff --git a/trade_process/strategy/macd_live_test.pyc b/trade_process/strategy/macd_live_test.pyc new file mode 100644 index 0000000..cf011a8 Binary files /dev/null and b/trade_process/strategy/macd_live_test.pyc differ diff --git a/trade_process/strategy/stop_loss.py b/trade_process/strategy/stop_loss.py index 4ec1ce2..af0c886 100644 --- a/trade_process/strategy/stop_loss.py +++ b/trade_process/strategy/stop_loss.py @@ -1,366 +1,366 @@ -#coding: utf-8 - -""" -止盈止损策略 -""" - -import pandas as pd -import copy -from operator import itemgetter - -from init import engine, engine_test -from util.codeConvert import GetNowDate -from data_process.online_data import get_real_price_dataframe - -#限价止损 -def stop_loss_by_price(): - """ - 限价止损 - :return: - """ - #查询持仓股票 - symbolAccount = SymbolAccount() - symbolAccount.init_position_from_db() - - threshold_profit_open = 0.08 #止盈开启阈值 - threshold_profit_pretect = 0.02 #止盈保护阈值 - threshold_loss_pretect = 0.1 #止损保护阈值 - - judgements = [] - - #计算持仓股票的最初价格 - for symbol in symbolAccount.avail_secpos.keys(): - orders = symbolAccount.order_history[symbol] - if len(orders) == 0: - continue - - trade_date_begin = orders[0][0] - - #止盈保护 - close_prices = get_close_prices(symbol, trade_date_begin[:10], GetNowDate()) - if len(close_prices) < 1: - continue - - close_price_begin = close_prices[0] - realtime_price = get_realtime_price(symbol) - if realtime_price == -1: - continue - - if max(close_prices) >= (1 + threshold_profit_open)*close_price_begin: #止盈策略启动 - max_price = max(close_prices) - if realtime_price <= (1 - threshold_profit_pretect) * max_price: - #符合止盈策略,卖出 - reason = '符合止盈策略:相比最高价,下跌了{:.2}%'.format((max_price - realtime_price) / max_price * 100) - symbolAccount.order(symbol, symbolAccount.avail_secpos[symbol], realtime_price, GetNowDate()) - judgements.append((symbol, -1, reason)) - - elif realtime_price <= (1 - threshold_loss_pretect) * close_price_begin: - #符合止损策略,卖出 - down = (close_price_begin-realtime_price)/close_price_begin*100 - reason = '符合止损策略:相比成本价,下跌了{:.2f}%'.format(down) - symbolAccount.order(symbol, symbolAccount.avail_secpos[symbol], realtime_price, GetNowDate()) - judgements.append((symbol, -1, reason)) - - return judgements - - - - - - -class SymbolAccount(): - """ - 股票账户类 - """ - - def __init__(self, account=100000): - self.account = account #初始资金 - self.cash = self.account - self.avail_secpos = {} # 字典，键为证券代码，值为持有的证券数量, {'000001': 100, '600000': 100} - self.order_history = {} #dict, {symbol:000001, [(order_time:2016-07-11, , order_amount:100, order_price)]} , order_amount为正时买入,为负时卖出 - self.position_history = [] # 持仓历史[2016-07-11, cash, {'600000':100, '600031':300}] - - def init_position_from_db(self): - """ - 从数据库初始化持仓情况 - :return: - """ - sql = "SELECT * FROM classifier_db.trade_order order by trade_time asc" - df = pd.read_sql(sql, engine_test) - #剩余现金 - if len(df): - self.cash = df['cash'].get_values()[-1] - - for code in df['code'].drop_duplicates(): - df_code = df[df['code'] == code] - self.avail_secpos[code] = df_code['trade_count'].sum() - - self.order_history.setdefault(code, []) - for _, row in df_code.iterrows(): - self.order_history[code].append([str(row['trade_time']), row['trade_count'], row['trade_price']]) - - - - - - def order(self, smybol, amount, price, trade_date): - """ - - Parameters - ---------- - smybol: 股票代码 - amount: 数量, 正数为买入,负数为卖出 - price: 下单价格 - trade_date: 下单日期 - - Returns: True,成功; False,失败 - ------- - - """ - self.avail_secpos.setdefault(smybol, 0) - self.order_history.setdefault(smybol, []) - - if amount > 0: # 买入 - if amount * price < self.cash: - self.cash -= amount * price - self.avail_secpos[smybol] += amount - self.order_history[smybol].append([str(trade_date)[:10], amount, price]) - else: - return False - - else: #卖出 - if abs(amount) <= self.avail_secpos[smybol]: - self.cash += abs(amount) * price - self.avail_secpos[smybol] += amount - self.order_history[smybol].append([str(trade_date)[:10],amount, price]) - - for smybol in self.avail_secpos.keys(): - if self.avail_secpos[smybol] == 0: #删除持仓为0的股票 - del self.avail_secpos[smybol] - - self.position_history.append([trade_date, self.cash, copy.deepcopy(self.avail_secpos)]) - - - # def order_pct(self, smybol, pct, price): - # """ - # - # Parameters - # ---------- - # smybol: 股票代码 - # pct: 买入比例 - # price: 买入价格 - # - # Returns - # ------- - # - # """ - # pass - - - def get_current_account(self, current_date): - """ - 获取当前时间的持仓市值 - Parameters - ---------- - current_date: 当前日期 - - Returns - ------- - - """ - - smybol_market_values = 0 - for symbol in self.avail_secpos.keys(): - - # 获取股票价格 - close_hfq = get_close_price(symbol, current_date) - if close_hfq == -1: - msg = "{},{},get code close_price error!".format(symbol, current_date) - # raise Exception(msg) - print msg - return 0 - - smybol_market_values += close_hfq * self.avail_secpos[symbol] - - return smybol_market_values + self.cash - - def get_sorted_symbols_by_return_rate(self, trade_date): - """ - 持仓股票按收益率排序 - Returns - ------- - - """ - smybol_rate_list = [] - for smybol in self.order_history.keys(): - #单只股票持仓成本 - total_count = 0 - total_cost = 0.0 - for one_order in self.order_history[smybol]: - total_count += one_order[1] - total_cost += one_order[1] * one_order[2] - if total_count == 0: - continue - - aver_price = total_cost/total_count #平均持仓成本 - now_price = get_close_price(smybol, trade_date) - smybol_rate_list.append((smybol, (now_price-aver_price)/aver_price)) - - smybol_rate_list = sorted(smybol_rate_list, key=itemgetter(1), reverse=True) - return smybol_rate_list - - def get_all_order_history_by_date(self): - orders = [] - for smybol in self.order_history.keys(): - smybol_orders = self.order_history[smybol] - smybol_orders = [[smybol] +one for one in smybol_orders] - orders.extend(smybol_orders) - orders = sorted(orders, key =lambda x : x[1]) - print orders[:3] - print orders[-3:] - return orders - - def calcute_maximun_drawdown(self): - """ - 计算最大回撤 - Returns - ------- - """ - - jingzhi_list = self.get_jingzhi_daliy() - - if len(jingzhi_list) >= 2: - jingzhi_list_sorted = sorted(jingzhi_list, key=lambda x : x[1], reverse=True) - max_vale = jingzhi_list_sorted[0][1] - max_value_date = jingzhi_list_sorted[0][0] - jingzhi_list_after = [jingzhi for jingzhi in jingzhi_list if jingzhi[0] > max_value_date] - jingzhi_list_sorted = sorted(jingzhi_list, key=lambda x : x[1]) - min_value = jingzhi_list_sorted[0][1] - - max_drawdown = (max_vale-min_value)/max_vale * 100.0 - return max_drawdown - - return 0 - - def get_jingzhi_daliy(self): - """ - 获取每日净值 - Returns - ------- - """ - if len(self.position_history) < 2: - return [] - - # 持仓起止日期范围 - rng = pd.date_range(self.position_history[0][0], self.position_history[-1][0], freq='D') - jingzhi_list = [] - - for trade_date in rng: - # 获取现金,持仓股票及数量 - position = self.get_position_by_trade_date(trade_date) - if not position: - continue - - # 此刻的持仓成本 - cash = position[1] # cash - avail_secpos = position[2] - is_trade_date = True - - for smybol in avail_secpos.keys(): - close_price = get_close_price(smybol, trade_date) - if close_price == -1: - is_trade_date = False - break - cash += avail_secpos[smybol] * close_price - - if not is_trade_date: - continue - - jingzhi_list.append([trade_date, cash * 1.0 / self.account]) - - return jingzhi_list - - def get_position_by_trade_date(self, trade_date): - """ - 获取持仓股票及数量 - Parameters - ---------- - trade_date - - Returns - ------- - """ - for i in range(1,len(self.position_history)): - # print type(trade_date.to_datetime()), str(position[0]) - if str(trade_date.to_datetime()) >= str(self.position_history[i-1][0]) and str(trade_date.to_datetime()) < str(self.position_history[i][0]): - return self.position_history[i-1] - - # if len(self.position_history): - # return self.position_history[-1] - return None - -def get_close_price(symbol, trade_date): - """ - 获取后复权价格 - Parameters - ---------- - symbol - trade_date - - Returns - ------- - - """ - if len(symbol) == 6: - sql = "SELECT close FROM hq_db.stock_kline_fq where code='{}' and date>='{}' order by date asc".format(symbol, trade_date) - else: - sql = "SELECT close FROM hq_db.stock_kline_fq where code='{}' and date>='{}' order by date asc".format( - symbol, trade_date) - df = pd.read_sql(sql, engine) - closes = df['close'].get_values() - if len(closes) == 0: - # raise Exception("get code close_price error!") - return -1 - - return closes[0] - - -def get_close_prices(symbol, trade_date_begin, trade_date_end): - - """ - 获取前复权价格序列 - :param symbol: - :param trade_date_begin: - :param trade_date_end: - :return: - """ - - if len(symbol) == 6: - sql = "SELECT date, close FROM hq_db.stock_kline_fq where code='{}' and date>='{}' and date <='{}' order by date asc".format( - symbol, trade_date_begin, trade_date_end) - else: - sql = "SELECT date, close FROM hq_db.stock_kline_fq where code='{}' and date>='{}' and date <='{}' order by date asc".format( - symbol, trade_date_begin, trade_date_end) - df = pd.read_sql(sql, engine) - closes = df['close'].get_values() - - return closes - -def get_realtime_price(symbol): - """ - 获取实时股价 - :param symbol: - :return: - """ - try: - df = get_real_price_dataframe() - df_s = df[df['code'] == symbol] - if len(df_s['trade'].get_values()): - return df_s['trade'].get_values()[0] - else: - return -1 - except: - return -1 - -if __name__ == "__main__": +#coding: utf-8 + +""" +止盈止损策略 +""" + +import pandas as pd +import copy +from operator import itemgetter + +# from init import engine, engine_test +from util.codeConvert import GetNowDate +from data_process.online_data import get_real_price_dataframe + +#限价止损 +def stop_loss_by_price(): + """ + 限价止损 + :return: + """ + #查询持仓股票 + symbolAccount = SymbolAccount() + symbolAccount.init_position_from_db() + + threshold_profit_open = 0.08 #止盈开启阈值 + threshold_profit_pretect = 0.02 #止盈保护阈值 + threshold_loss_pretect = 0.1 #止损保护阈值 + + judgements = [] + + #计算持仓股票的最初价格 + for symbol in symbolAccount.avail_secpos.keys(): + orders = symbolAccount.order_history[symbol] + if len(orders) == 0: + continue + + trade_date_begin = orders[0][0] + + #止盈保护 + close_prices = get_close_prices(symbol, trade_date_begin[:10], GetNowDate()) + if len(close_prices) < 1: + continue + + close_price_begin = close_prices[0] + realtime_price = get_realtime_price(symbol) + if realtime_price == -1: + continue + + if max(close_prices) >= (1 + threshold_profit_open)*close_price_begin: #止盈策略启动 + max_price = max(close_prices) + if realtime_price <= (1 - threshold_profit_pretect) * max_price: + #符合止盈策略,卖出 + reason = '符合止盈策略:相比最高价,下跌了{:.2}%'.format((max_price - realtime_price) / max_price * 100) + symbolAccount.order(symbol, symbolAccount.avail_secpos[symbol], realtime_price, GetNowDate()) + judgements.append((symbol, -1, reason)) + + elif realtime_price <= (1 - threshold_loss_pretect) * close_price_begin: + #符合止损策略,卖出 + down = (close_price_begin-realtime_price)/close_price_begin*100 + reason = '符合止损策略:相比成本价,下跌了{:.2f}%'.format(down) + symbolAccount.order(symbol, symbolAccount.avail_secpos[symbol], realtime_price, GetNowDate()) + judgements.append((symbol, -1, reason)) + + return judgements + + + + + + +class SymbolAccount(): + """ + 股票账户类 + """ + + def __init__(self, account=100000): + self.account = account #初始资金 + self.cash = self.account + self.avail_secpos = {} # 字典，键为证券代码，值为持有的证券数量, {'000001': 100, '600000': 100} + self.order_history = {} #dict, {symbol:000001, [(order_time:2016-07-11, , order_amount:100, order_price)]} , order_amount为正时买入,为负时卖出 + self.position_history = [] # 持仓历史[2016-07-11, cash, {'600000':100, '600031':300}] + + def init_position_from_db(self): + """ + 从数据库初始化持仓情况 + :return: + """ + sql = "SELECT * FROM classifier_db.trade_order order by trade_time asc" + df = pd.read_sql(sql, engine_test) + #剩余现金 + if len(df): + self.cash = df['cash'].get_values()[-1] + + for code in df['code'].drop_duplicates(): + df_code = df[df['code'] == code] + self.avail_secpos[code] = df_code['trade_count'].sum() + + self.order_history.setdefault(code, []) + for _, row in df_code.iterrows(): + self.order_history[code].append([str(row['trade_time']), row['trade_count'], row['trade_price']]) + + + + + + def order(self, smybol, amount, price, trade_date): + """ + + Parameters + ---------- + smybol: 股票代码 + amount: 数量, 正数为买入,负数为卖出 + price: 下单价格 + trade_date: 下单日期 + + Returns: True,成功; False,失败 + ------- + + """ + self.avail_secpos.setdefault(smybol, 0) + self.order_history.setdefault(smybol, []) + + if amount > 0: # 买入 + if amount * price < self.cash: + self.cash -= amount * price + self.avail_secpos[smybol] += amount + self.order_history[smybol].append([str(trade_date)[:10], amount, price]) + else: + return False + + else: #卖出 + if abs(amount) <= self.avail_secpos[smybol]: + self.cash += abs(amount) * price + self.avail_secpos[smybol] += amount + self.order_history[smybol].append([str(trade_date)[:10],amount, price]) + + for smybol in self.avail_secpos.keys(): + if self.avail_secpos[smybol] == 0: #删除持仓为0的股票 + del self.avail_secpos[smybol] + + self.position_history.append([trade_date, self.cash, copy.deepcopy(self.avail_secpos)]) + + + # def order_pct(self, smybol, pct, price): + # """ + # + # Parameters + # ---------- + # smybol: 股票代码 + # pct: 买入比例 + # price: 买入价格 + # + # Returns + # ------- + # + # """ + # pass + + + def get_current_account(self, current_date): + """ + 获取当前时间的持仓市值 + Parameters + ---------- + current_date: 当前日期 + + Returns + ------- + + """ + + smybol_market_values = 0 + for symbol in self.avail_secpos.keys(): + + # 获取股票价格 + close_hfq = get_close_price(symbol, current_date) + if close_hfq == -1: + msg = "{},{},get code close_price error!".format(symbol, current_date) + # raise Exception(msg) + print msg + return 0 + + smybol_market_values += close_hfq * self.avail_secpos[symbol] + + return smybol_market_values + self.cash + + def get_sorted_symbols_by_return_rate(self, trade_date): + """ + 持仓股票按收益率排序 + Returns + ------- + + """ + smybol_rate_list = [] + for smybol in self.order_history.keys(): + #单只股票持仓成本 + total_count = 0 + total_cost = 0.0 + for one_order in self.order_history[smybol]: + total_count += one_order[1] + total_cost += one_order[1] * one_order[2] + if total_count == 0: + continue + + aver_price = total_cost/total_count #平均持仓成本 + now_price = get_close_price(smybol, trade_date) + smybol_rate_list.append((smybol, (now_price-aver_price)/aver_price)) + + smybol_rate_list = sorted(smybol_rate_list, key=itemgetter(1), reverse=True) + return smybol_rate_list + + def get_all_order_history_by_date(self): + orders = [] + for smybol in self.order_history.keys(): + smybol_orders = self.order_history[smybol] + smybol_orders = [[smybol] +one for one in smybol_orders] + orders.extend(smybol_orders) + orders = sorted(orders, key =lambda x : x[1]) + print orders[:3] + print orders[-3:] + return orders + + def calcute_maximun_drawdown(self): + """ + 计算最大回撤 + Returns + ------- + """ + + jingzhi_list = self.get_jingzhi_daliy() + + if len(jingzhi_list) >= 2: + jingzhi_list_sorted = sorted(jingzhi_list, key=lambda x : x[1], reverse=True) + max_vale = jingzhi_list_sorted[0][1] + max_value_date = jingzhi_list_sorted[0][0] + jingzhi_list_after = [jingzhi for jingzhi in jingzhi_list if jingzhi[0] > max_value_date] + jingzhi_list_sorted = sorted(jingzhi_list, key=lambda x : x[1]) + min_value = jingzhi_list_sorted[0][1] + + max_drawdown = (max_vale-min_value)/max_vale * 100.0 + return max_drawdown + + return 0 + + def get_jingzhi_daliy(self): + """ + 获取每日净值 + Returns + ------- + """ + if len(self.position_history) < 2: + return [] + + # 持仓起止日期范围 + rng = pd.date_range(self.position_history[0][0], self.position_history[-1][0], freq='D') + jingzhi_list = [] + + for trade_date in rng: + # 获取现金,持仓股票及数量 + position = self.get_position_by_trade_date(trade_date) + if not position: + continue + + # 此刻的持仓成本 + cash = position[1] # cash + avail_secpos = position[2] + is_trade_date = True + + for smybol in avail_secpos.keys(): + close_price = get_close_price(smybol, trade_date) + if close_price == -1: + is_trade_date = False + break + cash += avail_secpos[smybol] * close_price + + if not is_trade_date: + continue + + jingzhi_list.append([trade_date, cash * 1.0 / self.account]) + + return jingzhi_list + + def get_position_by_trade_date(self, trade_date): + """ + 获取持仓股票及数量 + Parameters + ---------- + trade_date + + Returns + ------- + """ + for i in range(1,len(self.position_history)): + # print type(trade_date.to_datetime()), str(position[0]) + if str(trade_date.to_datetime()) >= str(self.position_history[i-1][0]) and str(trade_date.to_datetime()) < str(self.position_history[i][0]): + return self.position_history[i-1] + + # if len(self.position_history): + # return self.position_history[-1] + return None + +def get_close_price(symbol, trade_date): + """ + 获取后复权价格 + Parameters + ---------- + symbol + trade_date + + Returns + ------- + + """ + if len(symbol) == 6: + sql = "SELECT close FROM hq_db.stock_kline_fq where code='{}' and date>='{}' order by date asc".format(symbol, trade_date) + else: + sql = "SELECT close FROM hq_db.stock_kline_fq where code='{}' and date>='{}' order by date asc".format( + symbol, trade_date) + df = pd.read_sql(sql, engine) + closes = df['close'].get_values() + if len(closes) == 0: + # raise Exception("get code close_price error!") + return -1 + + return closes[0] + + +def get_close_prices(symbol, trade_date_begin, trade_date_end): + + """ + 获取前复权价格序列 + :param symbol: + :param trade_date_begin: + :param trade_date_end: + :return: + """ + + if len(symbol) == 6: + sql = "SELECT date, close FROM hq_db.stock_kline_fq where code='{}' and date>='{}' and date <='{}' order by date asc".format( + symbol, trade_date_begin, trade_date_end) + else: + sql = "SELECT date, close FROM hq_db.stock_kline_fq where code='{}' and date>='{}' and date <='{}' order by date asc".format( + symbol, trade_date_begin, trade_date_end) + df = pd.read_sql(sql, engine) + closes = df['close'].get_values() + + return closes + +def get_realtime_price(symbol): + """ + 获取实时股价 + :param symbol: + :return: + """ + try: + df = get_real_price_dataframe() + df_s = df[df['code'] == symbol] + if len(df_s['trade'].get_values()): + return df_s['trade'].get_values()[0] + else: + return -1 + except: + return -1 + +if __name__ == "__main__": stop_loss_by_price() \ No newline at end of file diff --git a/trade_process/strategy/stop_loss.pyc b/trade_process/strategy/stop_loss.pyc new file mode 100644 index 0000000..094657f Binary files /dev/null and b/trade_process/strategy/stop_loss.pyc differ diff --git a/trade_process/strategy/strategy_macd.py b/trade_process/strategy/strategy_macd.py index 56deb28..c4d43b5 100644 --- a/trade_process/strategy/strategy_macd.py +++ b/trade_process/strategy/strategy_macd.py @@ -1,66 +1,66 @@ -#!/usr/local/bin/python -#coding=utf-8 - -import os - -import matplotlib.pyplot as plt - -from trade_process.strategy import macd_back_test -import data_process.native_data as native_data - - -def strategy_macd(): - #读取历史数据 - - # 计算EMA（12日，26日）, DIF, DEA, MACD - downloadDir = os.path.pardir + '\\stockdata' - downloadDirNew = os.getcwd() + '\\stockdata_macd' - if os.path.exists(downloadDir) == False: - os.makedirs(downloadDir) - if os.path.exists(downloadDirNew) == False: - os.makedirs(downloadDirNew) - - #遍历文件夹下的所有csv数据 - for root, dirs, files in os.walk(downloadDir): - for name in files: - stockCsvPath = root + '\\' + name - if ".csv" in stockCsvPath: - basename = os.path.basename(stockCsvPath) - stock_code, exp = os.path.splitext(basename) - #macd_back_test.run(stockCsvPath, stock_code) - - each_stock_strategy(stockCsvPath, stock_code) - #break - -def each_stock_strategy(stockCsvPath, stock_code): - plt.figure() - - r = native_data.getCsvDataByFullPath(stockCsvPath) - dif_price, dea_price, macd_price = macd_back_test.getMAStrategy(stockCsvPath, stock_code, 'MACD') - ama_price = macd_back_test.getMAStrategy(stockCsvPath, stock_code, 'AMA') - macd_back_test.getMAStrategy(stockCsvPath, stock_code, 'MA') - macd_back_test.getMAStrategy(stockCsvPath, stock_code, 'DMA') - macd_back_test.getMAStrategy(stockCsvPath, stock_code, 'MACD') - macd_back_test.getMAStrategy(stockCsvPath, stock_code, 'TRIX') - - #plot.plotClosePrice(plt, str(stock_code), stock_code, root) - #plot.plotData(stockCsvPath + '\\' +name, name) -# plt.plot(range(len(r.adj_close)), r.adj_close, 'black', label="Close") -# plt.plot(range(len(dif_price)), dif_price, 'red', label='DIF') -# plt.plot(range(len(dea_price)), dea_price, 'blue', label='DEA') -# plt.plot(range(len(macd_price)), macd_price, 'cyan', label='MACD') -# plt.plot(range(len(ama_price)), ama_price, 'green', label='AMA') -# plt.title(stock_code) -# plt.legend() -# plt.grid() -# plt.show() - -# 实时策略 -def live_stock_strategy(stockCsvPath, stock_code): - r = native_data.getCsvDataByFullPath(stockCsvPath) - dif_price, dea_price, macd_price = macd_back_test.getMAStrategy(stockCsvPath, stock_code, 'MACD') - ama_price = macd_back_test.getMAStrategy(stockCsvPath, stock_code, 'AMA') - -if __name__ == "__main__": - strategy_macd() +#!/usr/local/bin/python +#coding=utf-8 + +import os + +import matplotlib.pyplot as plt + +from trade_process.strategy import macd_back_test +import data_process.native_data as native_data + + +def strategy_macd(): + #读取历史数据 + + # 计算EMA（12日，26日）, DIF, DEA, MACD + downloadDir = os.path.pardir + '\\stockdata' + downloadDirNew = os.getcwd() + '\\stockdata_macd' + if os.path.exists(downloadDir) == False: + os.makedirs(downloadDir) + if os.path.exists(downloadDirNew) == False: + os.makedirs(downloadDirNew) + + #遍历文件夹下的所有csv数据 + for root, dirs, files in os.walk(downloadDir): + for name in files: + stockCsvPath = root + '\\' + name + if ".csv" in stockCsvPath: + basename = os.path.basename(stockCsvPath) + stock_code, exp = os.path.splitext(basename) + #macd_back_test.run(stockCsvPath, stock_code) + + each_stock_strategy(stockCsvPath, stock_code) + #break + +def each_stock_strategy(stockCsvPath, stock_code): + plt.figure() + + r = native_data.getCsvDataByFullPath(stockCsvPath) + dif_price, dea_price, macd_price = macd_back_test.getMAStrategy(stockCsvPath, stock_code, 'MACD') + ama_price = macd_back_test.getMAStrategy(stockCsvPath, stock_code, 'AMA') + macd_back_test.getMAStrategy(stockCsvPath, stock_code, 'MA') + macd_back_test.getMAStrategy(stockCsvPath, stock_code, 'DMA') + macd_back_test.getMAStrategy(stockCsvPath, stock_code, 'MACD') + macd_back_test.getMAStrategy(stockCsvPath, stock_code, 'TRIX') + + #plot.plotClosePrice(plt, str(stock_code), stock_code, root) + #plot.plotData(stockCsvPath + '\\' +name, name) +# plt.plot(range(len(r.adj_close)), r.adj_close, 'black', label="Close") +# plt.plot(range(len(dif_price)), dif_price, 'red', label='DIF') +# plt.plot(range(len(dea_price)), dea_price, 'blue', label='DEA') +# plt.plot(range(len(macd_price)), macd_price, 'cyan', label='MACD') +# plt.plot(range(len(ama_price)), ama_price, 'green', label='AMA') +# plt.title(stock_code) +# plt.legend() +# plt.grid() +# plt.show() + +# 实时策略 +def live_stock_strategy(stockCsvPath, stock_code): + r = native_data.getCsvDataByFullPath(stockCsvPath) + dif_price, dea_price, macd_price = macd_back_test.getMAStrategy(stockCsvPath, stock_code, 'MACD') + ama_price = macd_back_test.getMAStrategy(stockCsvPath, stock_code, 'AMA') + +if __name__ == "__main__": + strategy_macd() #each_stock_strategy("..\\stockdata\\600000.csv", '399001') \ No newline at end of file diff --git a/trade_process/strategy/strategy_macd.pyc b/trade_process/strategy/strategy_macd.pyc new file mode 100644 index 0000000..25a9fb1 Binary files /dev/null and b/trade_process/strategy/strategy_macd.pyc differ diff --git a/trade_process/strategy/test.py b/trade_process/strategy/test.py index 3c55bf3..144c921 100644 --- a/trade_process/strategy/test.py +++ b/trade_process/strategy/test.py @@ -1,108 +1,108 @@ -#!/usr/local/bin/python -#coding=utf-8 - -from data_process.data_get import * -import pandas.io.data as web -import re, urllib, urllib2, cookielib - -def find_stock_by(code): - #print 'find begin' - code = getSixDigitalStockCode(code) - # 取六月以来的股价 - df = get_stock_k_line(code, date_start='2015-06-01') - close_prices = df[cm.KEY_CLOSE].get_values() - dates = df[cm.KEY_DATE].get_values() - if len(dates) == 0: - return - now_date = dates[-1][:10] - if now_date != '2015-07-10': - return - - max_price = close_prices.max() - now_price = close_prices[-1] - ratio = (max_price - now_price) / max_price - if ratio >= 0.6 and ratio < 0.: - print code, '相比六月以来最大值降幅 :%f' % (ratio * 100 ),'%' - -def getHtml(url): - page = urllib.urlopen(url) - html = page.read() - html = html.decode('GBK') - return html - -def getImg(html): - reg = r'"(http://quote.eastmoney.com/(sh|sz)[360].+html)">(.+)\(([0-9]{6})\)
    • ' - imgre = re.compile(reg) - imgList = re.findall(imgre, html) - - x = 0 - for imgurl in imgList: - print imgurl - #urllib.urlretrieve(imgurl, '%s.jpg' % x) - x = x + 1 - return len(imgList) - -if __name__ == "__main__": - -# codes = get_stock_codes() -# for code in codes: -# find_stock_by(code) -# some_text = 'alpha , beta ,,,gamma delta ' -# print re.split('[,]', some_text) -# -# pat = '[a-zA-Z]+' -# text = '"Hm...err -- are you sure?" he said, sounding insecure.' -# print re.findall(pat, text) -# -# pat = '{name}' -# text = 'Dear {name}...' -# print re.sub(pat, 'Mr. Gumby', text) -# -# print re.escape('www.python.org') -# print re.escape('but where is the ambiguity?') -# -# m = re.match(r'www\.(.*)\..{3}', 'www.python.org') -# print m.group(1) -# print m.start(1), m.end(1), m.span(1) - - #print getImg(getHtml('http://quote.eastmoney.com/stocklist.html')) - values = {"username":"cbbing", "password":"xx"} - data = urllib.urlencode(values) - url = "https://passport.csdn.net/account/login?from=http://my.csdn.net/my/mycsdn" - user_agent = 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.132 Safari/537.36' - headers = {'User-Agent':user_agent} - - geturl = url+"?"+data - print geturl - - request = urllib2.Request(url, data, headers) - response = urllib2.urlopen(request) - #print response.read() - - cookie = cookielib.CookieJar() - handler = urllib2.HTTPCookieProcessor(cookie) - opener = urllib2.build_opener(handler) - response = opener.open("http://www.baidu.com") - for item in cookie: - print 'Name= ' + item.name - print 'Value= ' + item.value - -# filename='cookie.txt' -# cookie = cookielib.MozillaCookieJar(filename) -# handler = urllib2.HTTPCookieProcessor(cookie) -# opener = urllib2.build_opener(handler) -# response = opener.open("http://www.baidu.com") -# cookie.save(ignore_discard=True, ignore_expires=True) - - cookie = cookielib.MozillaCookieJar() - cookie.load('cookie.txt', ignore_discard=True, ignore_expires=True) - req = urllib2.Request("http://www.baidu.com") - opener = urllib2.build_opener(urllib2.HTTPCookieProcessor(cookie)) - response = opener.open(req) - print response.read() - - - - - +#!/usr/local/bin/python +#coding=utf-8 + +from data_process.data_get import * +import pandas.io.data as web +import re, urllib, urllib2, cookielib + +def find_stock_by(code): + #print 'find begin' + code = getSixDigitalStockCode(code) + # 取六月以来的股价 + df = get_stock_k_line(code, date_start='2015-06-01') + close_prices = df[cm.KEY_CLOSE].get_values() + dates = df[cm.KEY_DATE].get_values() + if len(dates) == 0: + return + now_date = dates[-1][:10] + if now_date != '2015-07-10': + return + + max_price = close_prices.max() + now_price = close_prices[-1] + ratio = (max_price - now_price) / max_price + if ratio >= 0.6 and ratio < 0.: + print code, '相比六月以来最大值降幅 :%f' % (ratio * 100 ),'%' + +def getHtml(url): + page = urllib.urlopen(url) + html = page.read() + html = html.decode('GBK') + return html + +def getImg(html): + reg = r'"(http://quote.eastmoney.com/(sh|sz)[360].+html)">(.+)\(([0-9]{6})\)' + imgre = re.compile(reg) + imgList = re.findall(imgre, html) + + x = 0 + for imgurl in imgList: + print imgurl + #urllib.urlretrieve(imgurl, '%s.jpg' % x) + x = x + 1 + return len(imgList) + +if __name__ == "__main__": + +# codes = get_stock_codes() +# for code in codes: +# find_stock_by(code) +# some_text = 'alpha , beta ,,,gamma delta ' +# print re.split('[,]', some_text) +# +# pat = '[a-zA-Z]+' +# text = '"Hm...err -- are you sure?" he said, sounding insecure.' +# print re.findall(pat, text) +# +# pat = '{name}' +# text = 'Dear {name}...' +# print re.sub(pat, 'Mr. Gumby', text) +# +# print re.escape('www.python.org') +# print re.escape('but where is the ambiguity?') +# +# m = re.match(r'www\.(.*)\..{3}', 'www.python.org') +# print m.group(1) +# print m.start(1), m.end(1), m.span(1) + + #print getImg(getHtml('http://quote.eastmoney.com/stocklist.html')) + values = {"username":"cbbing", "password":"xx"} + data = urllib.urlencode(values) + url = "https://passport.csdn.net/account/login?from=http://my.csdn.net/my/mycsdn" + user_agent = 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.132 Safari/537.36' + headers = {'User-Agent':user_agent} + + geturl = url+"?"+data + print geturl + + request = urllib2.Request(url, data, headers) + response = urllib2.urlopen(request) + #print response.read() + + cookie = cookielib.CookieJar() + handler = urllib2.HTTPCookieProcessor(cookie) + opener = urllib2.build_opener(handler) + response = opener.open("http://www.baidu.com") + for item in cookie: + print 'Name= ' + item.name + print 'Value= ' + item.value + +# filename='cookie.txt' +# cookie = cookielib.MozillaCookieJar(filename) +# handler = urllib2.HTTPCookieProcessor(cookie) +# opener = urllib2.build_opener(handler) +# response = opener.open("http://www.baidu.com") +# cookie.save(ignore_discard=True, ignore_expires=True) + + cookie = cookielib.MozillaCookieJar() + cookie.load('cookie.txt', ignore_discard=True, ignore_expires=True) + req = urllib2.Request("http://www.baidu.com") + opener = urllib2.build_opener(urllib2.HTTPCookieProcessor(cookie)) + response = opener.open(req) + print response.read() + + + + + \ No newline at end of file diff --git a/trade_process/strategy/tread_tracking.py b/trade_process/strategy/tread_tracking.py index e42c720..735fbe2 100644 --- a/trade_process/strategy/tread_tracking.py +++ b/trade_process/strategy/tread_tracking.py @@ -1,542 +1,586 @@ -#!/usr/local/bin/python -#coding=utf-8 -# 趋势追踪策略 -""" -http://t.cn/RqQv0JW -""" - -import pandas as pd -import matplotlib.pyplot as plt -import numpy as np -from data_process.data_get import get_stock_k_line -from tushare.util import dateu as du -import datetime -import tushare as ts -import cwavelet - -#参数 -prama_ma_short = 12 # 短均线 -prama_ma_long = 40 # 长均线 -filter_range = 0.004 # 滤波区间 -drift = 0.0015 #漂移项 -param_big_band = 0.02 #大波段判断条件 -protect_big_band = 0.002 # 大波段保护止赢点 - -SIGNAL_BUY = 1 #买 -SIGNAL_SALE = -1 #卖 -SIGNAL_DEFAULT = 0 - -# 趋势追踪实时交易 -# 参数 -# code：股票代码 -# df: 个股的K线数据 -# price_now:实时股价 -def tread_track_live_trading(code, price_now, df=None): - - # 分析某个时间段的股票 - #dateS = datetime.datetime.today().date() + datetime.timedelta(-100) - #date_start = dateS.strftime("%Y-%m-%d") - #df = df[df.index > date_start] - - try: - df = get_stock_k_line(code) - #df = df.reindex(df.index[::-1]) - except Exception as e: - print str(e) - return - #print df - - close_price = df['close'].get_values() - close_price = np.append(close_price, float(price_now)) - ma_short = pd.rolling_mean(close_price, prama_ma_short) - ma_long = pd.rolling_mean(close_price, prama_ma_long) - #print ma_short - - - signal = SIGNAL_SALE - - print '交易开始' - - plt.figure(figsize=(16,8)) - #plt.plot(range(len(ma_short)), ma_short.get_values(), label=code, color="b", linewidth=1) - - plt.xlabel("Time") - plt.ylabel("Price") - - # 过滤微小波动 - extreIndex = -1 #极值点的索引 - for i in range(prama_ma_short+1, len(ma_short)-1): - bMax = ma_short[i] > ma_short[i-1] and ma_short[i] > ma_short[i+1] # 极大值的条件 - bMin = ma_short[i] < ma_short[i-1] and ma_short[i] < ma_short[i+1] # 极小值的条件 - if bMax or bMin: - extreIndex = i - elif extreIndex > 0: - if ma_short[i] > ma_short[extreIndex]*(1-filter_range) and \ - ma_short[i] < ma_short[extreIndex]*(1+filter_range): - ma_short[i] = ma_short[extreIndex] - #print i,ma_short[i] - - #plt.plot(range(len(ma_short.get_values())), ma_short.get_values(), label=code, color="r", linewidth=1) - - # 交易次数 - count_sale = 0 - count_buy = 0 - - min_index_pre = 0 #前一个极小值 - max_index_pre = 0 #前一个极大值 - - # 止损位 - keep_stop_price = 0 - keep_stop_index = 0 - - # 止赢位 - keep_win_price = 0 - keep_win_index = 0 - - total = 0 - price_buy = 0 - price_init = 0 - money_init = 50000 - stock_money = money_init - stock_count = 0 - - - #for i in range(prama_ma_short+1, len(ma_short)-1): - for i in range(len(ma_short)-30, len(ma_short)-1): - - #滤波后的均线走平时（即处于滤波区间内），将其识别为一个点 - index_post = i+1 - - try: - while(ma_short[index_post] == ma_short[i] and index_post < len(ma_short)-1): - index_post += 1 - except Exception as e: - print str(e) - - # 长均线保护策略 - bLongMA_protect_close = True - try: - bLongMA_protect_close = ma_short[i] > ma_long[i] # 长均线保护是否关闭 - except Exception as e: - print str(e) - #if bLongMA_protect_close == False: - #print "长均线保护打开", i - - # 高低点比较策略 - if ma_short[i] > ma_short[i-1] and ma_short[i] > ma_short[index_post]: - #print '极大值:', ma_short[i], 'pos:', i - if bLongMA_protect_close and max_index_pre > 0 and ma_short[i] < ma_short[max_index_pre] + drift*(i-max_index_pre) and signal == SIGNAL_BUY: - signal = SIGNAL_SALE - print '卖出:', close_price[i], 'pos:', i - count_sale += 1 - total += close_price[i] - price_buy - - stock_money += stock_count * close_price[i] - stock_count = 0 - - max_index_pre = i - elif ma_short[i] < ma_short[i-1] and ma_short[i] < ma_short[index_post]: - #print '极小值:', ma_short[i], 'pos:', i - if bLongMA_protect_close and min_index_pre > 0 and ma_short[i] > ma_short[min_index_pre] + drift*(i-min_index_pre) and signal == SIGNAL_SALE: - signal = SIGNAL_BUY - print '买入:', close_price[i], 'pos:', i - count_buy += 1 - price_buy = close_price[i] - if price_init == 0: - price_init = price_buy - - stock_count = (stock_money/100)/close_price[i]*100 - stock_money = stock_money - stock_count*close_price[i] - min_index_pre = i - - # 高低点突破策略 - # 股价突破前一个低点加漂移项，则卖出 - elif bLongMA_protect_close and signal == SIGNAL_BUY and min_index_pre > 0 and \ - ma_short[i] < ma_short[min_index_pre] + drift*(i-min_index_pre): - signal = SIGNAL_SALE - print '卖出:', close_price[i], 'pos:', i - count_sale += 1 - total += close_price[i] - price_buy - - stock_money += stock_count * close_price[i] - stock_count = 0 - - # 股价突破前一个高点加漂移项，则买入 - elif bLongMA_protect_close and signal == SIGNAL_SALE and max_index_pre > 0 and \ - ma_short[i] > ma_short[max_index_pre] + drift*(i-max_index_pre): - signal = SIGNAL_BUY - print '买入:', close_price[i], 'pos:', i - count_buy += 1 - price_buy = close_price[i] - - stock_count = (stock_money/100)/close_price[i]*100 - stock_money = stock_money - stock_count*close_price[i] - - # 大波段保护策略 - elif min_index_pre > 0 and ma_short[i] >= ma_short[min_index_pre]*(1+param_big_band): - keep_stop_price = ma_short[i] * (1-protect_big_band) #止损位 - keep_stop_index = i - elif bLongMA_protect_close and signal == SIGNAL_BUY and keep_stop_price > 0 and \ - ma_short[i] < keep_stop_price + drift*(i-keep_stop_index): - signal = SIGNAL_SALE - print '卖出:', close_price[i], 'pos:', i - count_sale += 1 - total += close_price[i] - price_buy - - stock_money += stock_count * close_price[i] - stock_count = 0 - - - - - print "buy count:", count_buy - print "sale count:", count_sale - - if stock_count > 0: - stock_money += stock_count * close_price[-1] - total += close_price[-1] - price_buy - - print "每股盈利：", total, "收益率：", (stock_money-money_init)/money_init*100,"%\n" - - print '交易结束' - - plt.grid() - #plt.legend() - #plt.show() - return (stock_money-money_init)/money_init*100 - - -# 趋势追踪回测 -# 参数 -# code：股票代码 -# df: 个股的K线数据 -def tread_track_backtest(code, df=None): - - df = get_stock_k_line(code, date_start='2015-12-30', date_end='2016-05-05') - if len(df) == 0: - return None - - print df.head(2) - # 分析某个时间段的股票 - #dateS = datetime.datetime.today().date() + datetime.timedelta(-100) - #date_start = dateS.strftime("%Y-%m-%d") - #df = df[df.index > date_start] - #print df - - # try: - # df = df.reindex(df.index[::-1]) - # except Exception as e: - # print str(e) - # return - #print df - - close_price = df['close'].get_values() - ma_short = pd.rolling_mean(df['close'], prama_ma_short) - ma_long = pd.rolling_mean(df['close'], prama_ma_long) - #print ma_short - - signal = SIGNAL_SALE - - print '交易开始' - - #plt.figure(figsize=(16,8)) - #plt.plot(range(len(ma_short)), ma_short.get_values(), label=code, color="b", linewidth=1) - - # plt.xlabel("Time") - # plt.ylabel("Price") - - - - # 判断极点 -# for i in range(prama_ma_short+1, len(ma_short)-1): -# if ma_short[i] > ma_short[i-1] and ma_short[i] > ma_short[i+1]: -# print '极大值:', ma_short[i], 'pos:', i -# -# elif ma_short[i] < ma_short[i-1] and ma_short[i] < ma_short[i+1]: -# print '极小值:', ma_short[i], 'pos:', i - - # 过滤微小波动 - extreIndex = -1 #极值点的索引 - for i in range(prama_ma_short+1, len(ma_short)-1): - bMax = ma_short[i] > ma_short[i-1] and ma_short[i] > ma_short[i+1] # 极大值的条件 - bMin = ma_short[i] < ma_short[i-1] and ma_short[i] < ma_short[i+1] # 极小值的条件 - if bMax or bMin: - extreIndex = i - elif extreIndex > 0: - if ma_short[i] > ma_short[extreIndex]*(1-filter_range) and \ - ma_short[i] < ma_short[extreIndex]*(1+filter_range): - ma_short[i] = ma_short[extreIndex] - #print i,ma_short[i] - - xticklabels = df['date'].apply(lambda x : str(x)).get_values() - # print xticklabels[:5] - # print xticklabels[-5:] - - #plt.plot(df['date'].get_values(), ma_short.get_values(), label=code, color="r", linewidth=1) - #plt.plot(df['date'].get_values(), ma_short.get_values(), color="r") - - def wavlet_plt(): - # trick to get the axes - fig,ax = plt.subplots() - - xValues = close_price[-200:] - ax.plot(xValues, label=code, color="r", linewidth=1) - zValues = cwavelet.getWaveletData(xValues, 'db2', 2, 'sqtwolog') - zxValue = np.arange(0,len(zValues),1) - #plt.figure(figsize=(16,8)) - - ax.plot(zxValue, zValues, color="b", linewidth=2) - ax.grid() - - # make ticks and tick labels - xticks=range(0, len(xValues)+1,10) - #xticklabels=['2000-01-0'+str(n) for n in range(1,len(xValues)+1)] - xticklabels = df['date'].apply(lambda x : str(x)).get_values() - - - # set ticks and tick labels - ax.set_xticks(xticks) - ax.set_xticklabels(xticklabels,rotation=15) - - #plt.legend() - plt.show() - - min_index_pre = 0 #前一个极小值 - max_index_pre = 0 #前一个极大值 - - keep_stop_up_price = 0 - keep_stop_down_price = 0 - - stockPos = StockPosition(50000, code) - - #for i in range(prama_ma_short+1, len(ma_short)-1): - for i in range(prama_ma_long-1, len(ma_short)-1): - # print prama_ma_long, len(ma_short)-1 - # print ma_short[prama_ma_long] - # print ma_long[prama_ma_long] - - #滤波后的均线走平时（即处于滤波区间内），将其识别为一个点 - index_post = i+1 - while (ma_short[index_post] == ma_short[i] and index_post < len(ma_short) - 1): - index_post += 1 - - # 长均线保护策略: - # bLongMA_forbid_buy = ma_short[i] < ma_long[i] # 买入保护 - # bLongMA_forbid_sale = not bLongMA_forbid_buy # 卖出保护 - # - # if bLongMA_forbid_buy and signal == SIGNAL_BUY: - # signal = SIGNAL_SALE - # stockPos.sale(close_price[i], str(df.ix[i, 'date'])) - # min_index_pre = 0 - # max_index_pre = 0 - # keep_stop_up_price = 0 - # keep_stop_down_price = 0 - # continue - # if bLongMA_forbid_buy and signal == SIGNAL_SALE: - # continue - - # if len(stockPos.transaction_records): # 有交易以后才考虑此情况 - # signal = SIGNAL_BUY - # stockPos.buy(close_price[i], str(df.ix[i, 'date'])) - # #print '买入:', close_price[i], ' ', str(df.ix[i, 'date']) - # continue - - # 高低点比较策略 - if ma_short[i] > ma_short[i-1] and ma_short[i] > ma_short[index_post]: # 极大值 - # <卖出> 如果当前高点比前一个高点的向下漂移项低，则空头开仓或持有空头； - # <买入> 如果当前高点比前一个高点的向上漂移项高，则多头开仓或持有多头； - if max_index_pre > 0 and i > max_index_pre: - - if signal == SIGNAL_BUY and ma_short[i] < ma_short[max_index_pre] - drift*(i-max_index_pre): - signal = SIGNAL_SALE - stockPos.sale(close_price[i], str(df.ix[i, 'date'])) - min_index_pre = 0 - max_index_pre = 0 - keep_stop_up_price = 0 - keep_stop_down_price = 0 - continue - elif signal == SIGNAL_SALE and ma_short[i] > ma_short[max_index_pre] + drift*(i-max_index_pre): - signal = SIGNAL_BUY - stockPos.buy(close_price[i], str(df.ix[i, 'date'])) - keep_stop_up_price = 0 - keep_stop_down_price = 0 - continue - - max_index_pre = i - elif ma_short[i] < ma_short[i-1] and ma_short[i] < ma_short[index_post]: # 极小值 - # < 买入 > 如果当前低点比前一个低点的向上漂移项高，则多头开仓或持有多头； - # < 卖出 > 如果当前低点比前一个低点的向下漂移项低，则空头开仓或持有空头 - if min_index_pre > 0 and i > min_index_pre: - if signal == SIGNAL_SALE and ma_short[i] > ma_short[min_index_pre] + drift*(i-min_index_pre): - signal = SIGNAL_BUY - stockPos.buy(close_price[i], str(df.ix[i, 'date'])) - keep_stop_up_price = 0 - keep_stop_down_price = 0 - continue - elif signal == SIGNAL_BUY and ma_short[i] > ma_short[min_index_pre] - drift*(i-min_index_pre): - signal = SIGNAL_SALE - stockPos.sale(close_price[i], str(df.ix[i, 'date'])) - min_index_pre = 0 - max_index_pre = 0 - keep_stop_up_price = 0 - keep_stop_down_price = 0 - continue - - min_index_pre = i - - # 高低点突破策略 - # <卖出>如果滤波后的均线比前一个低点的向下漂移项低，则空头开仓或持有空头。 - elif signal == SIGNAL_BUY and min_index_pre > 0 and ma_short[i] < ma_short[min_index_pre] - drift*(i-min_index_pre): - signal = SIGNAL_SALE - stockPos.sale(close_price[i], str(df.ix[i, 'date'])) - min_index_pre = 0 - max_index_pre = 0 - keep_stop_up_price = 0 - keep_stop_down_price = 0 - continue - - # <买入>如果滤波后的均线比前一个高点的向上漂移项高，则多头开仓或持有多头。 - elif signal == SIGNAL_SALE and max_index_pre > 0 and ma_short[i] > ma_short[max_index_pre] + drift*(i-max_index_pre): - signal = SIGNAL_BUY - stockPos.buy(close_price[i], str(df.ix[i, 'date'])) - keep_stop_up_price = 0 - keep_stop_down_price = 0 - continue - - # 大波段保护策略 - ## 向上的大波段 - if min_index_pre > 0 and close_price[i] >= ma_short[min_index_pre] * (1 + param_big_band): - if close_price[i] * (1 - protect_big_band) > keep_stop_up_price: - keep_stop_up_price = close_price[i] * (1 - protect_big_band) # 止盈位 - keep_stop_up_index = i - if signal == SIGNAL_BUY and keep_stop_up_price > 0 and \ - close_price[i] < keep_stop_up_price + drift*(i-keep_stop_up_index): - signal = SIGNAL_SALE - stockPos.sale(close_price[i], str(df.ix[i, 'date'])) - min_index_pre = 0 - max_index_pre = 0 - keep_stop_up_price = 0 - keep_stop_down_price = 0 - continue - - ## 向下的大波段 - if max_index_pre > 0 and close_price[i] <= ma_short[max_index_pre] * (1 - param_big_band): - if keep_stop_down_price == 0 or close_price[i] * (1 + protect_big_band) < keep_stop_down_price: - keep_stop_down_price = close_price[i] * (1 + protect_big_band) # 止损位 - keep_stop_down_index = i - if signal == SIGNAL_SALE and keep_stop_down_price > 0 and \ - close_price[i] > keep_stop_down_price + drift * (i + keep_stop_down_index): - - signal = SIGNAL_BUY - keep_stop_up_price = 0 - keep_stop_down_price = 0 - stockPos.buy(close_price[i], str(df.ix[i, 'date'])) - continue - - stockPos.current_price = close_price[-1] - stockPos.summary() - - print '交易结束' - - #plt.grid() - #plt.legend() - #plt.show() - return stockPos - -class StockPosition(): - - def __init__(self, cash, code): - self.code = code - self.init_cash = cash - self.cash = cash - self.stock_count = 0 - self.transaction_records = {} # {2016-05-05: ('BUY', 20.1元, 1000股), 2016-03-01: ('SALE', 23.0元, 200股)} - self.current_price = 0 - - def buy(self, price, buy_date, buy_ratio=1.0): - """ - :param price: - :param buy_date: - :param buy_ratio: 默认为1, 以所有现金购买; - :return: - """ - count = self.cash / price - count = (int)(count / 100) * 100 - self.cash = self.cash - price * count - self.stock_count = count - self.transaction_records[buy_date] = ('BUY', price, count) - print buy_date, 'BUY', price, count - a = 0 - - def sale(self, price, sale_date, sale_ratio=1.0): - """ - :param price: - :param sale_date: - :param sale_ratio: 默认为1, 卖出所有; - :return: - """ - money = price * self.stock_count - self.cash += money - - dates = self.transaction_records.keys() - dates.sort() - data = self.transaction_records.get(dates[-1]) - price_buy = data[1] - - self.transaction_records[sale_date] = ('SALE', price, self.stock_count) - print sale_date, 'SALE', price, self.stock_count, "{:.2f}%".format((price-price_buy)/price_buy*100) - - self.stock_count = 0 - - def summary(self, price = 0): - """ - 统计时的股价 - :param price: - :return: - """ - if price == 0: - price = self.current_price - - cash_now = self.cash + self.stock_count * price - out1 = '交易次数:{}'.format(len(self.transaction_records)) - out2 = "收益率：{}%".format((cash_now - self.init_cash) / self.init_cash * 100) - print self.code, out1, out2 - return (cash_now - self.init_cash) / self.init_cash * 100, len(self.transaction_records) - -if __name__ == "__main__": - list_stock = ['600011','002600','002505','000725','000783','600048','300315','002167','601001'] - result = [] -# for code in list_stock: -# result.append(tread_track_backtest(code)) -# print '平均盈亏',sum(result)/len(list_stock), '%' - #result += tread_track('150172') - # df = ts.get_realtime_quotes(list_stock) - # for index in df.index: - # tread_track_live_trading(list_stock[index], df.iloc[index]['price']) - - - # import tushare as ts - # df = ts.get_hs300s() - # print df.head() - # stockPoss = [tread_track_backtest(code) for code in df['code'].get_values()[::]] - # - # result_list = list() - # for stockPos in stockPoss: - # if stockPos: - # r1, r2 = stockPos.summary() - # result_list.append((stockPos.code, r2, r1)) - # result_list = sorted(result_list, key = lambda x : x[2]) - # print result_list[0] - # print result_list[-1] - - tread_track_backtest('002236') - - - - - - - - - - \ No newline at end of file +#!/usr/local/bin/python +#coding=utf-8 +# 趋势追踪策略 +""" +http://t.cn/RqQv0JW +""" + +import pandas as pd +import matplotlib.pyplot as plt +import numpy as np +# from data_process.data_get import get_stock_k_line +from trade_process import efund_mail2 +from tushare.util import dateu as du +import datetime +import tushare as ts +import cwavelet +from macd_back_test import match +#参数 +prama_ma_short = 3 # 短均线 +prama_ma_long = 7 # 长均线 +prama_ma_long20 = 15 # 长均线 +prama_ma_long30 = 20 # 长均线 +filter_range = 0.004 # 滤波区间 +drift = 0.0015 #漂移项 +param_big_band = 0.02 #大波段判断条件 +protect_big_band = 0.002 # 大波段保护止赢点 + +SIGNAL_BUY = 1 #买 +SIGNAL_SALE = -1 #卖 +SIGNAL_DEFAULT = 0 + +# 趋势追踪实时交易 +# 参数 +# code：股票代码 +# df: 个股的K线数据 +# price_now:实时股价 +def tread_track_live_trading(code, price_now, df=None): + # 分析某个时间段的股票 + #dateS = datetime.datetime.today().date() + datetime.timedelta(-100) + #date_start = dateS.strftime("%Y-%m-%d") + #df = df[df.index > date_start] + try: + fundlist = efund_mail2.get_histrydata(code,365) + df = pd.DataFrame(fundlist[::-1], columns=['date', 'close', 'countclose','change']) + #df = df.reindex(df.index[::-1]) + except Exception as e: + print str(e) + return + #print df + close_price = df['close'].get_values() + close_price = np.append(close_price, float(price_now)) + ma_short = pd.rolling_mean(close_price, prama_ma_short) + ma_long = pd.rolling_mean(close_price, prama_ma_long) + ma_long20 = pd.rolling_mean(close_price, prama_ma_long20) + ma_long30 = pd.rolling_mean(close_price, prama_ma_long30) + #print ma_short + + signal = SIGNAL_SALE + print '趋势追踪实时交易,交易开始' + + plt.figure(figsize=(8,5)) + plt.plot(range(len(ma_short)), ma_short, label=code, color="b", linewidth=1) + plt.plot(range(len(ma_long)), ma_long, label=code, color="r", linewidth=1) + plt.plot(range(len(ma_long20)), ma_long20, label=code, color="g", linewidth=1) + plt.plot(range(len(ma_long30)), ma_long30, label=code, linewidth=1) + plt.plot(range(len(close_price)), close_price, label='close', linewidth=1) + plt.xlabel("Time") + plt.ylabel("Price") + plt.savefig() + + # 过滤微小波动 + extreIndex = -1 #极值点的索引 + for i in range(prama_ma_short+1, len(ma_short)-1): + bMax = ma_short[i] > ma_short[i-1] and ma_short[i] > ma_short[i+1] # 极大值的条件 + bMin = ma_short[i] < ma_short[i-1] and ma_short[i] < ma_short[i+1] # 极小值的条件 + if bMax or bMin: + extreIndex = i + elif extreIndex > 0: + if ma_short[i] > ma_short[extreIndex]*(1-filter_range) and \ + ma_short[i] < ma_short[extreIndex]*(1+filter_range): + ma_short[i] = ma_short[extreIndex] + #print i,ma_short[i] + + #plt.plot(range(len(ma_short.get_values())), ma_short.get_values(), label=code, color="r", linewidth=1) + + # 交易次数 + count_sale = 0 + count_buy = 0 + + min_index_pre = 0 #前一个极小值 + max_index_pre = 0 #前一个极大值 + + # 止损位 + keep_stop_price = 0 + keep_stop_index = 0 + + # 止赢位 + keep_win_price = 0 + keep_win_index = 0 + + total = 0 + price_buy = 0 + price_init = 0 + money_init = 50000 + stock_money = money_init + stock_count = 0 + + #for i in range(prama_ma_short+1, len(ma_short)-1): + for i in range(len(ma_short)-30, len(ma_short)-1): + + #滤波后的均线走平时（即处于滤波区间内），将其识别为一个点 + index_post = i+1 + + try: + while(ma_short[index_post] == ma_short[i] and index_post < len(ma_short)-1): + index_post += 1 + except Exception as e: + print str(e) + + # 长均线保护策略 + bLongMA_protect_close = True + try: + bLongMA_protect_close = ma_short[i] > ma_long[i] # 长均线保护是否关闭 + except Exception as e: + print str(e) + #if bLongMA_protect_close == False: + #print "长均线保护打开", i + + # 高低点比较策略 + if ma_short[i] > ma_short[i-1] and ma_short[i] > ma_short[index_post]: + #print '极大值:', ma_short[i], 'pos:', i + if bLongMA_protect_close and max_index_pre > 0 and ma_short[i] < ma_short[max_index_pre] + drift*(i-max_index_pre) and signal == SIGNAL_BUY: + signal = SIGNAL_SALE + print '卖出:', close_price[i], 'pos:', i + count_sale += 1 + total += close_price[i] - price_buy + + stock_money += stock_count * close_price[i] + stock_count = 0 + + max_index_pre = i + elif ma_short[i] < ma_short[i-1] and ma_short[i] < ma_short[index_post]: + #print '极小值:', ma_short[i], 'pos:', i + if bLongMA_protect_close and min_index_pre > 0 and ma_short[i] > ma_short[min_index_pre] + drift*(i-min_index_pre) and signal == SIGNAL_SALE: + signal = SIGNAL_BUY + print '买入:', close_price[i], 'pos:', i + count_buy += 1 + price_buy = close_price[i] + if price_init == 0: + price_init = price_buy + + stock_count = (stock_money/100)/close_price[i]*100 + stock_money = stock_money - stock_count*close_price[i] + min_index_pre = i + + # 高低点突破策略 + # 股价突破前一个低点加漂移项，则卖出 + elif bLongMA_protect_close and signal == SIGNAL_BUY and min_index_pre > 0 and \ + ma_short[i] < ma_short[min_index_pre] + drift*(i-min_index_pre): + signal = SIGNAL_SALE + print '卖出:', close_price[i], 'pos:', i + count_sale += 1 + total += close_price[i] - price_buy + + stock_money += stock_count * close_price[i] + stock_count = 0 + + # 股价突破前一个高点加漂移项，则买入 + elif bLongMA_protect_close and signal == SIGNAL_SALE and max_index_pre > 0 and \ + ma_short[i] > ma_short[max_index_pre] + drift*(i-max_index_pre): + signal = SIGNAL_BUY + print '买入:', close_price[i], 'pos:', i + count_buy += 1 + price_buy = close_price[i] + + stock_count = (stock_money/100)/close_price[i]*100 + stock_money = stock_money - stock_count*close_price[i] + + # 大波段保护策略 + elif min_index_pre > 0 and ma_short[i] >= ma_short[min_index_pre]*(1+param_big_band): + keep_stop_price = ma_short[i] * (1-protect_big_band) #止损位 + keep_stop_index = i + elif bLongMA_protect_close and signal == SIGNAL_BUY and keep_stop_price > 0 and \ + ma_short[i] < keep_stop_price + drift*(i-keep_stop_index): + signal = SIGNAL_SALE + print '卖出:', close_price[i], 'pos:', i + count_sale += 1 + total += close_price[i] - price_buy + + stock_money += stock_count * close_price[i] + stock_count = 0 + print "buy count:", count_buy + print "sale count:", count_sale + + if stock_count > 0: + stock_money += stock_count * close_price[-1] + total += close_price[-1] - price_buy + + print "每股盈利：", total, "收益率：", (stock_money-money_init)/money_init*100,"%\n" + print '交易结束' + + # plt.grid() + # plt.legend() + # plt.show() + return (stock_money-money_init)/money_init*100 + +# 趋势追踪回测 +# 参数 +# code：股票代码 +# df: 个股的K线数据 +def tread_track_backtest(code, fundlist,avg): + # df = get_stock_k_line(code, date_start='2015-12-30', date_end='2016-05-05') + # df=efund_mail2.get_histrydata(code,365) + # fundlist = efund_mail2.get_histrydata(code, 365) + df = pd.DataFrame(fundlist[::-1], columns=['date', 'close', 'countclose', 'change']) + if len(df) == 0: + return None + # print df.head(2) + # 分析某个时间段的股票 + #dateS = datetime.datetime.today().date() + datetime.timedelta(-100) + #date_start = dateS.strftime("%Y-%m-%d") + #df = df[df.index > date_start] + #print df + + # try: + # df = df.reindex(df.index[::-1]) + # except Exception as e: + # print str(e) + # return + #print df + + close_price = df['close'].get_values() + ma_short = pd.rolling_mean(df['close'], avg[0]) + ma_long = pd.rolling_mean(df['close'], avg[1]) + ma_long20 = pd.rolling_mean(close_price, avg[2]) + # ma_long30 = pd.rolling_mean(close_price, prama_ma_long30) + #print ma_short + + signal = SIGNAL_SALE + + print '趋势追踪回测,交易开始' + + #plt.figure(figsize=(16,8)) + #plt.plot(range(len(ma_short)), ma_short.get_values(), label=code, color="b", linewidth=1) + plt.figure(figsize=(6, 3)) + plt.plot(range(len(ma_short)), ma_short, label=code, color="b", linewidth=1) + plt.plot(range(len(ma_long)), ma_long, label=code, color="r", linewidth=1) + plt.plot(range(len(ma_long20)), ma_long20, label=code, color="g", linewidth=1) + # plt.plot(range(len(ma_long30)), ma_long30, label=code, linewidth=1) + plt.plot(range(len(close_price)), close_price, label='close', linewidth=1) + plt.xlabel("Time") + plt.ylabel("Price") + plt.title(code[1]) + plt.savefig(r'E:\03IT\GitHub\stock-2\trade_process\stockdata\\'+code[1]+'.jpg') + + + # 判断极点 + # for i in range(prama_ma_short+1, len(ma_short)-1): + # if ma_short[i] > ma_short[i-1] and ma_short[i] > ma_short[i+1]: + # print '极大值:', ma_short[i], 'pos:', i + # + # elif ma_short[i] < ma_short[i-1] and ma_short[i] < ma_short[i+1]: + # print '极小值:', ma_short[i], 'pos:', i + + # 过滤微小波动 + extreIndex = -1 #极值点的索引 + for i in range(prama_ma_short+1, len(ma_short)-1): + bMax = ma_short[i] > ma_short[i-1] and ma_short[i] > ma_short[i+1] # 极大值的条件 + bMin = ma_short[i] < ma_short[i-1] and ma_short[i] < ma_short[i+1] # 极小值的条件 + if bMax or bMin: + extreIndex = i + elif extreIndex > 0: + if ma_short[i] > ma_short[extreIndex]*(1-filter_range) and \ + ma_short[i] < ma_short[extreIndex]*(1+filter_range): + ma_short[i] = ma_short[extreIndex] + #print i,ma_short[i] + + xticklabels = df['date'].apply(lambda x : str(x)).get_values() + # print xticklabels[:5] + # print xticklabels[-5:] + + #plt.plot(df['date'].get_values(), ma_short.get_values(), label=code, color="r", linewidth=1) + #plt.plot(df['date'].get_values(), ma_short.get_values(), color="r") + + def wavlet_plt(): + # trick to get the axes + fig,ax = plt.subplots() + + xValues = close_price[-200:] + ax.plot(xValues, label=code, color="r", linewidth=1) + zValues = cwavelet.getWaveletData(xValues, 'db2', 2, 'sqtwolog') + zxValue = np.arange(0,len(zValues),1) + #plt.figure(figsize=(16,8)) + + ax.plot(zxValue, zValues, color="b", linewidth=2) + ax.grid() + + # make ticks and tick labels + xticks=range(0, len(xValues)+1,10) + #xticklabels=['2000-01-0'+str(n) for n in range(1,len(xValues)+1)] + xticklabels = df['date'].apply(lambda x : str(x)).get_values() + + + # set ticks and tick labels + ax.set_xticks(xticks) + ax.set_xticklabels(xticklabels,rotation=15) + + #plt.legend() + plt.show() + + min_index_pre = 0 #前一个极小值 + max_index_pre = 0 #前一个极大值 + + keep_stop_up_price = 0 + keep_stop_down_price = 0 + + stockPos = StockPosition(50000, code) + + #for i in range(prama_ma_short+1, len(ma_short)-1): + for i in range(prama_ma_long-1, len(ma_short)-1): + # print prama_ma_long, len(ma_short)-1 + # print ma_short[prama_ma_long] + # print ma_long[prama_ma_long] + + #滤波后的均线走平时（即处于滤波区间内），将其识别为一个点 + index_post = i+1 + while (ma_short[index_post] == ma_short[i] and index_post < len(ma_short) - 1): + index_post += 1 + + # 长均线保护策略: + # bLongMA_forbid_buy = ma_short[i] < ma_long[i] # 买入保护 + # bLongMA_forbid_sale = not bLongMA_forbid_buy # 卖出保护 + # + # if bLongMA_forbid_buy and signal == SIGNAL_BUY: + # signal = SIGNAL_SALE + # stockPos.sale(close_price[i], str(df.ix[i, 'date'])) + # min_index_pre = 0 + # max_index_pre = 0 + # keep_stop_up_price = 0 + # keep_stop_down_price = 0 + # continue + # if bLongMA_forbid_buy and signal == SIGNAL_SALE: + # continue + + # if len(stockPos.transaction_records): # 有交易以后才考虑此情况 + # signal = SIGNAL_BUY + # stockPos.buy(close_price[i], str(df.ix[i, 'date'])) + # #print '买入:', close_price[i], ' ', str(df.ix[i, 'date']) + # continue + + # 高低点比较策略 + if ma_short[i] > ma_short[i-1] and ma_short[i] > ma_short[index_post]: # 极大值 + # <卖出> 如果当前高点比前一个高点的向下漂移项低，则空头开仓或持有空头； + # <买入> 如果当前高点比前一个高点的向上漂移项高，则多头开仓或持有多头； + if max_index_pre > 0 and i > max_index_pre: + + if signal == SIGNAL_BUY and ma_short[i] < ma_short[max_index_pre] - drift*(i-max_index_pre): + signal = SIGNAL_SALE + stockPos.sale(close_price[i], str(df.ix[i, 'date'])) + min_index_pre = 0 + max_index_pre = 0 + keep_stop_up_price = 0 + keep_stop_down_price = 0 + continue + elif signal == SIGNAL_SALE and ma_short[i] > ma_short[max_index_pre] + drift*(i-max_index_pre): + signal = SIGNAL_BUY + stockPos.buy(close_price[i], str(df.ix[i, 'date'])) + keep_stop_up_price = 0 + keep_stop_down_price = 0 + continue + + max_index_pre = i + elif ma_short[i] < ma_short[i-1] and ma_short[i] < ma_short[index_post]: # 极小值 + # < 买入 > 如果当前低点比前一个低点的向上漂移项高，则多头开仓或持有多头； + # < 卖出 > 如果当前低点比前一个低点的向下漂移项低，则空头开仓或持有空头 + if min_index_pre > 0 and i > min_index_pre: + if signal == SIGNAL_SALE and ma_short[i] > ma_short[min_index_pre] + drift*(i-min_index_pre): + signal = SIGNAL_BUY + stockPos.buy(close_price[i], str(df.ix[i, 'date'])) + keep_stop_up_price = 0 + keep_stop_down_price = 0 + continue + elif signal == SIGNAL_BUY and ma_short[i] > ma_short[min_index_pre] - drift*(i-min_index_pre): + signal = SIGNAL_SALE + stockPos.sale(close_price[i], str(df.ix[i, 'date'])) + min_index_pre = 0 + max_index_pre = 0 + keep_stop_up_price = 0 + keep_stop_down_price = 0 + continue + + min_index_pre = i + + # 高低点突破策略 + # <卖出>如果滤波后的均线比前一个低点的向下漂移项低，则空头开仓或持有空头。 + if signal == SIGNAL_BUY and min_index_pre > 0 and ma_short[i] < ma_short[min_index_pre] - drift*(i-min_index_pre): + signal = SIGNAL_SALE + stockPos.sale(close_price[i], str(df.ix[i, 'date'])) + min_index_pre = 0 + max_index_pre = 0 + keep_stop_up_price = 0 + keep_stop_down_price = 0 + continue + + # <买入>如果滤波后的均线比前一个高点的向上漂移项高，则多头开仓或持有多头。 + elif signal == SIGNAL_SALE and max_index_pre > 0 and ma_short[i] > ma_short[max_index_pre] + drift*(i-max_index_pre): + signal = SIGNAL_BUY + stockPos.buy(close_price[i], str(df.ix[i, 'date'])) + keep_stop_up_price = 0 + keep_stop_down_price = 0 + continue + + # 大波段保护策略 + ## 向上的大波段 + if min_index_pre > 0 and close_price[i] >= ma_short[min_index_pre] * (1 + param_big_band): + if close_price[i] * (1 - protect_big_band) > keep_stop_up_price: + keep_stop_up_price = close_price[i] * (1 - protect_big_band) # 止盈位 + keep_stop_up_index = i + if signal == SIGNAL_BUY and keep_stop_up_price > 0 and \ + close_price[i] < keep_stop_up_price + drift*(i-keep_stop_up_index): + signal = SIGNAL_SALE + stockPos.sale(close_price[i], str(df.ix[i, 'date'])) + min_index_pre = 0 + max_index_pre = 0 + keep_stop_up_price = 0 + keep_stop_down_price = 0 + continue + + ## 向下的大波段 + if max_index_pre > 0 and close_price[i] <= ma_short[max_index_pre] * (1 - param_big_band): + if keep_stop_down_price == 0 or close_price[i] * (1 + protect_big_band) < keep_stop_down_price: + keep_stop_down_price = close_price[i] * (1 + protect_big_band) # 止损位 + keep_stop_down_index = i + if signal == SIGNAL_SALE and keep_stop_down_price > 0 and \ + close_price[i] > keep_stop_down_price + drift * (i + keep_stop_down_index): + + signal = SIGNAL_BUY + keep_stop_up_price = 0 + keep_stop_down_price = 0 + stockPos.buy(close_price[i], str(df.ix[i, 'date'])) + continue + stockPos.current_price = close_price[-1] + stockPos.startcountPrice = close_price[0] + + stockPos.summary() + print '交易结束' + # plt.grid() + # plt.legend() + # plt.show() + return stockPos + +class StockPosition(): + + def __init__(self, cash, code): + self.code = code + self.init_cash = cash + self.cash = cash + self.stock_count = 0 + self.transaction_records = {} # {2016-05-05: ('BUY', 20.1元, 1000股), 2016-03-01: ('SALE', 23.0元, 200股)} + self.current_price = 0 + self.startcountPrice=0 + + + def buy(self, price, buy_date, buy_ratio=1.0): + """ + :param price: + :param buy_date: + :param buy_ratio: 默认为1, 以所有现金购买; + :return: + """ + count = self.cash / price + count = (int)(count / 100) * 100 + self.cash = self.cash - price * count + self.stock_count = count + self.transaction_records[buy_date] = ('BUY', price, count) + print buy_date, 'BUY', price, count + a = 0 + + def sale(self, price, sale_date, sale_ratio=1.0): + """ + :param price: + :param sale_date: + :param sale_ratio: 默认为1, 卖出所有; + :return: + """ + money = price * self.stock_count + self.cash += money + + dates = self.transaction_records.keys() + dates.sort() + data = self.transaction_records.get(dates[-1]) + price_buy = data[1] + + self.transaction_records[sale_date] = ('SALE', price, self.stock_count) + print sale_date, 'SALE', price, self.stock_count, "{:.2f}%".format((price-price_buy)/price_buy*100) + + self.stock_count = 0 + + def summary(self, price = 0): + """ + 统计时的股价 + :param price: + :return: + """ + if price == 0: + price = self.current_price + initcash_now=self.init_cash*(self.current_price/self.startcountPrice) + cash_now = self.cash + self.stock_count * price + out1 = '交易次数:{}'.format(len(self.transaction_records)) + out2 = "收益率：{}%".format((cash_now - self.init_cash) / self.init_cash * 100) + out3= "超额收益率：{}%".format((cash_now - initcash_now) / self.init_cash * 100) + print self.code, out1, out2 ,out3 + return (cash_now - self.init_cash) / self.init_cash * 100, len(self.transaction_records) + +def stock_trader_main(code): + codelist = [['002963', 'egold'], ['003321', 'eoil'], ['004744', 'eGEI'], ['110003', 'eSSE50'], ['110020', 'HS300'], + ['110031', 'eHSI'], ['161130', 'eNASDAQ100'], ['110028', 'anxinB'], ['110022', 'eConsumption '], + ['161125', 'SPX500']] + name = ['eGold', 'eoil', '创业板', '上证50', '沪深300', '恒生', '易方达纳斯达克100', '安心b'] + buysell = [] + stockTxtNewPath = "E:/03IT/GitHub/stock-2/stockdata/" + 'GAOpt100.txt' + file_to_read = open(stockTxtNewPath, 'r') + lines = file_to_read.readlines() # 整行读取数据 + for i in codelist: + if not lines: + break + pass + else: + contmatch = match('MA', i, lines) + if len(contmatch) != 0: + Avg = [int(contmatch[0][1].split(',')[0]), int(contmatch[0][1].split(',')[1]), + int(contmatch[0][1].split(',')[2])] + elif (len(contmatch)==0): + Avg = ([3, 8, 40]) + # save(strfundcode=i ,numdays=365*1) + ifundlist = efund_mail2.get_histrydata(i, 365) + buysell.append(tread_track_backtest(i,ifundlist,Avg)) + + for i in buysell: + i.summary() + # url = 'http://fund.eastmoney.com/%s.html' % i[0] + # todayvalue = efund_mail2.spider(url) + # if todayvalue != None: + # tread_track_live_trading(i,todayvalue[1]) + +if __name__ == "__main__": + # list_stock = ['600011','002600','002505','000725','000783','600048','300315','002167','601001'] + # result = [] + # for code in list_stock: + # result.append(tread_track_backtest(code)) + # print '平均盈亏',sum(result)/len(list_stock), '%' + #result += tread_track('150172') + # df = ts.get_realtime_quotes(list_stock) + # for index in df.index: + # tread_track_live_trading(list_stock[index], df.iloc[index]['price']) + + + # import tushare as ts + # df = ts.get_hs300s() + # print df.head() + # stockPoss = [tread_track_backtest(code) for code in df['code'].get_values()[::]] + # + # result_list = list() + # for stockPos in stockPoss: + # if stockPos: + # r1, r2 = stockPos.summary() + # result_list.append((stockPos.code, r2, r1)) + # result_list = sorted(result_list, key = lambda x : x[2]) + # print result_list[0] + # print result_list[-1] + code = [['002963', 'egold'], ['003321', 'eoil'], ['004744', 'eGEI'], ['110003', 'eSSE50'], ['110020', 'HS300'], + ['110031', 'eHSI'], ['161130', 'eNASDAQ100'], ['110028', 'anxinB'], ['110022', 'eConsumption '], + ['161125', 'SPX500']] + stock_trader_main(code) + + + + + + + + + + + + diff --git a/trade_process/strategy/tread_tracking.pyc b/trade_process/strategy/tread_tracking.pyc new file mode 100644 index 0000000..85e29b8 Binary files /dev/null and b/trade_process/strategy/tread_tracking.pyc differ diff --git a/trade_process/strategy/turtletrader.py b/trade_process/strategy/turtletrader.py index 8df6465..1d78217 100644 --- a/trade_process/strategy/turtletrader.py +++ b/trade_process/strategy/turtletrader.py @@ -1,11 +1,11 @@ -#!/usr/local/bin/python -#coding=utf-8 - -# 海龟交易系统 -# 参数： -# code：股票代码 -# klineValue： k线历史数据 -def turtle(code, klineValue): - print 'begin turtle' - +#!/usr/local/bin/python +#coding=utf-8 + +# 海龟交易系统 +# 参数： +# code：股票代码 +# klineValue： k线历史数据 +def turtle(code, klineValue): + print 'begin turtle' + print 'end turtle' \ No newline at end of file diff --git "a/trade_process/\345\237\272\351\207\221\347\273\274\345\220\210\346\216\222\345\220\215\347\273\223\346\236\234.txt" "b/trade_process/\345\237\272\351\207\221\347\273\274\345\220\210\346\216\222\345\220\215\347\273\223\346\236\234.txt" new file mode 100644 index 0000000..d9087fd --- /dev/null +++ "b/trade_process/\345\237\272\351\207\221\347\273\274\345\220\210\346\216\222\345\220\215\347\273\223\346\236\234.txt" @@ -0,0 +1,187 @@ +1 001740 光大中国制造2025混合 混合型 9 22 7 17 13.75 +2 260108 景顺长城新兴成长混合 混合型 24 11 9 18 15.5 +3 162605 景顺长城鼎益混合(LOF) 混合型 27 10 10 15 15.5 +4 110022 易方达消费行业 股票型 45 15 8 3 17.75 +5 161725 招商中证白酒指数分级 股票指数 70 4 3 1 19.5 +6 000248 汇添富中证主要消费ETF联接 联接基金 58 6 14 8 21.5 +7 160222 国泰国证食品饮料行业指数分级 股票指数 65 5 11 5 21.5 +8 000083 汇添富消费行业混合 混合型 42 24 16 34 29.0 +9 550016 信诚至远C 分级杠杆 1 3 49 68 30.25 +10 519664 银河美丽混合A 混合型 16 23 18 66 30.75 +11 481012 工银深证红利联接 联接基金 15 54 25 30 31.0 +12 160632 鹏华酒分级 股票指数 100 7 21 7 33.75 +13 206007 鹏华消费优选混合 混合型 35 34 19 51 34.75 +14 070032 嘉实优化红利混合 混合型 49 28 12 52 35.25 +15 519665 银河美丽混合C 混合型 21 31 22 70 36.0 +16 001542 国泰互联网+股票 股票型 100 35 6 6 36.75 +17 180012 银华富裕主题混合 混合型 100 33 17 4 38.5 +18 000854 鹏华养老产业 股票型 34 26 32 64 39.0 +19 001606 农银工业4.0混合 混合型 55 25 30 65 43.75 +20 000462 农银主题轮动混合 混合型 67 14 23 71 43.75 +21 110003 易方达上证50指数A 股票指数 33 59 60 26 44.5 +22 001287 安信优势增长混合A 混合型 32 13 72 62 44.75 +23 519056 海富通内需热点混合 混合型 11 12 76 84 45.75 +24 000431 鹏华品牌传承混合 混合型 46 27 35 76 46.0 +25 000619 东方红产业升级混合 混合型 100 48 26 13 46.75 +26 040008 华安策略优选混合 混合型 44 100 15 29 47.0 +27 004008 中融鑫思路混合A 混合型 100 1 4 86 47.75 +28 004009 中融鑫思路混合C 混合型 100 2 5 87 48.5 +29 000742 国泰新经济灵活配置混合 混合型 100 75 13 10 49.5 +30 001387 中融新经济混合A 混合型 100 100 1 2 50.75 +31 001112 东方红中国优势混合 混合型 100 69 20 16 51.25 +32 360007 光大保德信优势配置混合 混合型 14 62 71 63 52.5 +33 530015 建信深证60联接 联接基金 28 94 46 44 53.0 +34 260103 景顺长城动力平衡 混合型 18 95 65 35 53.25 +35 540012 汇丰晋信恒生龙头指数A 股票指数 38 100 54 23 53.75 +36 260110 景顺长城精选蓝筹混合 混合型 48 79 43 45 53.75 +37 260116 景顺长城核心竞争力混合A 混合型 52 87 38 38 53.75 +38 001076 易方达改革红利混合 混合型 82 9 48 79 54.5 +39 519690 交银稳健配置混合A 混合型 100 45 24 55 56.0 +40 310388 申万菱信消费增长混合 混合型 100 32 29 67 57.0 +41 001631 天弘中证食品饮料指数A 股票指数 100 16 85 28 57.25 +42 660010 农银策略精选混合 混合型 100 30 73 36 59.75 +43 001632 天弘中证食品饮料指数C 股票指数 100 17 92 31 60.0 +44 020005 国泰金马稳健 混合型 88 63 61 32 61.0 +45 004528 长盛盛通纯债A none 2 43 100 100 61.25 +46 001583 安信新常态股票 股票型 53 100 31 61 61.25 +47 003119 博时鑫源混合A 混合型 4 42 100 100 61.5 +48 003120 博时鑫源混合C 混合型 3 44 100 100 61.75 +49 001281 长安鑫利优选混合A 混合型 100 64 34 49 61.75 +50 169101 东方红睿丰混合 混合型 100 100 36 12 62.0 +51 165312 建信央视50 股票指数 100 100 28 21 62.25 +52 002072 长安鑫利优选混合C 混合型 100 66 37 50 63.25 +53 001208 诺安低碳经济股票 股票型 100 37 74 43 63.5 +54 001170 泰达宏利复兴混合 混合型 76 65 41 73 63.75 +55 000634 富国天盛灵活配置混合 混合型 39 19 100 100 64.5 +56 160216 国泰商品 QDII 10 50 100 100 65.0 +57 001605 国富沪港深成长精选股票 股票型 40 21 100 100 65.25 +58 000029 富国宏观策略 混合型 43 18 100 100 65.25 +59 164906 交银中证海外中国互联网指数 QDII-指数 100 100 42 20 65.5 +60 160628 鹏华地产分级 股票指数 5 58 100 100 65.75 +61 519066 汇添富蓝筹稳健 混合型 100 100 27 39 66.5 +62 110011 易方达中小盘混合 混合型 100 100 57 11 67.0 +63 519606 国泰金鑫 股票型 100 100 51 19 67.5 +64 519772 交银新生活力灵活配置混合 混合型 100 8 67 96 67.75 +65 217027 招商央视财经50指数A 股票指数 100 100 52 22 68.5 +66 161129 易方达原油A类人民币 QDII 25 52 100 100 69.25 +67 020026 国泰成长优选混合 混合型 100 100 64 14 69.5 +68 003321 易方达原油C类人民币 QDII 26 55 100 100 70.25 +69 519068 汇添富成长焦点混合 混合型 100 100 33 48 70.25 +70 000835 华润元大富时中国A50指数 股票指数 37 100 98 47 70.5 +71 002124 广发新兴产业混合 混合型 17 67 100 100 71.0 +72 002121 广发沪港深新起点股票 股票型 47 100 47 92 71.5 +73 002853 华富元鑫灵活配置混合A 混合型 100 100 2 85 71.75 +74 540009 汇丰晋信消费红利 股票型 100 100 56 33 72.25 +75 001044 嘉实新消费股票 股票型 100 100 50 40 72.5 +76 163412 兴全轻资产混合(LOF) 混合型 54 39 100 100 73.25 +77 000934 国富大中华精选混合 QDII 100 100 84 9 73.25 +78 310398 申万菱信沪深300价值指数 股票指数 57 100 96 41 73.5 +79 241001 华宝海外中国成长混合 QDII 100 100 70 25 73.75 +80 001712 东方红优势精选混合 混合型 100 100 59 37 74.0 +81 002851 南方品质混合 混合型 77 91 40 89 74.25 +82 398061 中海消费混合 混合型 100 40 82 75 74.25 +83 000988 嘉实全球互联网股票人民币 QDII 100 100 75 24 74.75 +84 002624 广发优企精选混合 混合型 83 20 100 100 75.75 +85 164508 国富中证100指数增强分级 股票指数 50 100 99 56 76.25 +86 519225 海富通集利债券 债券型 6 100 100 100 76.5 +87 001179 德邦大健康灵活配置混合 混合型 100 93 55 58 76.5 +88 160218 国泰国证房地产行业指数分级 股票指数 7 100 100 100 76.75 +89 003243 上投摩根中国世纪人民币 QDII 100 61 53 93 76.75 +90 161721 招商沪深300地产指数分级 股票指数 8 100 100 100 77.0 +91 001892 长盛新兴成长混合 混合型 22 89 100 100 77.75 +92 001518 万家瑞兴 混合型 12 100 100 100 78.0 +93 501018 南方原油 QDII 36 76 100 100 78.0 +94 519185 万家精选混合 混合型 13 100 100 100 78.25 +95 000173 汇添富美丽30混合 混合型 100 46 88 81 78.75 +96 001865 前海开源事件驱动混合C 混合型 80 77 81 80 79.5 +97 001371 富国沪港深价值混合 混合型 100 100 91 27 79.5 +98 004642 南方房地产ETF联接A none 19 100 100 100 79.75 +99 002085 长盛互联网+混合 混合型 20 100 100 100 80.0 +100 000423 前海开源事件驱动混合A 混合型 84 78 80 78 80.0 +101 004643 南方房地产ETF联接C none 23 100 100 100 80.75 +102 000751 嘉实新兴产业股票 股票型 100 100 66 57 80.75 +103 002666 前海开源沪港深创新成长混合A 混合型 100 99 39 88 81.5 +104 570001 诺德价值优势混合 混合型 100 100 68 59 81.75 +105 001047 光大国企改革股票 股票型 29 100 100 100 82.25 +106 001725 汇添富高端制造股票 股票型 100 29 100 100 82.25 +107 020023 国泰事件驱动策略混合 混合型 100 100 87 42 82.25 +108 519191 万家新利灵活配置混合 混合型 30 100 100 100 82.5 +109 001581 华安沪港深通精选灵活配置混合 混合型 31 100 100 100 82.75 +110 001878 嘉实沪港深精选股票 股票型 100 100 44 90 83.5 +111 376510 上投大盘蓝筹 股票型 100 100 62 72 83.5 +112 180013 银华领先策略混合 混合型 100 36 100 100 84.0 +113 002667 前海开源沪港深创新成长混合C 混合型 100 100 45 91 84.0 +114 118001 易方达亚洲精选 QDII 100 100 83 53 84.0 +115 000127 农银行业领先混合 混合型 100 38 100 100 84.5 +116 110015 易方达行业领先混合 混合型 100 100 69 69 84.5 +117 001410 信达澳银新能源产业股票 股票型 100 100 78 60 84.5 +118 167301 方正富邦保险主题指数分级 股票指数 100 100 94 46 85.0 +119 003704 光大保德信事件驱动混合 混合型 41 100 100 100 85.25 +120 002189 农银汇理国企改革混合 混合型 100 41 100 100 85.25 +121 002697 中欧消费主题股票C 股票型 100 47 100 100 86.75 +122 320010 诺安中证100 股票指数 99 100 95 54 87.0 +123 100022 富国天瑞强势 混合型 100 49 100 100 87.25 +124 001626 国泰央企改革股票 股票型 51 100 100 100 87.75 +125 002621 中欧消费主题股票A 股票型 100 51 100 100 87.75 +126 001404 招商移动互联网产业股票基金 股票型 56 96 100 100 88.0 +127 003940 银华盛世精选灵活配置混合发起式 混合型 100 100 58 94 88.0 +128 519008 汇添富优势精选 混合型 100 53 100 100 88.25 +129 163406 兴全合润分级混合 混合型 100 56 100 100 89.0 +130 001236 博时丝路主题股票A 股票型 100 57 100 100 89.25 +131 002534 华安稳固收益债券A 债券型 100 100 63 95 89.5 +132 501301 华宝香港大盘 股票指数 59 100 100 100 89.75 +133 519671 银河沪深300价值 股票指数 60 100 100 100 90.0 +134 001657 长安鑫富领先混合 混合型 100 60 100 100 90.0 +135 001651 工银新蓝筹股票 股票型 61 100 100 100 90.25 +136 003154 华宝新活力混合 混合型 81 80 100 100 90.25 +137 162719 广发道琼斯石油指数A QDII-指数 62 100 100 100 90.5 +138 000167 广发聚优灵活配置混合A 混合型 100 100 79 83 90.5 +139 003261 安信沪深300增强发起式A 股票指数 63 100 100 100 90.75 +140 004243 广发道琼斯石油指数C QDII-指数 64 100 100 100 91.0 +141 000020 景顺长城品质投资混合 混合型 100 100 90 74 91.0 +142 003262 安信沪深300增强发起式C 股票指数 66 100 100 100 91.5 +143 163302 大摩资源优选混合(LOF) 混合型 68 100 100 100 92.0 +144 002595 博时工业4.0主题股票 股票型 100 68 100 100 92.0 +145 110010 易方达价值成长混合A 混合型 69 100 100 100 92.25 +146 004374 华泰保兴吉年丰混合发起A 混合型 100 70 100 100 92.5 +147 213010 宝盈中证100增强 股票指数 71 100 100 100 92.75 +148 004375 华泰保兴吉年丰混合发起C 混合型 100 71 100 100 92.75 +149 410009 华富量子生命力混合 混合型 72 100 100 100 93.0 +150 001458 广发中证全指主要消费联接A 联接基金 100 72 100 100 93.0 +151 519087 新华优选分红混合 混合型 73 100 100 100 93.25 +152 002976 广发中证全指主要消费联接C 联接基金 100 73 100 100 93.25 +153 004357 南方智慧混合 混合型 74 100 100 100 93.5 +154 519165 新华鑫利灵活配置 混合型 100 74 100 100 93.5 +155 002214 中海沪港深价值优选混合 混合型 100 100 77 97 93.5 +156 310358 申万菱信新经济混合 混合型 100 100 97 77 93.5 +157 002443 前海开源沪港深龙头精选混合 混合型 75 100 100 100 93.75 +158 350005 天治中国制造2025 混合型 100 100 93 82 93.75 +159 410008 华富中证100 股票指数 78 100 100 100 94.5 +160 481001 工银核心价值混合A 混合型 79 100 100 100 94.75 +161 000746 招商行业精选股票 股票型 100 81 100 100 95.25 +162 398021 中海能源策略 混合型 100 82 100 100 95.5 +163 001681 新华积极价值灵活配置混合 混合型 100 83 100 100 95.75 +164 160605 鹏华中国50 混合型 98 86 100 100 96.0 +165 002142 博时外延增长主题灵活配置混合 混合型 100 84 100 100 96.0 +166 519196 万家新兴蓝筹灵活配置混合 混合型 100 100 86 98 96.0 +167 502040 长盛上证50指数分级 股票指数 85 100 100 100 96.25 +168 001528 诺安先进制造股票 股票型 100 85 100 100 96.25 +169 162203 泰达宏利稳定混合 混合型 86 100 100 100 96.5 +170 001040 新华策略精选股票 股票型 87 100 100 100 96.75 +171 160624 鹏华消费领先混合 混合型 100 88 100 100 97.0 +172 003597 长盛盛腾混合C 混合型 100 100 89 99 97.0 +173 519706 交银深证300联接 联接基金 89 100 100 100 97.25 +174 501050 华夏上证50AH优选指数 股票指数 90 100 100 100 97.5 +175 519732 交银定期支付双息平衡混合 混合型 100 90 100 100 97.5 +176 163803 中银持续增长混合A 混合型 91 100 100 100 97.75 +177 240019 华宝上证180成长联接 联接基金 92 100 100 100 98.0 +178 001186 富国文体健康股票 股票型 100 92 100 100 98.0 +179 502020 国金上证50分级 股票指数 93 100 100 100 98.25 +180 399001 中海上证50 股票指数 94 100 100 100 98.5 +181 160716 嘉实基本面50 股票指数 95 100 100 100 98.75 +182 004477 嘉实沪港深回报混合 混合型 96 100 100 100 99.0 +183 070023 嘉实深证120联接 联接基金 97 100 100 100 99.25 +184 100026 富国天合稳健优选混合 混合型 100 97 100 100 99.25 +185 004183 富国产业升级混合 none 100 98 100 100 99.5 +186 001163 银华中国梦30股票 股票型 100 100 100 100 100.0 +187 003596 长盛盛腾混合A 混合型 100 100 100 100 100.0 diff --git a/util/MyLogger.py b/util/MyLogger.py index 423aaba..c41a90b 100644 --- a/util/MyLogger.py +++ b/util/MyLogger.py @@ -1,88 +1,88 @@ -# -*- coding: utf-8 -*- -#!/usr/bin/env python - -__author__ = 'cbb' - -import sys -reload(sys) -sys.setdefaultencoding("utf-8") - -import logging -import logging.handlers -import ConfigParser -from codeConvert import * - -#用字典保存日志级别 -format_dict = { - 1 : logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(filename)s - %(funcName)s() - line:%(lineno)d - %(message)s'), #error - 2 : logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(filename)s - %(message)s'), # debug - 3 : logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'), #info - 4 : logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'), - 5 : logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') -} - - -#日志系统， 既把日志输出到控制台， 还要写入日志文件 -class Logger(): - def __init__(self, logname='', loglevel=1, logger='logger'): - ''' - 指定保存日志的文件路径，日志级别，以及调用文件 - 将日志存入到指定的文件中 - ''' - - # 创建一个logger - self.logger = logging.getLogger(logger) - self.logger.setLevel(logging.DEBUG) - - if len(logname) == 0: - cf = ConfigParser.ConfigParser() - cf.read('./config.ini') - logname = cf.get('log','info_log_name') - - - #hdlr=logging.basicConfig(logname,level=logging.NOTSET,format='%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S') - - # 创建一个handler，用于写入日志文件 - #fh = logging.handlers.TimedRotatingFileHandler(logname, 'D') - #fh.suffix = "%Y%m%d.log" - # fh = logging.FileHandler(logname) - # fh.setLevel(logging.DEBUG) - - # 再创建一个handler，用于输出到控制台 - ch = logging.StreamHandler() - ch.setLevel(logging.DEBUG) - - # 定义handler的输出格式 - #formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') - formatter = format_dict[int(loglevel)] - # fh.setFormatter(formatter) - ch.setFormatter(formatter) - - # 给logger添加handler - # self.logger.addHandler(fh) - self.logger.addHandler(ch) - - - def getlog(self): - return self.logger - - #自定义 - def addLog(self, msg, level='info'): - if level == 'info': - self.logger.info(encode_wrap(msg)) - elif level == 'debug': - self.logger.debug(encode_wrap(msg)) - elif level == 'error': - self.logger.error(encode_wrap(msg)) - - -#ErrorLogger = Logger(logname='./data/log/error.log', logger='error') -#InfoLogger = Logger(logname='./data/log/info.log', logger='info') - -if __name__ == "__main__": - - - - logger = Logger() - logger.addLog('百度') - +# -*- coding: utf-8 -*- +#!/usr/bin/env python + +__author__ = 'cbb' + +import sys +reload(sys) +sys.setdefaultencoding("utf-8") + +import logging +import logging.handlers +import ConfigParser +from codeConvert import * + +#用字典保存日志级别 +format_dict = { + 1 : logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(filename)s - %(funcName)s() - line:%(lineno)d - %(message)s'), #error + 2 : logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(filename)s - %(message)s'), # debug + 3 : logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'), #info + 4 : logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'), + 5 : logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') +} + + +#日志系统， 既把日志输出到控制台， 还要写入日志文件 +class Logger(): + def __init__(self, logname='', loglevel=1, logger='logger'): + ''' + 指定保存日志的文件路径，日志级别，以及调用文件 + 将日志存入到指定的文件中 + ''' + + # 创建一个logger + self.logger = logging.getLogger(logger) + self.logger.setLevel(logging.DEBUG) + + if len(logname) == 0: + cf = ConfigParser.ConfigParser() + cf.read('./config.ini') + logname = cf.get('log','info_log_name') + + + #hdlr=logging.basicConfig(logname,level=logging.NOTSET,format='%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S') + + # 创建一个handler，用于写入日志文件 + #fh = logging.handlers.TimedRotatingFileHandler(logname, 'D') + #fh.suffix = "%Y%m%d.log" + # fh = logging.FileHandler(logname) + # fh.setLevel(logging.DEBUG) + + # 再创建一个handler，用于输出到控制台 + ch = logging.StreamHandler() + ch.setLevel(logging.DEBUG) + + # 定义handler的输出格式 + #formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') + formatter = format_dict[int(loglevel)] + # fh.setFormatter(formatter) + ch.setFormatter(formatter) + + # 给logger添加handler + # self.logger.addHandler(fh) + self.logger.addHandler(ch) + + + def getlog(self): + return self.logger + + #自定义 + def addLog(self, msg, level='info'): + if level == 'info': + self.logger.info(encode_wrap(msg)) + elif level == 'debug': + self.logger.debug(encode_wrap(msg)) + elif level == 'error': + self.logger.error(encode_wrap(msg)) + + +#ErrorLogger = Logger(logname='./data/log/error.log', logger='error') +#InfoLogger = Logger(logname='./data/log/info.log', logger='info') + +if __name__ == "__main__": + + + + logger = Logger() + logger.addLog('百度') + diff --git a/util/MyLogger.pyc b/util/MyLogger.pyc new file mode 100644 index 0000000..883dab0 Binary files /dev/null and b/util/MyLogger.pyc differ diff --git a/util/__init__.py b/util/__init__.py index 1e72a8b..4f0848b 100644 --- a/util/__init__.py +++ b/util/__init__.py @@ -1,2 +1,2 @@ -from util.stockutil import getSixDigitalStockCode +from util.stockutil import getSixDigitalStockCode from util.commons import * \ No newline at end of file diff --git a/util/__init__.pyc b/util/__init__.pyc new file mode 100644 index 0000000..3fe90ff Binary files /dev/null and b/util/__init__.pyc differ diff --git a/util/codeConvert.py b/util/codeConvert.py index 6d77642..94b5383 100644 --- a/util/codeConvert.py +++ b/util/codeConvert.py @@ -1,63 +1,63 @@ -# -*- coding: utf8 -*- - -import sys, time -import datetime - -#console stdout -def encode_wrap(str): - try: - sse = sys.stdout.encoding - return str.encode(sse) - except Exception, e: - return str - -def str_qt_to_utf(qt_str): - utf_str = unicode(qt_str.toUtf8(), 'utf-8', 'ignore') - return utf_str - -def str_to_datatime(str_time, format='%Y-%m-%d %H:%M:%S'): - d = datetime.datetime.strptime(str_time, format) - return d - -def GetDate(timefrom1970): - return time.strftime("%Y-%m-%d",time.localtime(timefrom1970)) - -def GetTime(timefrom1970): - return time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(timefrom1970)) - -def GetNowTime(): - return time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(time.time())) - -def GetNowTime2(): - return time.strftime("%Y-%m-%d 00:00:00",time.localtime(time.time())) - -def GetNowTim3(): - return time.strftime("%Y-%m-%d-%H-%M-%S",time.localtime(time.time())) - -def GetNowDate(): - return time.strftime("%Y-%m-%d",time.localtime(time.time())) - -# 规整化发表时间 -def regularization_time(publish_time): - now = GetNowDate() - if '分钟前' in publish_time: # 22分钟前 - publish_time = publish_time.replace('分钟前','') - publish_time = time.time() - int(publish_time)*60 - publish_time = time.strftime("%Y-%m-%d %H:%M",time.localtime(publish_time)) - publish_time = publish_time + ':00' - - elif '今天' in publish_time: - publish_time = publish_time.replace('今天', now) + ':00' - - elif '月' in publish_time: - publish_time = publish_time.replace('月','-').replace('日','') - publish_time = time.strftime("%Y-",time.localtime(time.time())) + publish_time + ':00' - elif len(publish_time) == 5: # 形如14:58 - publish_time = now + " " + publish_time + ":00" - elif len(publish_time) == 11: #形如09-29 12:38 - publish_time = time.strftime("%Y-",time.localtime(time.time())) + publish_time + ':00' - elif len(publish_time) == 16: #形如2015-09-29 12:38 - publish_time = publish_time + ':00' - - +# -*- coding: utf8 -*- + +import sys, time +import datetime + +#console stdout +def encode_wrap(str): + try: + sse = sys.stdout.encoding + return str.encode(sse) + except Exception, e: + return str + +def str_qt_to_utf(qt_str): + utf_str = unicode(qt_str.toUtf8(), 'utf-8', 'ignore') + return utf_str + +def str_to_datatime(str_time, format='%Y-%m-%d %H:%M:%S'): + d = datetime.datetime.strptime(str_time, format) + return d + +def GetDate(timefrom1970): + return time.strftime("%Y-%m-%d",time.localtime(timefrom1970)) + +def GetTime(timefrom1970): + return time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(timefrom1970)) + +def GetNowTime(): + return time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(time.time())) + +def GetNowTime2(): + return time.strftime("%Y-%m-%d 00:00:00",time.localtime(time.time())) + +def GetNowTim3(): + return time.strftime("%Y-%m-%d-%H-%M-%S",time.localtime(time.time())) + +def GetNowDate(): + return time.strftime("%Y-%m-%d",time.localtime(time.time())) + +# 规整化发表时间 +def regularization_time(publish_time): + now = GetNowDate() + if '分钟前' in publish_time: # 22分钟前 + publish_time = publish_time.replace('分钟前','') + publish_time = time.time() - int(publish_time)*60 + publish_time = time.strftime("%Y-%m-%d %H:%M",time.localtime(publish_time)) + publish_time = publish_time + ':00' + + elif '今天' in publish_time: + publish_time = publish_time.replace('今天', now) + ':00' + + elif '月' in publish_time: + publish_time = publish_time.replace('月','-').replace('日','') + publish_time = time.strftime("%Y-",time.localtime(time.time())) + publish_time + ':00' + elif len(publish_time) == 5: # 形如14:58 + publish_time = now + " " + publish_time + ":00" + elif len(publish_time) == 11: #形如09-29 12:38 + publish_time = time.strftime("%Y-",time.localtime(time.time())) + publish_time + ':00' + elif len(publish_time) == 16: #形如2015-09-29 12:38 + publish_time = publish_time + ':00' + + return publish_time \ No newline at end of file diff --git a/util/codeConvert.pyc b/util/codeConvert.pyc new file mode 100644 index 0000000..43e4ead Binary files /dev/null and b/util/codeConvert.pyc differ diff --git a/util/commons.py b/util/commons.py index 7f5ed91..c153ced 100644 --- a/util/commons.py +++ b/util/commons.py @@ -1,37 +1,37 @@ -#!/usr/local/bin/python -#coding=utf-8 -# 定义的常量 - -import os - -################################## -# Redis - -#基本信息表 -PRE_STOCK_BASIC = 'stock_basic_info:' #股票基本信息的前缀 -KEY_CODE = 'code' -KEY_NAME = 'name' -KEY_INDUSTRY = 'industry' -KEY_AREA = 'area' -KEY_PE = 'pe' #市盈率 -KEY_EPS = 'eps' #每股收益 -KEY_PB = 'pb' #市净率 -KEY_TimeToMarket = 'timeToMarket' - -#历史K线数据 -PRE_STOCK_KLINE = 'kline_' -#PRE_STOCK_KLINE_Sqlite = 'kline_' # sqlite 不支持 ：，改成_ -KEY_DATE = 'date' -KEY_OPEN = 'open' -KEY_HIGH = 'high' -KEY_CLOSE = 'close' -KEY_LOW = 'low' -KEY_VOLUME = 'volume' -KEY_AMOUNT = 'amount' - -STOCK_BASIC_TABLE = 'stock_basic_all' # 索引：获取所有股票 -STOCK_KLINE_TABLE = 'stock_kline_fq' # 索引：获取单只股票的所有日期的K线(前复权,后复权) #stock_kline_all - - -################################## - +#!/usr/local/bin/python +#coding=utf-8 +# 定义的常量 + +import os + +################################## +# Redis + +#基本信息表 +PRE_STOCK_BASIC = 'stock_basic_info:' #股票基本信息的前缀 +KEY_CODE = 'code' +KEY_NAME = 'name' +KEY_INDUSTRY = 'industry' +KEY_AREA = 'area' +KEY_PE = 'pe' #市盈率 +KEY_EPS = 'eps' #每股收益 +KEY_PB = 'pb' #市净率 +KEY_TimeToMarket = 'timeToMarket' + +#历史K线数据 +PRE_STOCK_KLINE = 'kline_' +#PRE_STOCK_KLINE_Sqlite = 'kline_' # sqlite 不支持 ：，改成_ +KEY_DATE = 'date' +KEY_OPEN = 'open' +KEY_HIGH = 'high' +KEY_CLOSE = 'close' +KEY_LOW = 'low' +KEY_VOLUME = 'volume' +KEY_AMOUNT = 'amount' + +STOCK_BASIC_TABLE = 'stock_basic_all' # 索引：获取所有股票 +STOCK_KLINE_TABLE = 'stock_kline_fq' # 索引：获取单只股票的所有日期的K线(前复权,后复权) #stock_kline_all + + +################################## + diff --git a/util/commons.pyc b/util/commons.pyc new file mode 100644 index 0000000..78cd4cd Binary files /dev/null and b/util/commons.pyc differ diff --git a/util/helper.py b/util/helper.py index 539d142..53a7a99 100644 --- a/util/helper.py +++ b/util/helper.py @@ -1,20 +1,20 @@ -#!/usr/local/bin/python -#coding=utf-8 - -__author__ = 'bbchen' - -import time -from functools import wraps - -# 统计函数耗时 -def fn_timer(function): - @wraps(function) - def function_timer(*args, **kwargs): - t0 = time.time() - result = function(*args, **kwargs) - t1 = time.time() - print ("Total time running %s: %s seconds" % - (function.func_name, str(t1-t0)) - ) - return result +#!/usr/local/bin/python +#coding=utf-8 + +__author__ = 'bbchen' + +import time +from functools import wraps + +# 统计函数耗时 +def fn_timer(function): + @wraps(function) + def function_timer(*args, **kwargs): + t0 = time.time() + result = function(*args, **kwargs) + t1 = time.time() + print ("Total time running %s: %s seconds" % + (function.func_name, str(t1-t0)) + ) + return result return function_timer \ No newline at end of file diff --git a/util/helper.pyc b/util/helper.pyc new file mode 100644 index 0000000..4ddc7ca Binary files /dev/null and b/util/helper.pyc differ diff --git a/util/send_mail.py b/util/send_mail.py index 21a6329..c31c266 100644 --- a/util/send_mail.py +++ b/util/send_mail.py @@ -1,49 +1,49 @@ -# -*- coding: utf8 -*- - -__author__ = 'Cbb' -from init import * - -def send_email_163(from_addr= 'cbbhust@163.com', - password = '12356789', - to_addrs = ('m18410182275@163.com'), - subject= 'Result', - content = None): - """This function use 163 email to send simple message.If success,return Ture,else return False - from_addr:should be 163 email adress - password:password of your email account - to_addrs:should be a tuple,like ('xxxxxx@163.com','xxxxxx@163.com') - subject:subject of your email - content:content of your email - """ - if content == None: - errorLogger.logger.error( 'content is None.') - return False - - try: - from smtplib import SMTP - from email.mime.text import MIMEText - - infoLogger.logger.info('begin send email...') - - email_client = SMTP(host = 'smtp.163.com') - email_client.login(from_addr, password) - - #create msg - msg = MIMEText(content, _charset='utf-8') - msg['Subject'] = subject - email_client.sendmail(from_addr, to_addrs, msg.as_string()) - - infoLogger.logger.info('send email success!') - return True - - except Exception, e: - errorLogger.logger.error(str(e)) - return False - finally: - email_client.quit() - -if __name__ == '__main__': - send_email_163(content='tttt') - - - +# -*- coding: utf8 -*- + +__author__ = 'Cbb' +from init import * + +def send_email_163(from_addr= 'cbbhust@163.com', + password = '12356789', + to_addrs = ('1627041882@qq.com'), + subject= 'Result', + content = None): + """This function use 163 email to send simple message.If success,return Ture,else return False + from_addr:should be 163 email adress + password:password of your email account + to_addrs:should be a tuple,like ('xxxxxx@163.com','xxxxxx@163.com') + subject:subject of your email + content:content of your email + """ + if content == None: + errorLogger.logger.error( 'content is None.') + return False + + try: + from smtplib import SMTP + from email.mime.text import MIMEText + + infoLogger.logger.info('begin send email...') + + email_client = SMTP(host = 'smtp.163.com') + email_client.login(from_addr, password) + + #create msg + msg = MIMEText(content, _charset='utf-8') + msg['Subject'] = subject + email_client.sendmail(from_addr, to_addrs, msg.as_string()) + + infoLogger.logger.info('send email success!') + return True + + except Exception, e: + errorLogger.logger.error(str(e)) + return False + finally: + email_client.quit() + +if __name__ == '__main__': + send_email_163(content='tttt') + + + diff --git a/util/send_mail.pyc b/util/send_mail.pyc new file mode 100644 index 0000000..8f8cff7 Binary files /dev/null and b/util/send_mail.pyc differ diff --git a/util/stockutil.py b/util/stockutil.py index 836e79f..44987c9 100644 --- a/util/stockutil.py +++ b/util/stockutil.py @@ -1,41 +1,41 @@ -#!/usr/local/bin/python -#coding=utf-8 -import time -from functools import wraps - -# 补全股票代码(6位股票代码) -# input: int or string -# output: string -def getSixDigitalStockCode(code): - strZero = '' - for _ in range(len(str(code)), 6): - strZero += '0' - return strZero + str(code) - -# 统计函数耗时 -def fn_timer(function): - @wraps(function) - def function_timer(*args, **kwargs): - t0 = time.time() - result = function(*args, **kwargs) - t1 = time.time() - print ("Total time running %s: %s seconds" % - (function.func_name, str(t1-t0)) - ) - return result - return function_timer - -# 将list分为多组 -# 例：>>> print group_list(range(10), 3) -# [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] -def group_list(li,block): - size = len(li) -# group=[] -# for i in range(0,size,block): -# group.append(li[i:i+block]) -# return group - return [li[i:i+block] for i in range(0,size,block)] - - - - +#!/usr/local/bin/python +#coding=utf-8 +import time +from functools import wraps + +# 补全股票代码(6位股票代码) +# input: int or string +# output: string +def getSixDigitalStockCode(code): + strZero = '' + for _ in range(len(str(code)), 6): + strZero += '0' + return strZero + str(code) + +# 统计函数耗时 +def fn_timer(function): + @wraps(function) + def function_timer(*args, **kwargs): + t0 = time.time() + result = function(*args, **kwargs) + t1 = time.time() + print ("Total time running %s: %s seconds" % + (function.func_name, str(t1-t0)) + ) + return result + return function_timer + +# 将list分为多组 +# 例：>>> print group_list(range(10), 3) +# [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] +def group_list(li,block): + size = len(li) +# group=[] +# for i in range(0,size,block): +# group.append(li[i:i+block]) +# return group + return [li[i:i+block] for i in range(0,size,block)] + + + + diff --git a/util/stockutil.pyc b/util/stockutil.pyc new file mode 100644 index 0000000..4d402f1 Binary files /dev/null and b/util/stockutil.pyc differ diff --git "a/\345\237\272\351\207\221\347\273\274\345\220\210\346\216\222\345\220\215\347\273\223\346\236\234.txt" "b/\345\237\272\351\207\221\347\273\274\345\220\210\346\216\222\345\220\215\347\273\223\346\236\234.txt" new file mode 100644 index 0000000..55e6f2c --- /dev/null +++ "b/\345\237\272\351\207\221\347\273\274\345\220\210\346\216\222\345\220\215\347\273\223\346\236\234.txt" @@ -0,0 +1,559 @@ +1 160222 国泰国证食品饮料行业指数分级 股票指数 100 100 5 15 4 44.8 +2 110022 易方达消费行业 股票型 100 100 19 9 2 46.0 +3 000248 汇添富中证主要消费ETF联接 联接基金 100 100 4 18 10 46.4 +4 260108 景顺长城新兴成长混合 混合型 100 100 10 11 17 47.6 +5 162605 景顺长城鼎益混合(LOF) 混合型 100 100 12 13 13 47.6 +6 161725 招商中证白酒指数分级 股票指数 77 100 56 5 1 47.8 +7 000619 东方红产业升级混合 混合型 100 100 23 19 11 50.6 +8 000083 汇添富消费行业混合 混合型 100 100 11 14 30 51.0 +9 260116 景顺长城核心竞争力混合A 混合型 100 100 20 22 24 53.2 +10 001740 光大中国制造2025混合 混合型 100 100 42 8 23 54.6 +11 070032 嘉实优化红利混合 混合型 100 100 13 10 51 54.8 +12 001112 东方红中国优势混合 混合型 100 100 43 20 14 55.4 +13 000462 农银主题轮动混合 混合型 100 100 3 25 52 56.0 +14 260110 景顺长城精选蓝筹混合 混合型 100 100 25 34 32 58.2 +15 001606 农银工业4.0混合 混合型 100 100 21 27 49 59.4 +16 002863 金信深圳成长混合发起式 混合型 1 1 100 100 100 60.4 +17 001208 诺安低碳经济股票 股票型 100 100 15 60 28 60.6 +18 519066 汇添富蓝筹稳健 混合型 100 100 44 17 42 60.6 +19 164908 交银中证环境治理(LOF) 股票指数 2 2 100 100 100 60.8 +20 110003 易方达上证50指数A 股票指数 100 100 32 51 21 60.8 +21 001387 中融新经济混合A 混合型 100 100 100 1 3 60.8 +22 501030 汇添富中证环境治理指数A 股票指数 3 4 100 100 100 61.4 +23 501031 汇添富中证环境治理指数C 股票指数 4 3 100 100 100 61.4 +24 001822 华商智能生活灵活配置混合 混合型 5 5 100 100 100 62.0 +25 550016 信诚至远C 分级杠杆 100 100 1 36 74 62.2 +26 519664 银河美丽混合A 混合型 100 100 18 33 60 62.2 +27 519068 汇添富成长焦点混合 混合型 100 100 46 21 44 62.2 +28 001542 国泰互联网+股票 股票型 100 100 100 7 5 62.4 +29 000800 华商未来主题混合 混合型 7 6 100 100 100 62.6 +30 630011 华商主题精选混合 混合型 10 8 100 100 100 63.6 +31 000654 华商新锐产业混合 混合型 12 9 100 100 100 64.2 +32 000742 国泰新经济灵活配置混合 混合型 100 100 100 16 7 64.6 +33 001076 易方达改革红利混合 混合型 100 100 14 40 70 64.8 +34 630005 华商动态阿尔法 混合型 13 12 100 100 100 65.0 +35 001170 泰达宏利复兴混合 混合型 100 100 26 39 61 65.2 +36 519665 银河美丽混合C 混合型 100 100 22 38 67 65.4 +37 481012 工银深证红利联接 联接基金 100 100 58 35 36 65.8 +38 160632 鹏华酒分级 股票指数 70 100 100 55 8 66.6 +39 001679 前海开源中国稀缺资产混合A 混合型 20 14 100 100 100 66.8 +40 519772 交银新生活力灵活配置混合 混合型 100 100 2 43 89 66.8 +41 001071 华安媒体互联网混合 混合型 9 27 100 100 100 67.2 +42 180012 银华富裕主题混合 混合型 100 100 100 30 6 67.2 +43 002079 前海开源中国稀缺资产混合C 混合型 21 16 100 100 100 67.4 +44 660010 农银策略精选混合 混合型 100 100 41 67 29 67.4 +45 165523 信诚中证信息安全指数分级 股票指数 8 30 100 100 100 67.6 +46 000690 前海开源大海洋混合 混合型 18 20 100 100 100 67.6 +47 040008 华安策略优选混合 混合型 100 100 100 12 26 67.6 +48 001287 安信优势增长混合A 混合型 100 100 8 58 73 67.8 +49 001281 长安鑫利优选混合A 混合型 100 100 51 26 62 67.8 +50 165312 建信央视50 股票指数 100 100 100 24 19 68.6 +51 169101 东方红睿丰混合 混合型 100 100 100 31 12 68.6 +52 519908 华夏兴华混合A 混合型 15 29 100 100 100 68.8 +53 002983 长信国防军工量化混合 混合型 19 25 100 100 100 68.8 +54 540012 汇丰晋信恒生龙头指数A 股票指数 100 100 82 47 15 68.8 +55 160643 鹏华中证空天一体(LOF) none 24 21 100 100 100 69.0 +56 002072 长安鑫利优选混合C 混合型 100 100 54 28 64 69.2 +57 001179 德邦大健康灵活配置混合 混合型 100 100 52 54 46 70.4 +58 160630 鹏华国防 股票指数 34 22 100 100 100 71.2 +59 519690 交银稳健配置混合A 混合型 100 100 73 23 63 71.8 +60 310388 申万菱信消费增长混合 混合型 100 100 64 29 68 72.2 +61 164304 新华中证环保产业指数分级 股票指数 26 37 100 100 100 72.6 +62 001712 东方红优势精选混合 混合型 100 100 84 45 35 72.8 +63 000173 汇添富美丽30混合 混合型 100 100 17 75 76 73.6 +64 161024 富国中证军工指数分级 股票指数 27 42 100 100 100 73.8 +65 001266 国投瑞银招财混合 保本型 53 17 100 100 100 74.0 +66 004347 南方中证500信息技术联接C 联接基金 40 32 100 100 100 74.4 +67 002984 广发环保指数C 股票指数 29 44 100 100 100 74.6 +68 005035 银华信息科技量化股票发起式A none 35 39 100 100 100 74.8 +69 001044 嘉实新消费股票 股票型 100 100 100 37 37 74.8 +70 165524 信诚中证智能家居指数分级 股票指数 32 43 100 100 100 75.0 +71 001064 广发环保指数A 股票指数 30 46 100 100 100 75.2 +72 002900 南方中证500信息技术联接A 联接基金 42 34 100 100 100 75.2 +73 005036 银华信息科技量化股票发起式C none 36 41 100 100 100 75.4 +74 110011 易方达中小盘混合 混合型 100 100 100 61 16 75.4 +75 217027 招商央视财经50指数A 股票指数 100 100 100 57 22 75.8 +76 163114 申万中证环保产业指数分级 股票指数 31 49 100 100 100 76.0 +77 241001 华宝海外中国成长混合 QDII 100 100 100 49 31 76.0 +78 002236 大成360互联网+大数据100A 股票指数 6 75 100 100 100 76.2 +79 161031 富国中证工业4.0指数分级 股票指数 28 53 100 100 100 76.2 +80 260103 景顺长城动力平衡 混合型 100 100 77 66 38 76.2 +81 004224 南方军工改革灵活配置混合 混合型 17 67 100 100 100 76.8 +82 001838 国投瑞银国家安全混合 混合型 71 13 100 100 100 76.8 +83 519746 交银丰享收益债券A 债券型 100 100 100 2 82 76.8 +84 001591 天弘中证环保产业C 股票指数 37 48 100 100 100 77.0 +85 002853 华富元鑫灵活配置混合A 混合型 100 100 100 3 83 77.2 +86 206007 鹏华消费优选混合 混合型 100 100 100 48 39 77.4 +87 160634 鹏华环保分级 股票指数 33 55 100 100 100 77.6 +88 001590 天弘中证环保产业A 股票指数 38 50 100 100 100 77.6 +89 004008 中融鑫思路混合A 混合型 100 100 100 4 84 77.6 +90 540009 汇丰晋信消费红利 股票型 100 100 100 64 25 77.8 +91 000423 前海开源事件驱动混合A 混合型 100 100 40 72 78 78.0 +92 004009 中融鑫思路混合C 混合型 100 100 100 6 85 78.2 +93 001865 前海开源事件驱动混合C 混合型 100 100 39 73 80 78.4 +94 360007 光大保德信优势配置混合 混合型 100 100 71 68 54 78.6 +95 001475 易方达国防军工混合 混合型 72 23 100 100 100 79.0 +96 257070 国联安优选行业混合 混合型 76 19 100 100 100 79.0 +97 005117 金信价值精选灵活配置混合A none 63 33 100 100 100 79.2 +98 210004 金鹰稳健成长混合 混合型 23 74 100 100 100 79.4 +99 002199 前海开源中证军工指数C 股票指数 46 52 100 100 100 79.6 +100 005118 金信价值精选灵活配置混合C none 62 36 100 100 100 79.6 +101 164906 交银中证海外中国互联网指数 QDII-指数 100 100 100 59 41 80.0 +102 519606 国泰金鑫 股票型 100 100 100 80 20 80.0 +103 161628 融通军工分级 股票指数 39 62 100 100 100 80.2 +104 502003 易方达军工分级 股票指数 43 60 100 100 100 80.6 +105 310398 申万菱信沪深300价值指数 股票指数 100 100 100 70 33 80.6 +106 164819 工银中证环保产业指数分级 股票指数 55 51 100 100 100 81.2 +107 001899 东海社会安全 股票指数 59 47 100 100 100 81.2 +108 001725 汇添富高端制造股票 股票型 100 100 6 100 100 81.2 +109 000596 前海开源中证军工指数A 股票指数 50 57 100 100 100 81.4 +110 003119 博时鑫源混合A 混合型 100 100 7 100 100 81.4 +111 163115 申万菱信中证军工指数分级 股票指数 49 59 100 100 100 81.6 +112 000061 华夏盛世混合 混合型 90 18 100 100 100 81.6 +113 003120 博时鑫源混合C 混合型 100 100 9 100 100 81.8 +114 001583 安信新常态股票 股票型 100 100 100 44 65 81.8 +115 110015 易方达行业领先混合 混合型 100 100 100 56 53 81.8 +116 160127 南方新兴消费分级 股票型 100 100 100 100 9 81.8 +117 004314 前海开源沪港深新硬件A 混合型 100 10 100 100 100 82.0 +118 000854 鹏华养老产业 股票型 100 100 90 62 58 82.0 +119 004315 前海开源沪港深新硬件C 混合型 100 11 100 100 100 82.2 +120 000835 华润元大富时中国A50指数 股票指数 100 100 93 78 40 82.2 +121 530015 建信深证60联接 联接基金 100 100 100 63 48 82.2 +122 519671 银河沪深300价值 股票指数 100 100 100 77 34 82.2 +123 020026 国泰成长优选混合 混合型 100 100 100 97 18 83.0 +124 002624 广发优企精选混合 混合型 100 100 16 100 100 83.2 +125 002851 南方品质混合 混合型 100 100 70 53 93 83.2 +126 003243 上投摩根中国世纪人民币 QDII 100 100 72 52 92 83.2 +127 161631 融通人工智能指数(LOF) 股票指数 48 69 100 100 100 83.4 +128 001703 银华沪港深增长股票 股票型 100 100 100 32 86 83.6 +129 020023 国泰事件驱动策略混合 混合型 100 100 100 94 27 84.2 +130 398061 中海消费混合 混合型 100 100 76 74 72 84.4 +131 501002 长信中证能源互联指数(LOF) 股票指数 78 45 100 100 100 84.6 +132 161033 富国中证智能汽车(LOF) 股票指数 100 24 100 100 100 84.8 +133 004528 长盛盛通纯债A none 100 100 24 100 100 84.8 +134 162107 金鹰量化精选股票(LOF) 股票型 14 111 100 100 100 85.0 +135 620004 金元顺安价值增长混合 混合型 22 103 100 100 100 85.0 +136 163117 申万菱信申万传媒分级 股票指数 25 100 100 100 100 85.0 +137 164508 国富中证100指数增强分级 股票指数 100 100 89 86 50 85.0 +138 000696 汇添富环保行业股票 股票型 68 58 100 100 100 85.2 +139 000698 宝盈科技30混合 混合型 111 15 100 100 100 85.2 +140 000751 嘉实新兴产业股票 股票型 100 100 100 71 55 85.2 +141 164402 前海开源中航军工 股票指数 56 71 100 100 100 85.4 +142 163412 兴全轻资产混合(LOF) 混合型 100 100 27 100 100 85.4 +143 002482 宝盈互联网沪港深混合 混合型 100 28 100 100 100 85.6 +144 519642 银河智造混合 混合型 100 100 28 100 100 85.6 +145 002121 广发沪港深新起点股票 股票型 100 100 100 41 87 85.6 +146 501019 国泰国证航天军工指数 股票指数 51 78 100 100 100 85.8 +147 398041 中海量化策略混合 混合型 65 64 100 100 100 85.8 +148 002251 华夏军工安全混合 混合型 89 40 100 100 100 85.8 +149 160628 鹏华地产分级 股票指数 100 100 29 100 100 85.8 +150 519008 汇添富优势精选 混合型 100 100 30 100 100 86.0 +151 002666 前海开源沪港深创新成长混合A 混合型 100 100 100 42 88 86.0 +152 320010 诺安中证100 股票指数 100 100 100 83 47 86.0 +153 004374 华泰保兴吉年丰混合发起A 混合型 100 100 31 100 100 86.2 +154 570001 诺德价值优势混合 混合型 100 100 100 76 56 86.4 +155 001605 国富沪港深成长精选股票 股票型 100 100 33 100 100 86.6 +156 167301 方正富邦保险主题指数分级 股票指数 100 100 100 90 43 86.6 +157 001528 诺安先进制造股票 股票型 100 100 34 100 100 86.8 +158 004375 华泰保兴吉年丰混合发起C 混合型 100 100 35 100 100 87.0 +159 161030 富国中证体育产业指数分级 股票指数 11 125 100 100 100 87.2 +160 002124 广发新兴产业混合 混合型 100 100 36 100 100 87.2 +161 350005 天治中国制造2025 混合型 100 100 75 84 77 87.2 +162 002667 前海开源沪港深创新成长混合C 混合型 100 100 100 46 90 87.2 +163 163411 兴全精选混合 保本型 100 100 37 100 100 87.4 +164 320017 诺安全球不动产 QDII 100 38 100 100 100 87.6 +165 481001 工银核心价值混合A 混合型 100 100 38 100 100 87.6 +166 001878 嘉实沪港深精选股票 股票型 100 100 100 50 91 88.2 +167 002189 农银汇理国企改革混合 混合型 100 100 55 89 98 88.4 +168 001371 富国沪港深价值混合 混合型 100 100 100 98 45 88.6 +169 519056 海富通内需热点混合 混合型 100 100 45 100 100 89.0 +170 161224 国投瑞银新丝路混合(LOF) 混合型 47 100 100 100 100 89.4 +171 001163 银华中国梦30股票 股票型 100 100 47 100 100 89.4 +172 001892 长盛新兴成长混合 混合型 100 100 48 100 100 89.6 +173 376510 上投大盘蓝筹 股票型 100 100 100 79 69 89.6 +174 001657 长安鑫富领先混合 混合型 100 100 49 100 100 89.8 +175 166006 中欧行业成长混合(LOF)A 混合型 100 100 100 92 57 89.8 +176 000127 农银行业领先混合 混合型 100 100 50 100 100 90.0 +177 519961 长信利广混合A 混合型 125 26 100 100 100 90.2 +178 310358 申万菱信新经济混合 混合型 100 100 100 85 66 90.2 +179 002230 华夏大中华混合(QDII) QDII 97 56 100 100 100 90.6 +180 001209 前海开源一带一路混合A 混合型 122 31 100 100 100 90.6 +181 002085 长盛互联网+混合 混合型 100 100 53 100 100 90.6 +182 002974 广发信息技术联接C 联接基金 44 110 100 100 100 90.8 +183 540008 汇丰晋信低碳先锋 股票型 100 54 100 100 100 90.8 +184 000011 华夏大盘精选 混合型 100 100 100 96 59 91.0 +185 000942 广发信息技术联接A 联接基金 45 112 100 100 100 91.4 +186 000634 富国天盛灵活配置混合 混合型 100 100 57 100 100 91.4 +187 160422 华安中证定向增发 股票指数 58 100 100 100 100 91.6 +188 002080 前海开源一带一路混合C 混合型 123 35 100 100 100 91.6 +189 004505 博时新兴消费主题混合 混合型 100 100 59 100 100 91.8 +190 001236 博时丝路主题股票A 股票型 100 100 60 100 100 92.0 +191 000179 广发美国房地产指数 QDII-指数 100 61 100 100 100 92.2 +192 000029 富国宏观策略 混合型 100 100 61 100 100 92.2 +193 519732 交银定期支付双息平衡混合 混合型 100 100 62 100 100 92.4 +194 001681 新华积极价值灵活配置混合 混合型 100 100 63 100 100 92.6 +195 001668 汇添富全球互联混合 QDII 100 100 100 69 94 92.6 +196 162203 泰达宏利稳定混合 混合型 100 100 100 88 75 92.6 +197 501005 汇添富中证精准医疗指数A 股票指数 16 148 100 100 100 92.8 +198 001790 国泰智能汽车股票 none 100 65 100 100 100 93.0 +199 100022 富国天瑞强势 混合型 100 100 65 100 100 93.0 +200 004357 南方智慧混合 混合型 100 100 66 100 100 93.2 +201 003017 广发中证军工ETF联接 联接基金 84 83 100 100 100 93.4 +202 519035 富国天博创新主题混合 混合型 100 100 67 100 100 93.4 +203 160141 南方道琼斯美国精选C none 100 68 100 100 100 93.6 +204 004897 长安鑫恒回报混合A none 100 100 68 100 100 93.6 +205 161726 招商国证生物医药指数分级 股票指数 41 128 100 100 100 93.8 +206 501010 汇添富中证生物科技指数C 股票指数 52 117 100 100 100 93.8 +207 501009 汇添富中证生物科技指数A 股票指数 54 115 100 100 100 93.8 +208 519170 浦银安盛增长动力混合 混合型 73 96 100 100 100 93.8 +209 164905 交银国证新能源指数分级 股票指数 106 63 100 100 100 93.8 +210 004183 富国产业升级混合 none 100 100 69 100 100 93.8 +211 160140 南方道琼斯美国精选A none 100 70 100 100 100 94.0 +212 000020 景顺长城品质投资混合 混合型 100 100 100 99 71 94.0 +213 000167 广发聚优灵活配置混合A 混合型 100 100 100 91 81 94.4 +214 004748 天弘策略精选混合C none 66 107 100 100 100 94.6 +215 004898 长安鑫恒回报混合C none 100 100 74 100 100 94.8 +216 004694 天弘策略精选混合A none 67 109 100 100 100 95.2 +217 002345 华夏高端制造混合 混合型 100 76 100 100 100 95.2 +218 003597 长盛盛腾混合C 混合型 100 100 100 81 95 95.2 +219 000535 长盛航天海工装备灵活配置 混合型 100 77 100 100 100 95.4 +220 002595 博时工业4.0主题股票 股票型 100 100 78 100 100 95.6 +221 001659 富安达新动力混合 混合型 100 100 100 82 96 95.6 +222 001723 华商新动力灵活配置混合 混合型 79 100 100 100 100 95.8 +223 161028 富国中证新能源汽车指数分级 股票指数 100 79 100 100 100 95.8 +224 450004 国富深化价值混合 混合型 100 100 79 100 100 95.8 +225 003154 华宝新活力混合 混合型 100 100 80 100 100 96.0 +226 160605 鹏华中国50 混合型 81 100 100 100 100 96.2 +227 003670 中融物联网主题灵活配置混合 混合型 100 81 100 100 100 96.2 +228 001714 工银文体产业股票 股票型 100 100 81 100 100 96.2 +229 004698 博时军工主题股票 none 100 82 100 100 100 96.4 +230 519702 交银趋势混合 混合型 100 100 83 100 100 96.6 +231 002980 华夏创新前沿股票 股票型 100 84 100 100 100 96.8 +232 002989 融通通乾研究精选混合 混合型 100 100 100 87 97 96.8 +233 001915 宝盈医疗健康沪港深股票 股票型 98 87 100 100 100 97.0 +234 000541 华商创新成长灵活配置 混合型 100 85 100 100 100 97.0 +235 001651 工银新蓝筹股票 股票型 100 100 85 100 100 97.0 +236 001457 华商新常态混合 混合型 100 86 100 100 100 97.2 +237 001518 万家瑞兴 混合型 100 100 86 100 100 97.2 +238 161122 易方达生物分级 股票指数 64 123 100 100 100 97.4 +239 001040 新华策略精选股票 股票型 100 100 87 100 100 97.4 +240 004477 嘉实沪港深回报混合 混合型 100 100 88 100 100 97.6 +241 005496 创金合信科技成长股票C none 100 90 100 100 100 98.0 +242 005495 创金合信科技成长股票A none 100 91 100 100 100 98.2 +243 160216 国泰商品 QDII 100 100 91 100 100 98.2 +244 164907 交银中证互联网金融指数分级 股票指数 92 100 100 100 100 98.4 +245 160225 国泰国证新能源汽车指数 股票指数 100 92 100 100 100 98.4 +246 519087 新华优选分红混合 混合型 100 100 92 100 100 98.4 +247 002214 中海沪港深价值优选混合 混合型 100 100 100 93 99 98.4 +248 501008 汇添富中证互联网医疗指数C 股票指数 93 100 100 100 100 98.6 +249 501007 汇添富中证互联网医疗指数A 股票指数 94 100 100 100 100 98.8 +250 160607 鹏华价值优势混合(LOF) 混合型 100 100 94 100 100 98.8 +251 161123 易方达重组分级 股票指数 95 100 100 100 100 99.0 +252 206002 鹏华精选成长混合 混合型 100 100 95 100 100 99.0 +253 003596 长盛盛腾混合A 混合型 100 100 100 95 100 99.0 +254 161232 国投瑞银瑞盛混合(LOF) 混合型 108 88 100 100 100 99.2 +255 100020 富国天益价值混合 混合型 100 100 96 100 100 99.2 +256 001986 前海开源人工智能主题混合 混合型 100 97 100 100 100 99.4 +257 001581 华安沪港深通精选灵活配置混合 混合型 100 100 97 100 100 99.4 +258 002142 博时外延增长主题灵活配置混合 混合型 100 100 98 100 100 99.6 +259 001600 天弘中证高端装备制造C 股票指数 127 72 100 100 100 99.8 +260 003750 创金合信鑫收益混合C 混合型 100 100 99 100 100 99.8 +261 003145 中融竞争优势 股票型 100 100 100 100 100 100.0 +262 001186 富国文体健康股票 股票型 100 100 100 100 100 100.0 +263 001599 天弘中证高端装备制造A 股票指数 128 73 100 100 100 100.2 +264 630001 华商领先企业 混合型 105 100 100 100 100 101.0 +265 000603 易方达创新驱动灵活配置混合 混合型 100 105 100 100 100 101.0 +266 004397 长盛信息安全量化策略混合 混合型 100 106 100 100 100 101.2 +267 161609 融通动力先锋混合 混合型 91 116 100 100 100 101.4 +268 000965 汇丰晋信新动力混合 混合型 100 108 100 100 100 101.6 +269 004752 广发中证传媒ETF联接A none 109 100 100 100 100 101.8 +270 502036 大成中证互联网金融指数分级 股票指数 110 100 100 100 100 102.0 +271 519110 浦银安盛价值成长混合A 混合型 86 127 100 100 100 102.6 +272 000220 富国医疗保健行业混合 混合型 100 113 100 100 100 102.6 +273 580005 东吴进取策略 混合型 100 114 100 100 100 102.8 +274 004753 广发中证传媒ETF联接C none 115 100 100 100 100 103.0 +275 165315 建信网金融分级 股票指数 118 100 100 100 100 103.6 +276 001730 兴银大健康 混合型 100 118 100 100 100 103.6 +277 260109 景顺长城内需贰号混合 混合型 74 145 100 100 100 103.8 +278 160629 鹏华传媒分级 股票指数 119 100 100 100 100 103.8 +279 070030 嘉实中创400联接 联接基金 87 134 100 100 100 104.2 +280 004394 华泰柏瑞量化创优混合 none 121 100 100 100 100 104.2 +281 673010 西部利得新动向混合 混合型 100 121 100 100 100 104.2 +282 001959 华商乐享互联灵活配置混合 混合型 100 122 100 100 100 104.4 +283 163415 兴全商业模式优选混合 混合型 124 100 100 100 100 104.8 +284 001037 国投瑞银锐意改革混合 混合型 100 124 100 100 100 104.8 +285 610005 信达澳银红利回报混合 混合型 82 144 100 100 100 105.2 +286 161037 富国中证高端制造指数增强型 股票指数 107 120 100 100 100 105.4 +287 210008 金鹰策略配置混合 混合型 57 172 100 100 100 105.8 +288 000778 鹏华先进制造股票 股票型 100 129 100 100 100 105.8 +289 005038 银华新能源新材料C none 100 130 100 100 100 106.0 +290 590008 中邮战略新兴产业混合 混合型 131 100 100 100 100 106.2 +291 005037 银华新能源新材料A none 100 132 100 100 100 106.4 +292 001322 东吴新趋势 混合型 226 7 100 100 100 106.6 +293 001279 中海积极增利混合 混合型 100 133 100 100 100 106.6 +294 001195 工银农业产业股票 股票型 136 100 100 100 100 107.2 +295 000788 前海开源中国成长混合 混合型 100 136 100 100 100 107.2 +296 003634 嘉实农业产业股票 股票型 137 100 100 100 100 107.4 +297 000586 景顺中小板创业板精选股票 股票型 100 138 100 100 100 107.6 +298 161910 万家中创指数分级 股票指数 100 139 100 100 100 107.8 +299 003769 中银品质生活混合 混合型 140 100 100 100 100 108.0 +300 004343 南方创业板ETF联接C 联接基金 141 100 100 100 100 108.2 +301 206011 鹏华美国房地产 QDII 100 141 100 100 100 108.2 +302 003975 中信建投睿泰灵活配置混合C 混合型 142 100 100 100 100 108.4 +303 002656 南方创业板ETF联接A 联接基金 143 100 100 100 100 108.6 +304 000534 长盛高端装备制造灵活配置 混合型 145 98 100 100 100 108.6 +305 002229 华夏经济转型股票 股票型 100 143 100 100 100 108.6 +306 003974 中信建投睿泰灵活配置混合A 混合型 144 100 100 100 100 108.8 +307 161223 国投瑞银中证创业指数分级 股票指数 100 146 100 100 100 109.2 +308 502056 广发医疗指数分级 股票指数 148 100 100 100 100 109.6 +309 160633 鹏华证券分级 股票指数 149 100 100 100 100 109.8 +310 540010 汇丰晋信科技先锋 股票型 100 149 100 100 100 109.8 +311 160637 鹏华创业板分级 股票指数 151 100 100 100 100 110.2 +312 000727 融通健康产业灵活配置混合 混合型 112 140 100 100 100 110.4 +313 161613 融通创业板指数增强 股票指数 152 100 100 100 100 110.4 +314 164818 工银中证传媒指数分级 股票指数 153 100 100 100 100 110.6 +315 004671 中融核心成长 none 100 153 100 100 100 110.6 +316 000062 银华量化智慧动力混合 混合型 154 100 100 100 100 110.8 +317 270028 广发制造业精选混合 混合型 100 156 100 100 100 111.2 +318 000279 华商红利优选灵活配置 混合型 75 182 100 100 100 111.4 +319 160419 华安中证全指证券公司指数分级 股票指数 157 100 100 100 100 111.4 +320 164401 前海开源健康分级 股票指数 101 157 100 100 100 111.6 +321 502010 易方达证券公司分级 股票指数 161 100 100 100 100 112.2 +322 519918 华夏兴和混合 混合型 100 161 100 100 100 112.2 +323 161720 招商中证全指证券公司指数分级 股票指数 162 100 100 100 100 112.4 +324 000969 前海开源大安全混合 混合型 100 162 100 100 100 112.4 +325 001809 中信建投智信物联网A 混合型 100 163 100 100 100 112.6 +326 110026 易方达创业板ETF联接A 联接基金 164 100 100 100 100 112.8 +327 161027 富国中证全指证券公司指数分级 股票指数 165 100 100 100 100 113.0 +328 163209 诺安中证创业成长指数分级 股票指数 100 166 100 100 100 113.2 +329 001166 建信环保产业股票 股票型 100 167 100 100 100 113.4 +330 002064 华富产业升级灵活配置混合 混合型 88 181 100 100 100 113.8 +331 164820 工银中证高铁产业指数分级 股票指数 134 135 100 100 100 113.8 +332 000574 宝盈新价值混合 混合型 169 100 100 100 100 113.8 +333 180031 银华中小盘混合 混合型 170 100 100 100 100 114.0 +334 162102 金鹰中小盘精选 混合型 100 170 100 100 100 114.0 +335 000598 长盛生态环境主题混合 混合型 100 171 100 100 100 114.2 +336 160224 国泰深证TMT50指数分级 股票指数 96 177 100 100 100 114.6 +337 162412 华宝中证医疗指数分级 股票指数 173 100 100 100 100 114.6 +338 000913 农银医疗保健股票 股票型 174 100 100 100 100 114.8 +339 163116 申万菱信申万电子分级 股票指数 185 89 100 100 100 114.8 +340 160626 鹏华信息分级 股票指数 85 190 100 100 100 115.0 +341 166019 中欧价值智选混合A 混合型 176 100 100 100 100 115.2 +342 502053 长盛中证证券公司分级 股票指数 177 100 100 100 100 115.4 +343 161629 融通证券分级 股票指数 178 100 100 100 100 115.6 +344 257020 国联安精选混合 混合型 100 178 100 100 100 115.6 +345 163113 申万菱信申万证券分级 股票指数 179 100 100 100 100 115.8 +346 001361 景顺中证TMT150ETF联接 联接基金 80 200 100 100 100 116.0 +347 000971 诺安新经济股票 股票型 180 100 100 100 100 116.0 +348 001294 新华战略新兴产业灵活配置混合 混合型 100 180 100 100 100 116.0 +349 001629 天弘中证计算机指数A 股票指数 60 221 100 100 100 116.2 +350 001630 天弘中证计算机指数C 股票指数 61 220 100 100 100 116.2 +351 260104 景顺长城内需增长混合 混合型 102 179 100 100 100 116.2 +352 001956 国联安科技动力 股票型 126 155 100 100 100 116.2 +353 004070 南方中证全指证券ETF联接C 联接基金 181 100 100 100 100 116.2 +354 165522 信诚中证TMT产业指数分级 股票指数 69 213 100 100 100 116.4 +355 004069 南方中证全指证券ETF联接A 联接基金 183 100 100 100 100 116.6 +356 501016 国泰中证申万证券行业指数 股票指数 184 100 100 100 100 116.8 +357 519029 华夏平稳增长 混合型 100 185 100 100 100 117.0 +358 005033 银华智能汽车量化股票发起式A none 100 186 100 100 100 117.2 +359 005207 南方高端装备灵活配置混合C none 186 101 100 100 100 117.4 +360 005266 博时厚泽回报混合C none 187 100 100 100 100 117.4 +361 000431 鹏华品牌传承混合 混合型 243 100 100 65 79 117.4 +362 005034 银华智能汽车量化股票发起式C none 100 187 100 100 100 117.4 +363 001877 宝盈国家安全沪港深股票 股票型 83 205 100 100 100 117.6 +364 002168 嘉实智能汽车股票 股票型 100 188 100 100 100 117.6 +365 005265 博时厚泽回报混合A none 189 100 100 100 100 117.8 +366 003765 广发创业板ETF联接A none 190 100 100 100 100 118.0 +367 003766 广发创业板ETF联接C none 191 100 100 100 100 118.2 +368 400015 东方增长中小盘混合 混合型 100 193 100 100 100 118.6 +369 000457 上投摩根核心成长 股票型 100 195 100 100 100 119.0 +370 001618 天弘中证电子指数C 股票指数 146 150 100 100 100 119.2 +371 160919 大成产业升级股票 股票型 100 196 100 100 100 119.2 +372 005310 广发电子信息传媒股票 none 100 197 100 100 100 119.4 +373 519097 新华中小市值优选混合 混合型 104 194 100 100 100 119.6 +374 420005 天弘周期策略混合 混合型 198 100 100 100 100 119.6 +375 001617 天弘中证电子指数A 股票指数 147 152 100 100 100 119.8 +376 202027 南方高端装备灵活配置混合A 混合型 195 104 100 100 100 119.8 +377 502030 中海中证高铁产业指数分级 股票指数 158 142 100 100 100 120.0 +378 161022 富国创业板指数分级 股票指数 200 100 100 100 100 120.0 +379 519095 新华行业周期轮换混合 混合型 175 126 100 100 100 120.2 +380 003647 创金合信中证1000指数增强C 股票指数 129 173 100 100 100 120.4 +381 004593 广发全指工业ETF联接 none 133 169 100 100 100 120.4 +382 001593 天弘创业板C 股票指数 202 100 100 100 100 120.4 +383 001321 兴业聚优灵活配置混合 混合型 100 202 100 100 100 120.4 +384 379010 上投摩根中小盘混合 混合型 100 203 100 100 100 120.6 +385 161810 银华内需精选混合(LOF) 混合型 100 204 100 100 100 120.8 +386 003646 创金合信中证1000指数增强A 股票指数 130 175 100 100 100 121.0 +387 001592 天弘创业板A 股票指数 205 100 100 100 100 121.0 +388 001983 中邮低碳配置混合 混合型 100 206 100 100 100 121.2 +389 166009 中欧新动力混合(LOF)A 混合型 100 207 100 100 100 121.4 +390 001210 天弘互联网混合 混合型 208 100 100 100 100 121.6 +391 001576 国泰智能装备股票 none 100 208 100 100 100 121.6 +392 160135 南方中证高铁产业指数分级 股票指数 150 160 100 100 100 122.0 +393 160223 国泰创业板指数(LOF) 股票指数 210 100 100 100 100 122.0 +394 470028 汇添富社会责任混合 混合型 100 210 100 100 100 122.0 +395 502026 鹏华新丝路分级 股票指数 211 100 100 100 100 122.2 +396 001795 上投摩根文体休闲灵活配置混合 混合型 100 211 100 100 100 122.2 +397 180013 银华领先策略混合 混合型 213 100 100 100 100 122.6 +398 002968 新华高端制造混合 none 100 214 100 100 100 122.8 +399 217019 招商深证TMT50ETF联接A 联接基金 132 183 100 100 100 123.0 +400 005235 银华食品饮料量化股票发起式A none 215 100 100 100 100 123.0 +401 000601 华宝创新优选混合 混合型 100 216 100 100 100 123.2 +402 160512 博时卓越品牌混合(LOF) 混合型 160 158 100 100 100 123.6 +403 001227 中邮信息产业灵活配置混合 混合型 218 100 100 100 100 123.6 +404 162607 景顺长城资源垄断混合 混合型 100 219 100 100 100 123.8 +405 005236 银华食品饮料量化股票发起式C none 220 100 100 100 100 124.0 +406 162411 华宝标普油气上游股票 QDII-指数 222 100 100 100 100 124.4 +407 001268 富国国家安全主题混合 混合型 223 100 100 100 100 124.6 +408 162216 泰达中证500分级 股票指数 225 100 100 100 100 125.0 +409 000031 华夏复兴混合 混合型 100 225 100 100 100 125.0 +410 163119 申万中证申万新兴健康产业指数 股票指数 103 223 100 100 100 125.2 +411 519704 交银先进制造混合 混合型 100 228 100 100 100 125.6 +412 161025 富国中证移动互联网指数分级 股票指数 193 137 100 100 100 126.0 +413 005239 银华文体娱乐量化股票发起式A none 230 100 100 100 100 126.0 +414 000063 长盛电子信息主题灵活配置混合 混合型 237 93 100 100 100 126.0 +415 110013 易方达科翔混合 混合型 100 231 100 100 100 126.2 +416 000219 博时裕益灵活配置 混合型 232 100 100 100 100 126.4 +417 002955 融通新趋势灵活配置混合 混合型 233 100 100 100 100 126.6 +418 519993 长信增利动态策略混合 混合型 234 99 100 100 100 126.6 +419 700001 平安大华行业先锋混合 混合型 235 100 100 100 100 127.0 +420 000925 汇添富外延增长主题 股票型 100 235 100 100 100 127.0 +421 160639 鹏华高铁分级 股票指数 172 164 100 100 100 127.2 +422 001349 富国改革动力混合 混合型 257 80 100 100 100 127.4 +423 519034 海富通中证内地低碳指数 股票指数 100 237 100 100 100 127.4 +424 004273 中融量化小盘股票C none 238 100 100 100 100 127.6 +425 001410 信达澳银新能源产业股票 股票型 100 238 100 100 100 127.6 +426 290005 泰信优势增长 混合型 166 174 100 100 100 128.0 +427 003096 中欧医疗健康混合C 混合型 240 100 100 100 100 128.0 +428 001193 中金消费升级 股票型 100 240 100 100 100 128.0 +429 121003 国投瑞银核心企业混合 混合型 239 102 100 100 100 128.2 +430 005240 银华文体娱乐量化股票发起式C none 241 100 100 100 100 128.2 +431 000522 华润元大信息传媒科技混合 混合型 100 241 100 100 100 128.2 +432 519026 海富通中小盘混合 混合型 100 244 100 100 100 128.8 +433 003095 中欧医疗健康混合A 混合型 245 100 100 100 100 129.0 +434 002250 红土创新改革红利混合 混合型 100 245 100 100 100 129.0 +435 003865 创金合信量化多因子股票C 股票型 113 233 100 100 100 129.2 +436 004272 中融量化小盘股票A none 246 100 100 100 100 129.2 +437 001458 广发中证全指主要消费联接A 联接基金 247 100 100 100 100 129.4 +438 001018 易方达新经济混合 混合型 100 247 100 100 100 129.4 +439 002976 广发中证全指主要消费联接C 联接基金 248 100 100 100 100 129.6 +440 001644 汇丰晋信智造先锋股票C 股票型 100 248 100 100 100 129.6 +441 001028 华安物联网主题股票 股票型 100 249 100 100 100 129.8 +442 001879 长城创新动力灵活配置混合 none 100 250 100 100 100 130.0 +443 004855 广发中证全指汽车指数C none 135 217 100 100 100 130.4 +444 001692 南方国策动力 股票型 100 252 100 100 100 130.4 +445 004854 广发中证全指汽车指数A none 138 215 100 100 100 130.6 +446 001643 汇丰晋信智造先锋股票A 股票型 100 253 100 100 100 130.6 +447 161825 银华中证800分级 股票指数 254 100 100 100 100 130.8 +448 420003 天弘永定价值成长混合 混合型 188 168 100 100 100 131.2 +449 001449 华商双驱优选混合 混合型 256 100 100 100 100 131.2 +450 001126 上投摩根卓越制造股票 股票型 100 256 100 100 100 131.2 +451 580001 东吴嘉禾优势精选 混合型 291 66 100 100 100 131.4 +452 003554 泰达宏利大数据混合C 混合型 258 100 100 100 100 131.6 +453 160640 鹏华新能源分级 股票指数 265 94 100 100 100 131.8 +454 002256 金信新能源汽车混合发起式 混合型 171 189 100 100 100 132.0 +455 002263 泰达宏利大数据混合A 混合型 260 100 100 100 100 132.0 +456 000684 长盛养老健康产业灵活配置 混合型 100 260 100 100 100 132.0 +457 002210 创金合信量化多因子股票A 股票型 120 242 100 100 100 132.4 +458 001735 广发百发大数据成长混合E 混合型 100 262 100 100 100 132.4 +459 410001 华富竞争力优选 混合型 263 100 100 100 100 132.6 +460 164821 工银中证新能源指数分级 股票指数 268 95 100 100 100 132.6 +461 519033 海富通国策导向混合 混合型 100 263 100 100 100 132.6 +462 502000 西部利得中证500等权重分级 股票指数 266 100 100 100 100 133.2 +463 163409 兴全绿色投资混合(LOF) 混合型 100 266 100 100 100 133.2 +464 000471 富国城镇发展股票 股票型 236 131 100 100 100 133.4 +465 160420 华安创业板50指数分级 股票指数 267 100 100 100 100 133.4 +466 001734 广发百发大数据成长混合A 混合型 100 268 100 100 100 133.6 +467 003956 南方教育股票 股票型 139 230 100 100 100 133.8 +468 000926 中信建投睿信灵活配置混合A 混合型 269 100 100 100 100 133.8 +469 580008 东吴新产业精选混合 混合型 100 269 100 100 100 133.8 +470 001733 泰达宏利量化股票 股票型 270 100 100 100 100 134.0 +471 161612 融通深证成份指数A 股票指数 271 100 100 100 100 134.2 +472 002564 新沃通盈灵活配置混合 混合型 253 119 100 100 100 134.4 +473 003101 诺德天禧债券 债券型 116 257 100 100 100 134.6 +474 004497 前海开源多元策略混合C 混合型 273 100 100 100 100 134.6 +475 162510 国联安双力中小板综指 股票指数 182 192 100 100 100 134.8 +476 004496 前海开源多元策略混合A 混合型 274 100 100 100 100 134.8 +477 004345 南方深证成份ETF联接C 联接基金 275 100 100 100 100 135.0 +478 004670 长盛分享经济主题灵活配置混合 none 100 275 100 100 100 135.0 +479 202017 南方深证成份ETF联接A 联接基金 276 100 100 100 100 135.2 +480 270008 广发核心精选混合 混合型 100 278 100 100 100 135.6 +481 005237 银华医疗健康量化优选A none 279 100 100 100 100 135.8 +482 004522 安信工业4.0灵活配置混合C none 156 224 100 100 100 136.0 +483 004905 华泰柏瑞生物医药混合 none 100 280 100 100 100 136.0 +484 160636 鹏华互联网分级 股票指数 227 154 100 100 100 136.2 +485 005238 银华医疗健康量化优选C none 281 100 100 100 100 136.2 +486 160106 南方高增长混合(LOF) 混合型 100 281 100 100 100 136.2 +487 160137 南方中证互联网指数分级 股票指数 282 100 100 100 100 136.4 +488 002076 浙商聚潮灵活配置 混合型 285 100 100 100 100 137.0 +489 003715 宝盈消费主题混合 混合型 100 285 100 100 100 137.0 +490 004065 中融量化多因子混合A 混合型 114 272 100 100 100 137.2 +491 004435 博时逆向投资混合C 混合型 286 100 100 100 100 137.2 +492 167001 平安大华鼎泰混合(LOF) 混合型 100 286 100 100 100 137.2 +493 000985 嘉实逆向策略股票 股票型 288 100 100 100 100 137.6 +494 001797 华融新利灵活配置混合 混合型 100 288 100 100 100 137.6 +495 004434 博时逆向投资混合A 混合型 289 100 100 100 100 137.8 +496 001042 华夏领先股票 股票型 100 289 100 100 100 137.8 +497 004785 中融量化多因子混合C none 117 273 100 100 100 138.0 +498 002907 南方中证500量化增强C 股票指数 290 100 100 100 100 138.0 +499 002692 富国创新科技混合 混合型 100 291 100 100 100 138.2 +500 260115 景顺长城中小盘混合 混合型 194 198 100 100 100 138.4 +501 001815 华泰柏瑞激励动力混合A 混合型 292 100 100 100 100 138.4 +502 590005 中邮核心主题混合 混合型 293 100 100 100 100 138.6 +503 003292 嘉实优势成长混合 混合型 100 293 100 100 100 138.6 +504 005283 中金价值轮动混合C none 155 239 100 100 100 138.8 +505 164823 工银瑞信深证成份指数(LOF) none 294 100 100 100 100 138.8 +506 004195 招商中证1000指数增强C 股票指数 99 296 100 100 100 139.0 +507 206010 鹏华深证民营ETF联接 联接基金 295 100 100 100 100 139.0 +508 519150 新华优选消费混合 混合型 100 295 100 100 100 139.0 +509 470006 汇添富医药保健混合 混合型 167 229 100 100 100 139.2 +510 003598 华商润丰灵活配置混合 混合型 197 199 100 100 100 139.2 +511 001561 天弘中证移动互联网C 股票指数 249 147 100 100 100 139.2 +512 000711 嘉实医疗保健股票 股票型 297 100 100 100 100 139.4 +513 000549 华安大国新经济股票 股票型 100 297 100 100 100 139.4 +514 168001 国寿安保中证养老产业指数分级 股票指数 298 100 100 100 100 139.6 +515 000796 宝盈睿丰创新混合C 混合型 100 298 100 100 100 139.6 +516 502016 长信中证一带一路主题指数分级 股票指数 299 100 100 100 100 139.8 +517 001313 上投摩根智慧互联股票 股票型 100 299 100 100 100 139.8 +518 004194 招商中证1000指数增强A 股票指数 100 300 100 100 100 140.0 +519 165525 信诚中证基建工程指数LOF 股票指数 300 100 100 100 100 140.0 +520 001560 天弘中证移动互联网A 股票指数 250 151 100 100 100 140.2 +521 004521 安信工业4.0灵活配置混合A none 168 234 100 100 100 140.4 +522 000523 国投瑞银医疗保健混合 混合型 228 176 100 100 100 140.8 +523 005282 中金价值轮动混合A none 159 246 100 100 100 141.0 +524 519956 长信睿进混合C 混合型 207 212 100 100 100 143.8 +525 161616 融通医疗保健行业混合 混合型 259 165 100 100 100 144.8 +526 162201 泰达宏利成长混合 混合型 217 222 100 100 100 147.8 +527 003318 景顺中证500行业中性低波动 股票指数 163 277 100 100 100 148.0 +528 660011 农银中证500 股票指数 199 243 100 100 100 148.4 +529 001645 国泰大健康股票 股票型 287 159 100 100 100 149.2 +530 519957 长信睿进混合A 混合型 229 218 100 100 100 149.4 +531 001702 东方创新科技混合 混合型 264 184 100 100 100 149.6 +532 000008 嘉实中证500ETF联接 联接基金 192 258 100 100 100 150.0 +533 001039 嘉实先进制造股票 股票型 262 191 100 100 100 150.6 +534 004348 南方中证500ETF联接C 联接基金 203 259 100 100 100 152.4 +535 001052 华夏中证500ETF联接 联接基金 209 255 100 100 100 152.8 +536 001513 易方达信息产业混合 混合型 255 209 100 100 100 152.8 +537 160119 南方中证500ETF联接A 联接基金 204 261 100 100 100 153.0 +538 160616 鹏华中证500 股票指数 196 274 100 100 100 154.0 +539 162711 广发中证500ETF联接A 联接基金 216 254 100 100 100 154.0 +540 002903 广发中证500ETF联接C 联接基金 219 251 100 100 100 154.0 +541 501037 汇添富中证500C none 201 276 100 100 100 155.4 +542 001214 华泰柏瑞中证500ETF联接 联接基金 212 270 100 100 100 156.4 +543 001709 华富物联世界灵活配置混合 混合型 283 201 100 100 100 156.8 +544 001241 国寿安保中证500ETF联接 联接基金 214 271 100 100 100 157.0 +545 501036 汇添富中证500A none 206 282 100 100 100 157.6 +546 000962 天弘中证500指数 股票指数 224 264 100 100 100 157.6 +547 001069 华泰柏瑞消费成长混合 混合型 261 236 100 100 100 159.4 +548 004536 嘉实中小企业量化活力混合 none 280 227 100 100 100 161.4 +549 000059 国联安中证医药100 股票指数 244 265 100 100 100 161.8 +550 270026 广发中小板300联接 联接基金 277 232 100 100 100 161.8 +551 570007 诺德优选30混合 混合型 284 226 100 100 100 162.0 +552 164809 工银中证500分级 股票指数 221 294 100 100 100 163.0 +553 001455 景顺长城中证500ETF联接 联接基金 231 292 100 100 100 164.6 +554 202025 南方上证380联接 联接基金 242 287 100 100 100 165.8 +555 001551 天弘中证医药100C 股票指数 251 283 100 100 100 166.8 +556 001550 天弘中证医药100A 股票指数 252 284 100 100 100 167.2 +557 070021 嘉实主题新动力混合 混合型 278 267 100 100 100 169.0 +558 001351 诺安中证500ETF联接 联接基金 272 290 100 100 100 172.4 +559 519929 长信电子信息行业量化混合 混合型 296 279 100 100 100 175.0 diff --git "a/\345\237\272\351\207\221\347\273\274\345\220\210\346\216\222\345\220\215\347\273\223\346\236\2342.txt" "b/\345\237\272\351\207\221\347\273\274\345\220\210\346\216\222\345\220\215\347\273\223\346\236\2342.txt" new file mode 100644 index 0000000..7f52421 --- /dev/null +++ "b/\345\237\272\351\207\221\347\273\274\345\220\210\346\216\222\345\220\215\347\273\223\346\236\2342.txt" @@ -0,0 +1,6 @@ +1 164906 交银中证海外中国互联网指数 QDII-指数 1 2 1.5 +2 241001 华宝海外中国成长混合 QDII 2 3 2.5 +3 000934 国富大中华精选混合 QDII 6 1 3.5 +4 003243 上投摩根中国世纪人民币 QDII 3 6 4.5 +5 118001 易方达亚洲精选 QDII 4 5 4.5 +6 000988 嘉实全球互联网股票人民币 QDII 5 4 4.5