From c362a8f79844b3a333829b7282305ba2bff30cda Mon Sep 17 00:00:00 2001 From: lishulincug <1627041882@qq.com> Date: Wed, 17 Jan 2018 11:36:01 +0800 Subject: [PATCH 01/67] Create .travis.yml --- .travis.yml | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 .travis.yml diff --git a/.travis.yml b/.travis.yml new file mode 100644 index 0000000..a50b693 --- /dev/null +++ b/.travis.yml @@ -0,0 +1,8 @@ +language: python +python: + - "3.5" +# command to install dependencies +install: "pip install -r requirements.txt" +# command to run tests +script: + - python3 ./trade_process/live_trade.py From 973e9bb904e302e4387ec96a45af467896fb1c60 Mon Sep 17 00:00:00 2001 From: lishulincug <1627041882@qq.com> Date: Wed, 17 Jan 2018 21:11:52 +0800 Subject: [PATCH 02/67] Add files via upload MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 组合策略 --- BP.py | 386 ++--- BP.pyc | Bin 0 -> 4716 bytes README.md | 6 +- cleastsq.py | 228 +-- cleastsq.pyc | Bin 0 -> 2978 bytes config.ini | 20 +- cwavelet.py | 256 ++-- cwavelet.pyc | Bin 0 -> 2468 bytes data_process/Stock.py | 176 +-- data_process/Stock.pyc | Bin 0 -> 3301 bytes data_process/__init__.py | 10 +- data_process/__init__.pyc | Bin 0 -> 313 bytes data_process/data_calcute.py | 310 ++-- data_process/data_calcute.pyc | Bin 0 -> 3930 bytes data_process/data_download.py | 590 ++++---- data_process/data_get.py | 236 ++-- data_process/data_get.pyc | Bin 0 -> 3331 bytes data_process/download_stock.py | 407 +++--- data_process/download_stock.pyc | Bin 0 -> 4923 bytes data_process/get_all_china_stock_code.py | 394 +++--- data_process/native_data.py | 66 +- data_process/native_data.pyc | Bin 0 -> 870 bytes data_process/online_data.py | 297 ++-- data_process/online_data.pyc | Bin 0 -> 4135 bytes init.py | 159 ++- init.pyc | Bin 0 -> 4154 bytes k-line.py | 72 +- livedata.py | 414 +++--- new_trade/main.py | 104 +- new_trade/strategy_ma.py | 71 +- plot.py | 414 +++--- plot.pyc | Bin 0 -> 4990 bytes requirements.txt | 16 + shedule/trade_daily.bat | 2 +- stock.py | 474 +++---- stock_trader.py | 21 + trade_process/__init__.py | 2 +- trade_process/__init__.pyc | Bin 0 -> 168 bytes trade_process/efund_mail2.py | 565 ++++++++ trade_process/efund_mail2.pyc | Bin 0 -> 14798 bytes trade_process/fibonacci.py | 32 +- trade_process/fund_zf.py | 274 ++++ trade_process/fund_zf.pyc | Bin 0 -> 6073 bytes trade_process/fundlist-2017-03-27.txt | 1 + trade_process/kline_technical_index.py | 504 +++---- trade_process/live_trade.py | 296 ++-- trade_process/requirements.txt | 10 + trade_process/select_stock_center.py | 148 +- trade_process/strategy/__init__.py | 8 +- trade_process/strategy/__init__.pyc | Bin 0 -> 323 bytes trade_process/strategy/macd_back_test.py | 1253 +++++++++-------- trade_process/strategy/macd_back_test.pyc | Bin 0 -> 14063 bytes trade_process/strategy/macd_live_test.py | 504 +++---- trade_process/strategy/macd_live_test.pyc | Bin 0 -> 6728 bytes trade_process/strategy/stop_loss.py | 730 +++++----- trade_process/strategy/stop_loss.pyc | Bin 0 -> 9831 bytes trade_process/strategy/strategy_macd.py | 130 +- trade_process/strategy/strategy_macd.pyc | Bin 0 -> 1972 bytes trade_process/strategy/test.py | 214 +-- trade_process/strategy/tread_tracking.py | 1099 ++++++++------- trade_process/strategy/tread_tracking.pyc | Bin 0 -> 11195 bytes trade_process/strategy/turtletrader.py | 20 +- ...2\345\220\215\347\273\223\346\236\234.txt" | 174 +++ util/MyLogger.py | 176 +-- util/MyLogger.pyc | Bin 0 -> 2314 bytes util/__init__.py | 2 +- util/__init__.pyc | Bin 0 -> 247 bytes util/codeConvert.py | 124 +- util/codeConvert.pyc | Bin 0 -> 2967 bytes util/commons.py | 74 +- util/commons.pyc | Bin 0 -> 813 bytes util/helper.py | 38 +- util/helper.pyc | Bin 0 -> 755 bytes util/send_mail.py | 98 +- util/send_mail.pyc | Bin 0 -> 1597 bytes util/stockutil.py | 82 +- util/stockutil.pyc | Bin 0 -> 1267 bytes 77 files changed, 6429 insertions(+), 5258 deletions(-) create mode 100644 BP.pyc create mode 100644 cleastsq.pyc create mode 100644 cwavelet.pyc create mode 100644 data_process/Stock.pyc create mode 100644 data_process/__init__.pyc create mode 100644 data_process/data_calcute.pyc create mode 100644 data_process/data_get.pyc create mode 100644 data_process/download_stock.pyc create mode 100644 data_process/native_data.pyc create mode 100644 data_process/online_data.pyc create mode 100644 init.pyc create mode 100644 plot.pyc create mode 100644 requirements.txt create mode 100644 stock_trader.py create mode 100644 trade_process/__init__.pyc create mode 100644 trade_process/efund_mail2.py create mode 100644 trade_process/efund_mail2.pyc create mode 100644 trade_process/fund_zf.py create mode 100644 trade_process/fund_zf.pyc create mode 100644 trade_process/fundlist-2017-03-27.txt create mode 100644 trade_process/requirements.txt create mode 100644 trade_process/strategy/__init__.pyc create mode 100644 trade_process/strategy/macd_back_test.pyc create mode 100644 trade_process/strategy/macd_live_test.pyc create mode 100644 trade_process/strategy/stop_loss.pyc create mode 100644 trade_process/strategy/strategy_macd.pyc create mode 100644 trade_process/strategy/tread_tracking.pyc create mode 100644 "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" create mode 100644 util/MyLogger.pyc create mode 100644 util/__init__.pyc create mode 100644 util/codeConvert.pyc create mode 100644 util/commons.pyc create mode 100644 util/helper.pyc create mode 100644 util/send_mail.pyc create mode 100644 util/stockutil.pyc 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 0000000000000000000000000000000000000000..172f6b9588abfd09008226536beca93525d68849 GIT binary patch literal 4716 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zjcdr9V1g&=0#3EGLY^}qvmrI)*Ri8se%OD?ZSD`3ygzKbgH<$4- z;Pf3R9g^SO`BG@-%Dl)+Ligt%;5$i>LcysQ3k82j?)xil-FM3uFtV8wtr5!wx#If0 z@5ZH#=EcXzsaL}(w%)fPrc=0N&VWQosM%EoW9;n3a;eo+f)iN34V zft<9%uN{XKeuHee<&|p^Oj|IPF&T~sz8VDPPP}AXTy*QUFTt{>xFy#dsW1sg401BU z@~O!z78^len>$z!d+e$be^_~9c$eyzH|j^Y|1)b-P;t0gcUrD`k~MorKnyLb_48lM xh^2?pgXu&%o*wDf`?YLrR9j3K7{Z1&pk=gaJ%i8WXlfTelzsZBHmvW0 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 0000000000000000000000000000000000000000..5448e13223b6b48fcfa5a607c4007fe60a30e33e GIT binary patch literal 2468 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HcmV?d00001 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 0000000000000000000000000000000000000000..78d42042cc86d716914bfa7e797d46980b2e8729 GIT binary patch literal 3301 zcmb_eX>Su}5T0GfPVC$v2MOW6TvQ@EX#k3bALcP&%BP4pwJJc-rdLV%)I*^^UTb?k-usa z5C1%!$x-Q7!~X~P3C=KBd~4(qjcAn7NQ11CtJBF+E5fZ#veuKefvk;WZ6a$kSzE~3 zO4c^Awv)Ak3e}XUAgi6Mon&>;C89~94>D1*)=_^l$&6JpaS>vh9t1Vw-+`cjn5EGk1QOemYgQrPE@Y`TWNG2^{V$COwBy~eeUYi^po#Ow$$G4-XOti znST1y^piWYU;lBiY}wZ%Z!LWFeD=}3`JXS&TzMk4o>ZzQtwx-^aee;r{kf^zv)4Xd zoGra)ue_&*Tln&`#o6|x(y3Hw2fUJ7*XJ%jShS^kQ|Z0kK_#z5&2%V?a=SnDbHimu zb=Dl>C-{cJCJ@Q&Wpu#^2nIcj%m`yluu`y!3fwa;m;m*eYQY+^YiXzsL}qHquBV{} z!Fp&Wd60VP)+hlKp-Zf3BC7)2XV#Dvl}0mJF=@1r(VxT~LVhrp`Z@T^VfA=mKg>_y za!_!`2@MnRf`_A2Jgc$D%WIz?GBsix(gmdxlo7mOMO2Rp5h|1r6(OpGmmTG0NR%K}`cU`L#?PW!WQmiX0I+bF5S+PMWHj1J&dL4P3i5yGGP3-kP2Plj< zNJ)&QCSEr4b~8=w`?4p<9V z2j~E-2XrzdFlDtaA^nd-EZ?%T?!gg%jO`d0;>oe1=by8S2Ft2&g$~b)9iJH+f5$mzANTUU 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* +from data_process.native_data import * +from data_process.download_stock import * + diff --git a/data_process/__init__.pyc b/data_process/__init__.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d70c7b721620e822282b4cbfc8c1a4786ab2e756 GIT binary patch literal 313 zcmZSn%*(axc3f040~9a;X$K%K)&UYJK*Y$9!@v*%Wii58OmG%6kj2ChticS_kog}7 zG#G)b5)h%~r@;bZ3jv9g#FE7Lf};H7)Z${j;FA30>=Frd;rzUu%)HcikWx&^yu^~s zGN`09y3UmR^1Ph<#FY4AkjWZAX$AyQYz*YOTE!R`dxpfMmFA@wg2@=LT3w?UWT(Z% k$7kkcmc+;F6;zgR0QK4A=BJeAq}qY}Q_Kb=xEMhY01kms3jhEB literal 0 HcmV?d00001 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 0000000000000000000000000000000000000000..e74a3d1e014a92a3b99ee70bd68ae3078d2bbb2a GIT binary patch literal 3930 zcmb_f-EJG#5uUT8s2@`zMMupzXO5iZ#Lvl<5o7aYDTb9P@$*WIwiKjU7;m4J z=FAwMmFC z#h({{LHv@O%t*5$ep%kY{w47j`OhwkUy(OCX|9OBq)WHOUsj|lwk&5*jiv9)70z1~ 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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 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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 0000000000000000000000000000000000000000..ae342107cf8703d2ffc3ca989ff69372b5f9f3d3 GIT binary patch literal 4923 zcmb_fU2hy$8Gg^KybJEL8Bfp<7Xui1LcS;&l<8PFtQ5xIR(wG)+Ql7)18S%2> zEr^$6_L<1T#;o{r;^k%Ak@zp-6~rrwhk?)^-OyoHJTy2a-n?uUrEyIB8S#(FGb5(2 zGp$!%z^sFR;SWj%!-p0IJ!5?y%GA9*qhRHD` z<%h48d#{zfrSjd?@;j^Ly5+=oT3b<+oPhPmAAj)auYU9CN8kJ8yFdBUkG}uO`@i_} z`#)c;Orgp;NTNiSbIBF>ySms>I=Gv}1dv~lT6n-%;KhF`;z+(Y7n z9!C6>njkBHlVP@-A#4D>=O8Wsq5ymjLQjrK1x96+UBHU+3M6$?V5C4G2HN}>zs}{n 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-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 0000000000000000000000000000000000000000..7e1acec6d1be5759f13304ffa157912ecfd27123 GIT binary patch literal 870 zcmb`E&u$Yj5XQ&eZ6Qmb2S7P+;kXwnC=v%$Rj8%>xkSj}6s@op+oef2yV2T-6e&HG zXX#7v7=3{9jk^il!CKFb$DYrg-y}B=v+G~q#)i5f@qdTuk6|o6K^4(>>kAqSvXXux zD9I+#Nyw#SQ@W7U9+BOlkDuz8I?8t;Gr7eW8kSBm6U-CLcts#uLr~CQO*DY;0Gd~l zE+qv)Q%P${SAs4RcBa@O66=Jf>Bbx7tU2@ow(#ul8^1&@QJe3;297mzS4U@7mEXfX zL~!V!btdo;Q#IbjZQryZ3Wf}E9~yI^O=DfSkEJa``K1i!fh##XSFn_Cjei68^lkC- z_2;j}*`l_uqAjAxi-Ur*^}KD2^S-FdFj+dyvHkffJVojc7f$^0JtD`e(Y%@jzN&`} z&rc!oOzeqFWQpO@nBkTqU(6gMa%Lws>_lV-h(Ot~1I3P=q*2b54DSkI5Q?=G2NkO; zMyGfq=jDdmu$(!?K>j0vCy@J_z=uUuF@VR2cL4Tw?*PPJXgw<@wbuRt4lu^&1|4=? y%ejKa2Y$3)dBqeJAM8?V6rlB=) 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 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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 +#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' \ No newline at end of file diff --git a/init.pyc 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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 0000000000000000000000000000000000000000..a48fe3e661c10a5f17d70424e6bfce5fd2f1d11a GIT binary patch literal 4990 zcmb7HZ){uD6+ibmaS|tv?Y1H5KN<=JccaWr>7RA1gC(d%s!I2Y2(nT<$L~3L>5HBC zJvV7*YeO9qFespnX)sVVKva_^ZA=C~ z@9N6OH`w3RmG6$^cSrX3bme**7 zJ(AxhE@I8d*5fDK5#^JzwJo}2i8;OeshO^5mYm6c32Ec~2Y;us25cAu@vCzl8{Xgg 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+wrapcache==1.0.8 +numpy==1.13.3 +BeautifulSoup==3.2.1 +beautifulsoup4==4.6.0 +scrapy==1.5.0 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..404a22e 100644 --- a/stock.py +++ b/stock.py @@ -1,237 +1,237 @@ -#!/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..c5140ea --- /dev/null +++ b/stock_trader.py @@ -0,0 +1,21 @@ +#coding=utf-8 +from trade_process.strategy import main +from trade_process.strategy.tread_tracking import stock_trader_main +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 = [['002963', 'egold'], ['003321', 'eoil'], ['004744', 'eGEI'], ['110003', 'eSSE50'], ['110020', 'HS300'], + ['110031', 'eHSI'], ['161130', 'eNASDAQ100'], ['110028', 'anxinB'], ['110022', 'eConsumption '], + ['161125', 'SPX500']] + #均线策略 + main(ChinaStockIndexList) + stock_trader_main(code) \ 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 0000000000000000000000000000000000000000..a67d3a0b3e804de9defaba7dcab7048111c981a1 GIT binary patch literal 168 zcmZSn%*(axc3f040~9a(.*?)') + 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[:-200:-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[:5:1]] + change = [x[3] for x in funddata[:5:1]] + 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)>=0.5): + sell_point.append({'mean5up':round(sum(change)/len(change),5)}) + elif(sum(change)/len(change)<=-0.5): + 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':big}) + # 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':small}) + 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']] + 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 = [] + for i in code: + # save(strfundcode=i ,numdays=365*1) + sb=fenxi(strfundcode=i, numdays=425 * 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=425 * 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 = 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): + if(H == "14" and int(M) == 35 and S == "20"):#(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(): + a=1 + 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 14:35 '+'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') + now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) + H = now[9:11] + M = now[12:14] + S = now[15:] + all_fund_list = main_zf() + 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 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) + 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') + # 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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..aebd6b5 --- /dev/null +++ b/trade_process/fund_zf.py @@ -0,0 +1,274 @@ +# !/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(): + # 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=20) + 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('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 = [3, 6,12, 24] + all_fund_list = [] + + for int_month in month_list: + try: + if int_month == 24: + # 1年增幅 + print 'get nearly 2 year top 50 funds' + url = 'http://fund.eastmoney.com/data/rankhandler.aspx?op=ph&dt=kf&ft=all&rs=1nzf,100&gs=0&sc=2nzf&st=desc&sd=' + \ + strsdate + '&ed=' + strs20date + '&qdii=&tabSubtype=,,,,,&pi=1&pn=200&dx=1' + elif int_month == 12: + # 1年增幅 + print 'get nearly 1 year top 50 funds' + url = 'http://fund.eastmoney.com/data/rankhandler.aspx?op=ph&dt=kf&ft=all&rs=1nzf,100&gs=0&sc=1nzf&st=desc&sd=' + \ + strsdate + '&ed=' + strs20date + '&qdii=&tabSubtype=,,,,,&pi=1&pn=200&dx=1' + elif int_month == 36: + # 1年增幅 + print 'get nearly 3 year top 50 funds' + url = 'http://fund.eastmoney.com/data/rankhandler.aspx?op=ph&dt=kf&ft=all&rs=1nzf,100&gs=0&sc=3nzf&st=desc&sd=' + \ + strsdate + '&ed=' + strs20date + '&qdii=&tabSubtype=,,,,,&pi=1&pn=200&dx=1' + elif int_month == 60: + # 1年增幅 + print 'get nearly 1 year top 50 funds' + url = 'http://fund.eastmoney.com/data/rankhandler.aspx?op=ph&dt=kf&ft=all&rs=1nzf,100&gs=0&sc=5nzf&st=desc&sd=' + \ + strsdate + '&ed=' + strs20date + '&qdii=&tabSubtype=,,,,,&pi=1&pn=200&dx=1' + elif int_month == 0.25: + # 1年增幅 + print 'get nearly 1 week top 50 funds' + url = 'http://fund.eastmoney.com/data/rankhandler.aspx?op=ph&dt=kf&ft=all&rs=zzf,100&gs=0&sc=zzf&st=dasc&sd=' + \ + strsdate + '&ed=' + strs20date + '&qdii=&tabSubtype=,,,,,&pi=1&pn=200&dx=1' + 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=all&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=1' + 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 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 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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..1f22bfe 100644 --- a/trade_process/live_trade.py +++ b/trade_process/live_trade.py @@ -1,152 +1,144 @@ -#!/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 +logging.basicConfig() +# import errorLogger +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" 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/strategy/__init__.py b/trade_process/strategy/__init__.py index 65d9a01..699f4ff 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 0000000000000000000000000000000000000000..b3adedf5807b0458a512105670244ff8e1b7a63e GIT binary patch literal 323 zcmZSn%*(axc3f040~9a;X$K%KRss?!K*Y$9!@v*%Wii58Oh6VBL$C%jP+8`GAkbh0 zvPwXNmLFKOL=_}Zl$eqlUr>~voLXG0R}AEqq^4KuuMEaVC)$ZlUACSVhAQPO2Tqm&GhVf{T%h5dbnWT4MkJ literal 0 HcmV?d00001 diff --git a/trade_process/strategy/macd_back_test.py b/trade_process/strategy/macd_back_test.py index 2be2045..48b4367 100644 --- a/trade_process/strategy/macd_back_test.py +++ b/trade_process/strategy/macd_back_test.py @@ -1,609 +1,646 @@ -#!/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' +#!/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 +from data_process.download_stock import downloadAllHistoryAShareData + +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' +def main(stockList): + print "main begin" + # stockList = ['000725', '000783', '002167', '002505', '002600', '300315', '600000', '600011', '600048', '601001'] + downloadAllHistoryAShareData(stockList) + # 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" +if __name__ == "__main__": + print "main begin" + stockList=['000725','000783','002167','002505','002600','300315','600000','600011','600048','601001'] + downloadAllHistoryAShareData(stockList) + #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 diff --git a/trade_process/strategy/macd_back_test.pyc b/trade_process/strategy/macd_back_test.pyc new file mode 100644 index 0000000000000000000000000000000000000000..04fcc0e05eb36fd7bb13c00b579d94e2ed18cea6 GIT 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: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 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 + diff --git a/trade_process/strategy/macd_live_test.pyc b/trade_process/strategy/macd_live_test.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b7393f9a8b47b0ae645eee7aae7c032faaa6b7e5 GIT binary patch literal 6728 zcmcIp-)|gO6}~gGzc+5|#E#cd)i`a57nDS$L4`&pjbpoMz;UXvleWfTnCy(*ab{;X zJF|%`><35%eE}qd+5!~RBqxsb%IeD$J{TL9G;YO;&|PRWGTP63RK{6_l4(Zz*+KsW(>2>|v=|L~p^r@v~Sp zkWETRYHnC-C%#j6wTu;?>s6oklf}lZIXCgOtbl6I_nWmwb4%J^oLNd*s9xXNHM?`r zi?1N@JM+R=R!okAf2Mj%&(Y!-S2COrGg;+jC4(8{B$H3)fq7z1)F~=kPo_F(e}$Qi9G+wP8`XfpyWf=(#I2Izo)XdPox^>I%D>d6g8@w#81- z-EB+lz`m(ke^yD6@{JN_hQ9E(lTk^T`>~BqvuYZ8%;nT{vQKRcQ61%V3=>wLKnnxL zA{gG!D(FqI&^c;!fg{n2f*v-8 zIY6)2=?jCejOuQY9UsG(y^`vejPJ+-P$xgw9!uY8FWFt~Q_1)a778ZxV6Yh5DCVToq{%`{9~oxO=h)vPbqIDsUob5;blm!^nnioWKxR z%8B1>v=aElE@9Y+)|0$$L)~PgubzvpTlJfwl+CKpRFcHA$jzT#eeTqS<<+%z(E6$9H2X>_<-VXE?V 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zqcFXzDz7I8afQ7y+$c%70mmq)3F?af;cWzEHm?Bg^sMU!ZsZI!PKZT3ShE zU39?;eQW4(KH?PVHMYNsTJfZ~eXZ|MfI>R1b5!6!a878Kd$Z-k8$)leyy-LO>@WDl zbSs5(;-EETJSmp{7_zF7#W))Rj5q(71PwR7dV=K8of6MJ zrN(>Hf}k_4E5;4Eujo)(u(>EvwS5e>>{pRUQP}z9bAkZzBp)L|o;UqgqMtiC82-0Io= (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 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Hf(`XAhtsLP literal 0 HcmV?d00001 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..ca6ee03 100644 --- a/trade_process/strategy/tread_tracking.py +++ b/trade_process/strategy/tread_tracking.py @@ -1,542 +1,559 @@ -#!/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') - - - - - - - - - +#!/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 + +#参数 +prama_ma_short = 2 # 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") + + # 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') + # 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'], prama_ma_short) + ma_long = pd.rolling_mean(df['close'], 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.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.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): + # code = [['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 = [] + for i in code: + # save(strfundcode=i ,numdays=365*1) + buysell.append(tread_track_backtest(i)) + 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] + stock_trader_main() + + + + + + + + + + + \ No newline at end of file diff --git a/trade_process/strategy/tread_tracking.pyc b/trade_process/strategy/tread_tracking.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1a75282049f9d51c6ed69cb8f8654440e74c6ecf GIT binary patch literal 11195 zcmb_iU2t2+bw2mvhXnX53KT(!`n8;}i6To>Q^~9~e z?<{ZuO0>sqNboLp&+a{Y_MEf3=i5c(KiiUDI++>GN%b#+@887ZTb76`GK$m^*Or1M zqjoJHk;$lx#%jg5j5cXLB86s|PRM9NEWQ?%LQKijgSg<=C2o_9ZWOm!MpNQ~Ww*FV8Qml<*!76pDx+y}!L64ZMmLKKZd)j++aXVb zvQ_h)n%^ca*n&fsJS}obljMK_Y< 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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..c827033 --- /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 +1,174 @@ +1 161725 招商中证白酒指数分级 股票指数 14 4 3 1 5.5 +2 160222 国泰国证食品饮料行业指数分级 股票指数 15 5 9 4 8.25 +3 110022 易方达消费行业 股票型 16 14 4 2 9.0 +4 162605 景顺长城鼎益混合(LOF) 混合型 10 10 10 11 10.25 +5 260108 景顺长城新兴成长混合 混合型 9 11 8 14 10.5 +6 000248 汇添富中证主要消费ETF联接 联接基金 26 6 12 8 13.0 +7 001740 光大中国制造2025混合 混合型 28 8 11 19 16.5 +8 160632 鹏华酒分级 股票指数 44 7 17 7 18.75 +9 000083 汇添富消费行业混合 混合型 29 31 14 24 24.5 +10 206007 鹏华消费优选混合 混合型 17 33 15 36 25.25 +11 000854 鹏华养老产业 股票型 18 24 24 51 29.25 +12 001542 国泰互联网+股票 股票型 88 19 5 5 29.25 +13 519664 银河美丽混合A 混合型 20 27 18 59 31.0 +14 481012 工银深证红利联接 联接基金 23 51 26 30 32.5 +15 310388 申万菱信消费增长混合 混合型 40 16 23 52 32.75 +16 070032 嘉实优化红利混合 混合型 48 29 13 47 34.25 +17 550016 信诚至远C 分级杠杆 1 3 60 77 35.25 +18 000462 农银主题轮动混合 混合型 38 15 28 60 35.25 +19 519665 银河美丽混合C 混合型 24 32 22 65 35.75 +20 180012 银华富裕主题混合 混合型 100 23 19 6 37.0 +21 000431 鹏华品牌传承混合 混合型 27 18 30 75 37.5 +22 001076 易方达改革红利混合 混合型 32 9 40 72 38.25 +23 001606 农银工业4.0混合 混合型 43 30 29 53 38.75 +24 000742 国泰新经济灵活配置混合 混合型 100 41 16 9 41.5 +25 020005 国泰金马稳健 混合型 39 49 51 28 41.75 +26 260110 景顺长城精选蓝筹混合 混合型 31 65 37 40 43.25 +27 001631 天弘中证食品饮料指数A 股票指数 75 25 62 22 46.0 +28 260116 景顺长城核心竞争力混合A 混合型 33 89 35 34 47.75 +29 001632 天弘中证食品饮料指数C 股票指数 77 28 65 23 48.25 +30 004008 中融鑫思路混合A 混合型 100 1 6 87 48.5 +31 000619 东方红产业升级混合 混合型 100 52 27 15 48.5 +32 001112 东方红中国优势混合 混合型 100 57 21 17 48.75 +33 519056 海富通内需热点混合 混合型 19 17 76 85 49.25 +34 004009 中融鑫思路混合C 混合型 100 2 7 88 49.25 +35 360007 光大保德信优势配置混合 混合型 49 26 71 57 50.75 +36 001387 中融新经济混合A 混合型 100 100 1 3 51.0 +37 260103 景顺长城动力平衡 混合型 22 98 58 29 51.75 +38 001287 安信优势增长混合A 混合型 50 13 74 71 52.0 +39 110011 易方达中小盘混合 混合型 62 87 47 12 52.0 +40 040008 华安策略优选混合 混合型 63 95 20 31 52.25 +41 110003 易方达上证50指数A 股票指数 42 67 75 32 54.0 +42 519690 交银稳健配置混合A 混合型 100 45 25 48 54.5 +43 002851 南方品质混合 混合型 37 64 31 89 55.25 +44 530015 建信深证60联接 联接基金 47 86 43 46 55.5 +45 160216 国泰商品 QDII 7 35 100 100 60.5 +46 160628 鹏华地产分级 股票指数 2 48 100 100 62.5 +47 165312 建信央视50 股票指数 100 100 33 20 63.25 +48 519066 汇添富蓝筹稳健 混合型 84 100 32 38 63.5 +49 169101 东方红睿丰混合 混合型 100 100 38 16 63.5 +50 161129 易方达原油A类人民币 QDII 21 39 100 100 65.0 +51 540012 汇丰晋信恒生龙头指数A 股票指数 69 100 67 25 65.25 +52 004528 长盛盛通纯债A none 3 60 100 100 65.75 +53 003321 易方达原油C类人民币 QDII 25 40 100 100 66.25 +54 519772 交银新生活力灵活配置混合 混合型 100 12 66 95 68.25 +55 217027 招商央视财经50指数A 股票指数 100 100 52 21 68.25 +56 519606 国泰金鑫 股票型 100 100 55 18 68.25 +57 519068 汇添富成长焦点混合 混合型 100 100 34 41 68.75 +58 270041 广发消费品精选混合 混合型 71 100 79 26 69.0 +59 540009 汇丰晋信消费红利 股票型 100 100 49 27 69.0 +60 001208 诺安低碳经济股票 股票型 100 47 88 45 70.0 +61 001281 长安鑫利优选混合A 混合型 100 83 42 55 70.0 +62 001044 嘉实新消费股票 股票型 100 100 46 35 70.25 +63 002666 前海开源沪港深创新成长混合A 混合型 100 56 36 90 70.5 +64 398061 中海消费混合 混合型 100 36 78 69 70.75 +65 020026 国泰成长优选混合 混合型 100 100 70 13 70.75 +66 002072 长安鑫利优选混合C 混合型 100 85 44 56 71.25 +67 001170 泰达宏利复兴混合 混合型 100 66 50 70 71.5 +68 160127 南方新兴消费分级 股票型 100 100 77 10 71.75 +69 002853 华富元鑫灵活配置混合A 混合型 100 100 2 86 72.0 +70 000167 广发聚优灵活配置混合A 混合型 51 100 56 83 72.5 +71 002667 前海开源沪港深创新成长混合C 混合型 100 61 39 91 72.75 +72 001712 东方红优势精选混合 混合型 100 100 53 39 73.0 +73 002624 广发优企精选混合 混合型 59 34 100 100 73.25 +74 000634 富国天盛灵活配置混合 混合型 72 21 100 100 73.25 +75 001892 长盛新兴成长混合 混合型 13 84 100 100 74.25 +76 660010 农银策略精选混合 混合型 100 54 99 44 74.25 +77 002124 广发新兴产业混合 混合型 30 69 100 100 74.75 +78 160218 国泰国证房地产行业指数分级 股票指数 4 96 100 100 75.0 +79 000029 富国宏观策略 混合型 80 22 100 100 75.5 +80 002697 中欧消费主题股票C 股票型 60 43 100 100 75.75 +81 001404 招商移动互联网产业股票基金 股票型 46 58 100 100 76.0 +82 000746 招商行业精选股票 股票型 97 46 85 76 76.0 +83 001583 安信新常态股票 股票型 100 100 41 63 76.0 +84 003119 博时鑫源混合A 混合型 5 100 100 100 76.25 +85 003120 博时鑫源混合C 混合型 6 100 100 100 76.5 +86 570001 诺德价值优势混合 混合型 100 100 57 49 76.5 +87 001605 国富沪港深成长精选股票 股票型 87 20 100 100 76.75 +88 161721 招商沪深300地产指数分级 股票指数 8 100 100 100 77.0 +89 001047 光大国企改革股票 股票型 53 55 100 100 77.0 +90 020023 国泰事件驱动策略混合 混合型 93 100 84 33 77.5 +91 241001 华宝海外中国成长混合 QDII 100 100 68 42 77.5 +92 164906 交银中证海外中国互联网指数 QDII-指数 100 100 73 37 77.5 +93 519225 海富通集利债券 债券型 11 100 100 100 77.75 +94 002085 长盛互联网+混合 混合型 12 99 100 100 77.75 +95 000751 嘉实新兴产业股票 股票型 100 100 59 54 78.25 +96 001179 德邦大健康灵活配置混合 混合型 100 100 63 50 78.25 +97 376510 上投大盘蓝筹 股票型 100 100 54 64 79.5 +98 002621 中欧消费主题股票A 股票型 70 50 100 100 80.0 +99 110015 易方达行业领先混合 混合型 100 100 61 62 80.75 +100 001725 汇添富高端制造股票 股票型 82 42 100 100 81.0 +101 000423 前海开源事件驱动混合A 混合型 66 100 89 78 83.25 +102 004642 南方房地产ETF联接A none 34 100 100 100 83.5 +103 001865 前海开源事件驱动混合C 混合型 64 100 90 80 83.5 +104 000011 华夏大盘精选 混合型 90 100 83 61 83.5 +105 160605 鹏华中国50 混合型 35 100 100 100 83.75 +106 163805 中银策略混合 混合型 61 74 100 100 83.75 +107 004643 南方房地产ETF联接C none 36 100 100 100 84.0 +108 004374 华泰保兴吉年丰混合发起A 混合型 100 37 100 100 84.25 +109 001878 嘉实沪港深精选股票 股票型 100 100 45 92 84.25 +110 001371 富国沪港深价值混合 混合型 100 100 94 43 84.25 +111 004375 华泰保兴吉年丰混合发起C 混合型 100 38 100 100 84.5 +112 501018 南方原油 QDII 41 100 100 100 85.25 +113 002121 广发沪港深新起点股票 股票型 100 100 48 93 85.25 +114 001236 博时丝路主题股票A 股票型 100 44 100 100 86.0 +115 001518 万家瑞兴 混合型 45 100 100 100 86.25 +116 070018 嘉实回报 混合型 100 92 97 58 86.75 +117 000532 景顺长城优势企业混合 混合型 81 100 86 81 87.0 +118 000529 广发竞争优势混合 混合型 100 93 82 74 87.25 +119 003704 光大保德信事件驱动混合 混合型 54 97 100 100 87.75 +120 040005 华安宏利混合 混合型 52 100 100 100 88.0 +121 163412 兴全轻资产混合(LOF) 混合型 79 73 100 100 88.0 +122 180013 银华领先策略混合 混合型 100 53 100 100 88.25 +123 000173 汇添富美丽30混合 混合型 100 81 93 79 88.25 +124 000020 景顺长城品质投资混合 混合型 100 100 81 73 88.5 +125 001040 新华策略精选股票 股票型 55 100 100 100 88.75 +126 519185 万家精选混合 混合型 56 100 100 100 89.0 +127 519087 新华优选分红混合 混合型 57 100 100 100 89.25 +128 001157 国联安睿祺灵活配置混合 混合型 58 100 100 100 89.5 +129 163406 兴全合润分级混合 混合型 86 72 100 100 89.5 +130 003243 上投摩根中国世纪人民币 QDII 100 100 64 94 89.5 +131 100022 富国天瑞强势 混合型 100 59 100 100 89.75 +132 001163 银华中国梦30股票 股票型 100 94 98 67 89.75 +133 001581 华安沪港深通精选灵活配置混合 混合型 100 62 100 100 90.5 +134 118001 易方达亚洲精选 QDII 100 100 96 66 90.5 +135 398021 中海能源策略 混合型 100 63 100 100 90.75 +136 001681 新华积极价值灵活配置混合 混合型 65 100 100 100 91.25 +137 003940 银华盛世精选灵活配置混合发起式 混合型 100 100 69 96 91.25 +138 217001 招商安泰混合 混合型 67 100 100 100 91.75 +139 110010 易方达价值成长混合A 混合型 68 100 100 100 92.0 +140 001458 广发中证全指主要消费联接A 联接基金 100 68 100 100 92.0 +141 200008 长城品牌优选混合 混合型 100 100 100 68 92.0 +142 002214 中海沪港深价值优选混合 混合型 100 100 72 97 92.25 +143 002189 农银汇理国企改革混合 混合型 100 70 100 100 92.5 +144 002976 广发中证全指主要消费联接C 联接基金 100 71 100 100 92.75 +145 163810 中银价值精选 混合型 73 100 100 100 93.25 +146 519979 长信内需成长混合 混合型 100 100 91 82 93.25 +147 162719 广发道琼斯石油指数A QDII-指数 74 100 100 100 93.5 +148 000127 农银行业领先混合 混合型 100 75 100 100 93.75 +149 004243 广发道琼斯石油指数C QDII-指数 76 100 100 100 94.0 +150 004183 富国产业升级混合 none 100 76 100 100 94.0 +151 519642 银河智造混合 混合型 100 77 100 100 94.25 +152 519191 万家新利灵活配置混合 混合型 78 100 100 100 94.5 +153 161706 招商优质成长混合(LOF) 混合型 100 78 100 100 94.5 +154 002534 华安稳固收益债券A 债券型 100 100 80 98 94.5 +155 217012 招商行业领先混合A 混合型 100 79 100 100 94.75 +156 370024 上投摩根核心优选混合 混合型 100 100 95 84 94.75 +157 001663 中银互联网+股票 股票型 100 80 100 100 95.0 +158 001657 长安鑫富领先混合 混合型 100 82 100 100 95.5 +159 000835 华润元大富时中国A50指数 股票指数 83 100 100 100 95.75 +160 163411 兴全精选混合 保本型 85 100 100 100 96.25 +161 001927 华夏消费升级灵活配置混合A 混合型 100 100 87 99 96.5 +162 004477 嘉实沪港深回报混合 混合型 100 88 100 100 97.0 +163 519915 富国消费主题混合 混合型 89 100 100 100 97.25 +164 004263 华安沪港深机会灵活配置混合 none 100 90 100 100 97.5 +165 001626 国泰央企改革股票 股票型 91 100 100 100 97.75 +166 160624 鹏华消费领先混合 混合型 100 91 100 100 97.75 +167 005063 广发中证全指家用电器指数A none 92 100 100 100 98.0 +168 001928 华夏消费升级灵活配置混合C 混合型 100 100 92 100 98.0 +169 005064 广发中证全指家用电器指数C none 94 100 100 100 98.5 +170 000496 长安产业精选混合A 混合型 95 100 100 100 98.75 +171 519706 交银深证300联接 联接基金 96 100 100 100 99.0 +172 481001 工银核心价值混合A 混合型 98 100 100 100 99.5 +173 004357 南方智慧混合 混合型 99 100 100 100 99.75 +174 000017 财通可持续混合 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 0000000000000000000000000000000000000000..97b6da73829dbdec851163074ec6546c651a7f45 GIT binary patch literal 2314 zcmb_d-D@0G6hAXNyGb^?H6|+5FM<-BC`tXIlp-Rntsm4NlltHi940e&H?y6cDRb{u z0-L8qTS~zfv0CskRgh}&L8X=U5Aijd)F&VO1N3*!>?Sd98fUp@?z#8e-#s6{Gh6zz zUcLF-M~#pMPYLfgF!b*b5m5uuqR6JiqJ~AvCS}pOMHXJ|5=A9Z1||+QoS}Jn-@HPN zimW@7jL`YGOv<75C`DyH9aZS~Kpzo(bXOk}y?Q_&KcLs>cw>yBIzoB zR7s6f^blRJDesf2@@k_-szz;?PKe_v!n0}7okX;TV-J^?sX961@+-||BLV9u zt%DYzW4=(IKGc1CU^auj`tQK@PIF>nhXp6aR3Oe}t(M9|%;e$CQyBUfghHNC)**Ai zk&O~HY>FJNO#`ecbI~`zD2Kd7cLZk)21(2UpA_&6%e(9Vr$YY-0n@fcJz-vt$hQfy 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zcmZSn%*(axc3f040~9a;X$K%KmID$gK*Y$9!@!Ws$PmTIzzE_qG2}2YgrqPt1Z%JW zm1O=00u4qWt3(V)q^FhyXI8jmre~HU<^-4ICuck7r=&vUL5j5eG?+nZ`G7=eNoI~- zF-Q%F6$BJvDB*#LC+Fwp=I0e_fJ6~Qu{MzFY87K(>=_c1R+^V$2qt5|rsx{QfDDR> ikI&4@EQycTE2u2t0IIji%}*)KNwovnUkvh}03!gHK{(X_ literal 0 HcmV?d00001 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 0000000000000000000000000000000000000000..5670a662a5795161274a6d1259bcc7eafd04c346 GIT binary patch literal 2967 zcmcIm&2Jl35PxsiPGZMyX-P=>T~$~Z30G-zAVd{H3Ki|4KCFv?$Y2@oI^D+JHT#|k zLFp+06(l%t;ex~kkoX(;PvF`hapn(bf4^COwe(QMVeL0N`#$E)Z|1$3asMsmfBWl` z&6XNG4&LvenXf>6{I%*RwK?Lo+SJ2-M{PRFchmtUxXO2h$tWNHtUAEVtnz21b4K~I z!ptgvPMA66=Y+{AKQByP`SZfeE59I2fxFkrMQ9Q}##3!+ur68_Z3E4$f$UyTR;l}1 z-FMUjt@gmXYEP?uEdC%`P#K$1+j*6&D*eDw8soJOfvi@hv}&2MEQOj~V`uobe%!Zq z4r~y$Vn2+wq+dP)n?wK2FIqv*hH+Hp!gRGw(zgjL1qWpiCB$3>cXPeIvU>Y&z1@%e zYeMVB#;xtj^?KiiojNuOHsa{>AhB0_yLO%;xO>ppNt(S`e40Fn(ylJ(Q~IP9W6)h0 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('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 0000000000000000000000000000000000000000..8f8cff7c2a334ab0b6dc8a4f760d8639aa864ef3 GIT binary patch literal 1597 zcmbVM-EJF26h5=-zt~P=3PR!nX)7dZDtA>Uv<+5;ib@cXNQ7)lts<+d_3qeSXLrZU z%!&j#SK%?Z=52Ta9)U|9pqz8o4hC_>SUxl7%s1cpnKSGDx!?Tt_qP`jY(@>|m-si| zqKE-5Pzs10NCk{z7vc)URfua4w;-;=&sDf^L87q%mk#K!K_X@k;wIdJ5ILT1!Km9t zGV7vvHJv8!aC8M(NIWtJD5L3gQ5gHO|Ln+*RAw7!>mMFHefIqL3-bXgS^xO+{*(Uk z;oE0ES9Yrstnk$ZfyNr}_Rb(w#iGQ6q7x(>@0tkD}1JGfnAzYQwGtq?a2 zSi7)BwFxf>+h(jorsY0zz^%LMqg6(C4@R%^M^Lc`aM=X?2fErr*ft54N`JWyc5vknD+zvMLe;`Z;OS#>~TAr(1`a7Lm!R^u6`1`W2se8{(&rZhj%9{64 znaX*Rmu2Rn`@WC;2e44a8hRWivnzkM`GaT?Y9pb{T9VJyhL^ykG8Z~Xa(=-SA)Uq-1Q43QSe@i^4sWar$>x)*NUo?Y`TKwf-u|IT zY?quc=B~Y%b=x|uY@4Kvy{rzeHSQo^ za&$VL;G>EAEGNdQ=<@O5q_9akq0|Dd%<9_S!vkz6>G2+^(B@MV&~A&4s5maTICq?m zb09kIebE#jIUUhJ?L)EWa4G@u{ukEROT;UBQDOEbKz*AmH63PwbE?JA6O~ osOf?r4>K7Ae<$-lGa|AVnTm^4eo5bL?*E!-I<6>x?N8d`FT_)Uh5!Hn literal 0 HcmV?d00001 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 0000000000000000000000000000000000000000..296e77603fe6a0415b24b0f3e2c0349444d2531a GIT binary patch literal 1267 zcmb7E&2G~`5T3P@mQoCnpQ&cfmSITkOD$T2`XH2a4I21p;53MYg@-oy&JWl z^b`b~cnn^O$KV0rn{ofHvApA%+4N})Dm%d@crLZJSiNdllW%lWLEAZF<@N_)|)|Nre^U>4WoJ-Q@ zqcn~zi%FEq*Vw$+X3roCBfAO)+J2O0;y(1CAR*=%xOc5V^Tmgw!FZO22SNwNX2I#+ z^TEvOq!5_fpMJ5Ma6rbMzUb&!+fmYE(>C%{uw(9lkXli@YF!B`3_c%^m)QnEEk6Z@ zA4FqRuIRgnC?BQwJa*^?AjI(UVlQ(D0@oAR5`%^qKocInAi8(9&@{!nCk2irnme=w z$j1(C9=@Nu*D1^nA|7R^pkehSYUHyt)oI*#YFbzg69risnp-H@$a0NPaQwN?Bky+E}Iqg7y^` z0PTw*O7qbjG;!hLGSs<|+*DA!it0PZ4xMdZcT6M3RSg{5=aG{8A;2bDF2v^q<0$ Date: Wed, 17 Jan 2018 21:15:51 +0800 Subject: [PATCH 03/67] Update .travis.yml --- .travis.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index a50b693..e6f1a0c 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,8 +1,8 @@ language: python python: - - "3.5" + - "2.7" # command to install dependencies install: "pip install -r requirements.txt" # command to run tests script: - - python3 ./trade_process/live_trade.py + - python stock_trader.py From 8df77fc8866b0a42ba913eeceade50cd98a2b9bc Mon Sep 17 00:00:00 2001 From: lishulincug <1627041882@qq.com> Date: Wed, 17 Jan 2018 21:23:05 +0800 Subject: [PATCH 04/67] Add files via upload --- stock_trader.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/stock_trader.py b/stock_trader.py index c5140ea..cb6f67b 100644 --- a/stock_trader.py +++ b/stock_trader.py @@ -1,5 +1,5 @@ #coding=utf-8 -from trade_process.strategy import main +from trade_process.strategy.macd_back_test import macdmain from trade_process.strategy.tread_tracking import stock_trader_main if __name__ == '__main__': stockList = ['000725', '000783', '002167', '002505', '002600', '300315', '600000', '600011', '600048', '601001'] @@ -17,5 +17,5 @@ ['110031', 'eHSI'], ['161130', 'eNASDAQ100'], ['110028', 'anxinB'], ['110022', 'eConsumption '], ['161125', 'SPX500']] #均线策略 - main(ChinaStockIndexList) + macdmain(ChinaStockIndexList) stock_trader_main(code) \ No newline at end of file From 0173302da8381c488eb7a2ad8e58d7ccfe6a2ce7 Mon Sep 17 00:00:00 2001 From: lishulincug <1627041882@qq.com> Date: Wed, 17 Jan 2018 21:25:29 +0800 Subject: [PATCH 05/67] Update macd_back_test.py --- trade_process/strategy/macd_back_test.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/trade_process/strategy/macd_back_test.py b/trade_process/strategy/macd_back_test.py index 48b4367..4d74a21 100644 --- a/trade_process/strategy/macd_back_test.py +++ b/trade_process/strategy/macd_back_test.py @@ -570,7 +570,7 @@ def run(stockCsvPath, stockName): select_Time_AMA(stock_data, stockName) print '\n' -def main(stockList): +def macdmain(stockList): print "main begin" # stockList = ['000725', '000783', '002167', '002505', '002600', '300315', '600000', '600011', '600048', '601001'] downloadAllHistoryAShareData(stockList) @@ -643,4 +643,4 @@ def main(stockList): select_Time_AMA(stock_data, stockName) print '\n' - print "main end" \ No newline at end of file + print "main end" From 4af32481c2582af576b3b09daddc9d8cbff7fd06 Mon Sep 17 00:00:00 2001 From: lishulincug <1627041882@qq.com> Date: Wed, 17 Jan 2018 21:41:41 +0800 Subject: [PATCH 06/67] Update __init__.py --- trade_process/strategy/__init__.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/trade_process/strategy/__init__.py b/trade_process/strategy/__init__.py index 699f4ff..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 * From 35030baa4c38ab874ac5f1a1c6aa019fb9887689 Mon Sep 17 00:00:00 2001 From: lishulincug <1627041882@qq.com> Date: Wed, 17 Jan 2018 21:44:38 +0800 Subject: [PATCH 07/67] Update __init__.py --- data_process/__init__.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/data_process/__init__.py b/data_process/__init__.py index 0d95d7d..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 * From 32528916ffd6110417d506602f9e09f29f937e93 Mon Sep 17 00:00:00 2001 From: lishulincug <1627041882@qq.com> Date: Wed, 17 Jan 2018 23:29:30 +0800 Subject: [PATCH 08/67] Add files via upload --- trade_process/live_trade.py | 19 ++--- trade_process/strategy/__init__.pyc | Bin 323 -> 146 bytes trade_process/strategy/macd_back_test.py | 81 +++++++++++++--------- trade_process/strategy/macd_back_test.pyc | Bin 14063 -> 13976 bytes trade_process/strategy/macd_live_test.py | 48 +++++++++++-- trade_process/strategy/tread_tracking.py | 5 +- trade_process/strategy/tread_tracking.pyc | Bin 11195 -> 11544 bytes 7 files changed, 100 insertions(+), 53 deletions(-) diff --git a/trade_process/live_trade.py b/trade_process/live_trade.py index 1f22bfe..b8851d8 100644 --- a/trade_process/live_trade.py +++ b/trade_process/live_trade.py @@ -5,13 +5,6 @@ import platform from apscheduler.schedulers.blocking import BlockingScheduler import logging -logging.basicConfig() -# import errorLogger -if platform.system() == 'Windows': - sys.path.append('../') -else: - sys.path.append('/Users/cbb/Documents/pythonspace/stock-master/') - import ctypes from ConfigParser import ConfigParser @@ -26,6 +19,13 @@ 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(): @@ -42,7 +42,6 @@ def main(): 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') @@ -78,7 +77,6 @@ def main(): send_email_163(subject='MA Strategy Results', content=str_all) - @fn_timer_ def live_mult_stock(stockClassList): # pool = ThreadPool(processes=4) @@ -131,9 +129,6 @@ def live_single_stock(stock): stock = Stock() return stock - - - if __name__ == "__main__": print ">>live trade begin" main() diff --git a/trade_process/strategy/__init__.pyc b/trade_process/strategy/__init__.pyc index b3adedf5807b0458a512105670244ff8e1b7a63e..e2ec273ba67b1efb62683402bc3af6cc4acc0938 100644 GIT binary patch delta 79 zcmX@iG>OrU`7pjVQLB_)>Z@8>1;m GDFXoIB?@%_ literal 323 zcmZSn%*(axc3f040~9a;X$K%KRss?!K*Y$9!@v*%Wii58Oh6VBL$C%jP+8`GAkbh0 zvPwXNmLFKOL=_}Zl$eqlUr>~voLXG0R}AEqq^4KuuMEaVC)$ZlUACSVhAQPO2Tqm&GhVf{T%h5dbnWT4MkJ diff --git a/trade_process/strategy/macd_back_test.py b/trade_process/strategy/macd_back_test.py index 4d74a21..54b7a47 100644 --- a/trade_process/strategy/macd_back_test.py +++ b/trade_process/strategy/macd_back_test.py @@ -8,9 +8,10 @@ import os import numpy as np from data_process.download_stock import downloadAllHistoryAShareData +from trade_process import efund_mail2 -AVR_SHORT = 12 -AVR_LONG = 40 +# AVR_SHORT = 12 +# AVR_LONG = 40 SIGNAL_BUY = 1 #买 SIGNAL_SALE = -1 #卖 @@ -48,9 +49,11 @@ def processEMA(stockCsvPath, stockCsvNewPath): stock_data.to_csv(stockCsvNewPath, index=False) # 自适应均线 -def self_adaptive_ma(stock_data): +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) + # stock_data.sort('Date', inplace=True) close_price = stock_data['Adj Close'].get_values() high_price = stock_data['High'].get_values() @@ -97,14 +100,15 @@ def self_adaptive_ma(stock_data): # MA指标择时 (回测） -def select_Time_MA(stock_data, stockName): - +def select_Time_MA(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) + # stock_data.sort('Date', inplace=True) close_price = stock_data['Adj Close'].get_values() @@ -166,14 +170,15 @@ def select_Time_MA(stock_data, stockName): #stock_data.sort('date', ascending=False, inplace=True) # MACD指标择时 (回测） -def select_Time_MACD(stock_data, stockName): - +def select_Time_MACD(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) + # stock_data.sort('Date', inplace=True) close_price = stock_data['Adj Close'].get_values() @@ -244,14 +249,15 @@ def select_Time_MACD(stock_data, stockName): return dif_price, dea_price, macd_price # DMA指标择时 (回测） -def select_Time_DMA(stock_data, stockName): - +def select_Time_DMA(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) + # stock_data.sort('Date', inplace=True) close_price = stock_data['Adj Close'].get_values() @@ -319,14 +325,15 @@ def select_Time_DMA(stock_data, stockName): #stock_data.sort('date', ascending=False, inplace=True) # DMA指标择时 (回测） -def select_Time_TRIX(stock_data, stockName): - +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) + # stock_data.sort('Date', inplace=True) close_price = stock_data['Adj Close'].get_values() @@ -382,8 +389,9 @@ def select_Time_TRIX(stock_data, stockName): # 组合择时指标 (回测） -def select_Time_Mix(stock_data, stockName): - +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 @@ -450,8 +458,9 @@ def select_Time_Mix(stock_data, stockName): stock_data['SIGNAL_MIX'] = signals # AMA指标择时 -def select_Time_AMA(stock_data, stockName): - +def select_Time_AMA(stock_data, stockName,Avg): + AVR_SHORT = Avg[0] + AVR_LONG = Avg[1] percentage = 0.1 start_money = 100000000 @@ -460,7 +469,7 @@ def select_Time_AMA(stock_data, stockName): one_hand = 10000 # 将数据按照交易日期从远到近排序 - stock_data.sort('Date', inplace=True) + # stock_data.sort('Date', inplace=True) close_price = stock_data['Adj Close'].get_values() @@ -573,40 +582,44 @@ def run(stockCsvPath, stockName): def macdmain(stockList): print "main begin" # stockList = ['000725', '000783', '002167', '002505', '002600', '300315', '600000', '600011', '600048', '601001'] - downloadAllHistoryAShareData(stockList) + # downloadAllHistoryAShareData(stockList) # stockList = ['000725'] + for stockName in stockList: # if stockName != '002600': # continue - stockCsvPath = os.path.pardir + "\\stockdata\\" + stockName + '.csv' + # stockCsvPath = os.path.pardir + "\\stockdata\\" + stockName + '.csv' # stockCsvPath = stockName + '.csv' - if os.path.exists(stockCsvPath) == False: - continue + # if os.path.exists(stockCsvPath) == False: + # continue # stockCsvNewPath = stockName + '_macd.csv' # processEMA(stockCsvPath, stockCsvNewPath) - stock_data = pd.read_csv(stockCsvPath) + # stock_data = pd.read_csv(stockCsvPath) + fundlist = efund_mail2.get_histrydata(stockName, 365) + stock_data = pd.DataFrame(fundlist[::-1], columns=['Date', 'Adj Close', 'countclose', 'change']) # self_adaptive_ma(stock_data) - - print u'>>>>>>>>>>>>> MA 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' + + print u'-- MA 策略 --' select_Time_MA(stock_data, stockName) - print u'>>>>>>>>>>>>> MACD 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' + print u'-- MACD 策略 --' select_Time_MACD(stock_data, stockName) - print u'>>>>>>>>>>>>> DMA 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' + print u'-- DMA 策略 --' select_Time_DMA(stock_data, stockName) - print u'>>>>>>>>>>>>> TRIX 策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' + print u'-- TRIX 策略 -->' select_Time_TRIX(stock_data, stockName) - print u'>>>>>>>>>>>>> 组合策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' + print u'-- 组合策略 --' select_Time_Mix(stock_data, stockName) - print u'>>>>>>>>>>>>> AMA策略 >>>>>>>>>>>>>>>>>>>>>>>>>>' + print u'-- AMA策略 --' select_Time_AMA(stock_data, stockName) print '\n' print "main end" + if __name__ == "__main__": print "main begin" stockList=['000725','000783','002167','002505','002600','300315','600000','600011','600048','601001'] @@ -643,4 +656,4 @@ def macdmain(stockList): select_Time_AMA(stock_data, stockName) print '\n' - print "main end" + print "main end" \ No newline at end of file diff --git a/trade_process/strategy/macd_back_test.pyc b/trade_process/strategy/macd_back_test.pyc index 04fcc0e05eb36fd7bb13c00b579d94e2ed18cea6..d5dc0daa97ea156e16f76d00e0643ec1bae21521 100644 GIT binary patch literal 13976 zcmeHOU2GiJbv`r8zg;fL6}1#eOO_>DmbZ?rQgUqBg(8U}rKMt#wuf?Lvr@OKoe{Ov z{*XIEnM}#Tv6aaEOA9m!(xgC8v;|TiE%H>iv4NsMQ51bBiufVOV}M$EtBV#*fdWPQ zedo^X&MqaJhUxSr)XtrI&;6Zy&-v~-=UyfLV<7dxLSdmK;a>{x(|G)Uw?th0EUAbr zSW(=TrGzYWMagbi=#Jt&ve2t>TPl50P0B)2EH;slN=mBzve1uomn@{kMZE!WQDcxb z7KX&_m4%GBeR2!QUE(G+GAwRNBfG`z*GN{}v_?k69ni=gaR)UrD(;X*#>CBNWL#V{ zH7vI*S$II)-AddmZdN1v#CuTO5xHSY<6kXzkK|`uw1L{FhvF!>Ph1q)FYZI)9+1V9 zEbJFADc*j0(-Ld#?--lD@~$jABwn{H9gwAi;vSSy?;#m=AC}RDha>tU;yuht-XZPp zsuS29{mHd5o0NI3-8Q 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z&JqMjv$z+lc+`oIibtIYsd&_hkcvm02vI!hMF_WD8bk zGA@~o-1HXnMW6U*uJZCC@O7Uv@^dNsWF&bODf6i?==@hN+khZWRqk|~TFI7ioiHYX zHR*f0;HeU>7kAX@Il<<25BpRkkPO53IYSYAiSJ@H+wFE|V$dFhrred-P5?7svb?hHsI|)r!UQx}o%){%$D)6_$*5p(J}e~rPc8{X5m zIaR%#cUbpb1~Ag7=!#@AMQDeajPoAJDu8b@_S+0p6l_~Md_azUftY%!Z-I+lD E4^O`51^@s6 diff --git a/trade_process/strategy/macd_live_test.py b/trade_process/strategy/macd_live_test.py index f774466..2100f27 100644 --- a/trade_process/strategy/macd_live_test.py +++ b/trade_process/strategy/macd_live_test.py @@ -6,7 +6,7 @@ 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 @@ -166,8 +166,7 @@ def select_Time_DMA(self): 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 - + return signal # TRIX指标择时 (回测） def select_Time_TRIX(self): @@ -204,9 +203,6 @@ def select_Time_TRIX(self): signal = SIGNAL_SALE return signal - - - # AMA指标择时 def select_Time_AMA(self): @@ -250,3 +246,43 @@ def _getConstaint(self, prices): 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/tread_tracking.py b/trade_process/strategy/tread_tracking.py index ca6ee03..adfe897 100644 --- a/trade_process/strategy/tread_tracking.py +++ b/trade_process/strategy/tread_tracking.py @@ -211,7 +211,7 @@ def tread_track_backtest(code, df=None): # 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']) + df = pd.DataFrame(fundlist[::-1], columns=['Date', 'Adj Close', 'countclose', 'change']) if len(df) == 0: return None # print df.head(2) @@ -544,6 +544,9 @@ def stock_trader_main(code): # 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() diff --git a/trade_process/strategy/tread_tracking.pyc b/trade_process/strategy/tread_tracking.pyc index 1a75282049f9d51c6ed69cb8f8654440e74c6ecf..c0d85d3481fdc1b1df10fd1303c43552809d6caf 100644 GIT binary patch delta 1244 zcmZ8eOKcNI82)DyJBdx=SNw|O#yBC_JP5mX5|UQ53BJe&W0 zG12t4-Sh6pbBim`_`Eny<6m9AllYSj-uZX`wI#;Gz#~vlfJcGHB#8s>k|Y7VTaqO3 zuO#UKo|0rg@U$eo!22XQ0K8w40pKc;f(I7UV5MLSfRzEb0;W1d{lf^Ji?+SyjIvSR z!8VAO+~nE;uoWn^z&6G~)oz6?1?o+os$$1Kk@mpxJeI4s!sdTqw*wlv7ov7E)S6(6 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OaeSFk#SZ+C>HPx){J4<- From d3ef3c7cbfc2e6f64be1e808d8c4604b7c927e81 Mon Sep 17 00:00:00 2001 From: lishulincug <1627041882@qq.com> Date: Wed, 17 Jan 2018 23:30:21 +0800 Subject: [PATCH 09/67] Add files via upload --- stock_trader.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/stock_trader.py b/stock_trader.py index cb6f67b..1bfb07e 100644 --- a/stock_trader.py +++ b/stock_trader.py @@ -1,6 +1,7 @@ #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 if __name__ == '__main__': stockList = ['000725', '000783', '002167', '002505', '002600', '300315', '600000', '600011', '600048', '601001'] ChinaStockIndexList = [ @@ -17,5 +18,6 @@ ['110031', 'eHSI'], ['161130', 'eNASDAQ100'], ['110028', 'anxinB'], ['110022', 'eConsumption '], ['161125', 'SPX500']] #均线策略 - macdmain(ChinaStockIndexList) - stock_trader_main(code) \ No newline at end of file + macdmain(code) + stock_trader_main(code) + macd_live_main(code) \ No newline at end of file From 99cbec28e2c383db93f5106335f624e916e4dd3f Mon Sep 17 00:00:00 2001 From: lishulincug <1627041882@qq.com> Date: Wed, 17 Jan 2018 23:36:18 +0800 Subject: [PATCH 10/67] Update macd_back_test.py --- trade_process/strategy/macd_back_test.py | 52 ++++++++++++------------ 1 file changed, 26 insertions(+), 26 deletions(-) diff --git a/trade_process/strategy/macd_back_test.py b/trade_process/strategy/macd_back_test.py index 54b7a47..886a9c9 100644 --- a/trade_process/strategy/macd_back_test.py +++ b/trade_process/strategy/macd_back_test.py @@ -598,24 +598,24 @@ def macdmain(stockList): fundlist = efund_mail2.get_histrydata(stockName, 365) stock_data = pd.DataFrame(fundlist[::-1], columns=['Date', 'Adj Close', 'countclose', 'change']) # self_adaptive_ma(stock_data) - + Avg=[5,12] print u'-- MA 策略 --' - select_Time_MA(stock_data, stockName) + select_Time_MA(stock_data, stockName,Avg) print u'-- MACD 策略 --' - select_Time_MACD(stock_data, stockName) + select_Time_MACD(stock_data, stockName,Avg) print u'-- DMA 策略 --' - select_Time_DMA(stock_data, stockName) + select_Time_DMA(stock_data, stockName,Avg) print u'-- TRIX 策略 -->' - select_Time_TRIX(stock_data, stockName) + select_Time_TRIX(stock_data, stockName,Avg) print u'-- 组合策略 --' - select_Time_Mix(stock_data, stockName) + select_Time_Mix(stock_data, stockName,Avg) print u'-- AMA策略 --' - select_Time_AMA(stock_data, stockName) + select_Time_AMA(stock_data, stockName,Avg) print '\n' print "main end" @@ -636,24 +636,24 @@ def macdmain(stockList): #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) + Avg=[5,10] + 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" \ No newline at end of file + print "main end" From afdac25a01933943a0572ce522899d53ca33988a Mon Sep 17 00:00:00 2001 From: lishulincug <1627041882@qq.com> Date: Thu, 18 Jan 2018 09:16:03 +0800 Subject: [PATCH 11/67] Update init.py --- init.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/init.py b/init.py index cb1f582..58d8f4e 100644 --- a/init.py +++ b/init.py @@ -27,7 +27,7 @@ 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'}) +#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): @@ -111,4 +111,4 @@ def close_mytable(self): 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 +config_file_path = '../config.ini' From 22648b37fe3e30cb309d0ffc73a033078319e784 Mon Sep 17 00:00:00 2001 From: lishulincug <1627041882@qq.com> Date: Thu, 18 Jan 2018 09:21:15 +0800 Subject: [PATCH 12/67] Update efund_mail2.py --- trade_process/efund_mail2.py | 52 ++++++++++++++++++------------------ 1 file changed, 26 insertions(+), 26 deletions(-) diff --git a/trade_process/efund_mail2.py b/trade_process/efund_mail2.py index e05d342..9af6aa9 100644 --- a/trade_process/efund_mail2.py +++ b/trade_process/efund_mail2.py @@ -475,8 +475,7 @@ def check_time(H, M,S): # 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(): a=1 while True: @@ -494,30 +493,7 @@ def main1(): # 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') +def run(): now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) H = now[9:11] M = now[12:14] @@ -547,6 +523,30 @@ def main1(): # print sendmsg send_email(sendmsg) main1() +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') + run() # f.close() # itchat.send("@fil@%s" % 'buysell.txt', 'filehelper') # for (name, fund_id) in ids.items(): From 21f8ec2f32d7cedfdb836f8b6e2e85c7018aac76 Mon Sep 17 00:00:00 2001 From: lishulincug <1627041882@qq.com> Date: Thu, 18 Jan 2018 09:30:17 +0800 Subject: [PATCH 13/67] Update tread_tracking.py --- trade_process/strategy/tread_tracking.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/trade_process/strategy/tread_tracking.py b/trade_process/strategy/tread_tracking.py index adfe897..104d27b 100644 --- a/trade_process/strategy/tread_tracking.py +++ b/trade_process/strategy/tread_tracking.py @@ -16,7 +16,7 @@ import cwavelet #参数 -prama_ma_short = 2 # 短均线 +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 @@ -211,7 +211,7 @@ def tread_track_backtest(code, df=None): # 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', 'Adj Close', 'countclose', 'change']) + df = pd.DataFrame(fundlist[::-1], columns=['date', 'close', 'countclose', 'change']) if len(df) == 0: return None # print df.head(2) @@ -559,4 +559,4 @@ def stock_trader_main(code): - \ No newline at end of file + From e03173f4fb34fcf9ef9686f1c62fa556691c71b7 Mon Sep 17 00:00:00 2001 From: lishulincug <1627041882@qq.com> Date: Thu, 18 Jan 2018 09:46:38 +0800 Subject: [PATCH 14/67] Update macd_back_test.py --- trade_process/strategy/macd_back_test.py | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/trade_process/strategy/macd_back_test.py b/trade_process/strategy/macd_back_test.py index 886a9c9..c33683a 100644 --- a/trade_process/strategy/macd_back_test.py +++ b/trade_process/strategy/macd_back_test.py @@ -103,6 +103,7 @@ def self_adaptive_ma(stock_data,Avg): 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 @@ -113,9 +114,10 @@ def select_Time_MA(stock_data, stockName,Avg): close_price = stock_data['Adj Close'].get_values() #EMA - ma_list = [AVR_SHORT, AVR_LONG] + 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 @@ -125,7 +127,7 @@ def select_Time_MA(stock_data, stockName,Avg): 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]: + and ema_close_short[t-1] < ema_vclose_long[t-1]: if bBuySignal: signal = SIGNAL_BUY now_count = (int)(start_money / close_price[t] /100)*100 @@ -173,6 +175,7 @@ def select_Time_MA(stock_data, stockName,Avg): 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 @@ -183,10 +186,11 @@ def select_Time_MACD(stock_data, stockName,Avg): close_price = stock_data['Adj Close'].get_values() #EMA - ma_list = [AVR_SHORT, AVR_LONG] + ma_list = [AVR_SHORT, AVR_LONG,AVR_vLONG] 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]) + ema_close_vlong = pd.ewma(close_price, span=ma_list[2]) dif_price = ema_close_short - ema_close_long @@ -252,6 +256,7 @@ def select_Time_MACD(stock_data, stockName,Avg): 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 @@ -262,10 +267,11 @@ def select_Time_DMA(stock_data, stockName,Avg): close_price = stock_data['Adj Close'].get_values() #MA - ma_list = [AVR_SHORT, AVR_LONG] + ma_list = [AVR_SHORT, AVR_LONG,AVR_vLONG] 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]) + ma_close_vlong = pd.rolling_mean(close_price, ma_list[2]) dma_price = ma_close_short - ma_close_long @@ -598,7 +604,7 @@ def macdmain(stockList): fundlist = efund_mail2.get_histrydata(stockName, 365) stock_data = pd.DataFrame(fundlist[::-1], columns=['Date', 'Adj Close', 'countclose', 'change']) # self_adaptive_ma(stock_data) - Avg=[5,12] + Avg=[5,12,40] print u'-- MA 策略 --' select_Time_MA(stock_data, stockName,Avg) @@ -636,7 +642,7 @@ def macdmain(stockList): #processEMA(stockCsvPath, stockCsvNewPath) stock_data = pd.read_csv(stockCsvPath) #self_adaptive_ma(stock_data) - Avg=[5,10] + Avg=[5,10,40] print u'-- MA 策略 --' select_Time_MA(stock_data, stockName,Avg) From d07e95144893c8d543e99c7e6093899628bbf190 Mon Sep 17 00:00:00 2001 From: lishulincug <1627041882@qq.com> Date: Thu, 18 Jan 2018 09:57:17 +0800 Subject: [PATCH 15/67] Update macd_back_test.py --- trade_process/strategy/macd_back_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/trade_process/strategy/macd_back_test.py b/trade_process/strategy/macd_back_test.py index c33683a..bb28de9 100644 --- a/trade_process/strategy/macd_back_test.py +++ b/trade_process/strategy/macd_back_test.py @@ -127,7 +127,7 @@ def select_Time_MA(stock_data, stockName,Avg): 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_vclose_long[t-1]: + 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 From a935030677a005b92dc2a27fbaf51e4d5a8492e8 Mon Sep 17 00:00:00 2001 From: lishulincug <1627041882@qq.com> Date: Thu, 18 Jan 2018 10:07:14 +0800 Subject: [PATCH 16/67] Update macd_back_test.py --- trade_process/strategy/macd_back_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/trade_process/strategy/macd_back_test.py b/trade_process/strategy/macd_back_test.py index bb28de9..7a98ef2 100644 --- a/trade_process/strategy/macd_back_test.py +++ b/trade_process/strategy/macd_back_test.py @@ -126,7 +126,7 @@ def select_Time_MA(stock_data, stockName,Avg): signal = SIGNAL_DEFAULT - if ema_close_short[t] > ema_close_short[t-1] and ema_close_short[t] > ema_close_long[t] \ + 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 From ede9b81d7c841b7780a01c8ca7322ffab0b69211 Mon Sep 17 00:00:00 2001 From: lishulincug <1627041882@qq.com> Date: Thu, 18 Jan 2018 10:15:28 +0800 Subject: [PATCH 17/67] Update macd_back_test.py --- trade_process/strategy/macd_back_test.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/trade_process/strategy/macd_back_test.py b/trade_process/strategy/macd_back_test.py index 7a98ef2..8d10db5 100644 --- a/trade_process/strategy/macd_back_test.py +++ b/trade_process/strategy/macd_back_test.py @@ -604,9 +604,10 @@ def macdmain(stockList): fundlist = efund_mail2.get_histrydata(stockName, 365) stock_data = pd.DataFrame(fundlist[::-1], columns=['Date', 'Adj Close', 'countclose', 'change']) # self_adaptive_ma(stock_data) - Avg=[5,12,40] + Avg=[4,12,40] + Avg1=[3,11,40] print u'-- MA 策略 --' - select_Time_MA(stock_data, stockName,Avg) + select_Time_MA(stock_data, stockName,Avg1) print u'-- MACD 策略 --' select_Time_MACD(stock_data, stockName,Avg) From 6eaba5dcb1ecff4055568d42caf10c0647af7e64 Mon Sep 17 00:00:00 2001 From: lishulincug <1627041882@qq.com> Date: Thu, 18 Jan 2018 10:30:27 +0800 Subject: [PATCH 18/67] Update macd_back_test.py --- trade_process/strategy/macd_back_test.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/trade_process/strategy/macd_back_test.py b/trade_process/strategy/macd_back_test.py index 8d10db5..8c71d1b 100644 --- a/trade_process/strategy/macd_back_test.py +++ b/trade_process/strategy/macd_back_test.py @@ -144,7 +144,7 @@ def select_Time_MA(stock_data, stockName,Avg): bBuySignal = True signals.append(signal) if signal != 0: - #print 't:', t, ' signal:', signal + print 't:', t, ' signal:', signal tradeTimes += 1 @@ -222,7 +222,7 @@ def select_Time_MACD(stock_data, stockName,Avg): bBuySignal = True signals.append(signal) if signal != 0: - #print 't:', t, ' signal:', signal + print 't:', t, ' signal:', signal tradeTimes += 1 @@ -302,7 +302,7 @@ def select_Time_DMA(stock_data, stockName,Avg): bBuySignal = True signals.append(signal) if signal != 0: - #print 't:', t, ' signal:', signal + print 't:', t, ' signal:', signal tradeTimes += 1 @@ -384,7 +384,7 @@ def select_Time_TRIX(stock_data, stockName,Avg): bBuySignal = True signals.append(signal) if signal != 0: - #print 't:', t, ' signal:', signal + print 't:', t, ' signal:', signal tradeTimes += 1 @@ -456,7 +456,7 @@ def select_Time_Mix(stock_data, stockName,Avg): signals.append(signal) if signal != 0: - #print 't:', t, ' signal:', signal + 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' \ @@ -518,7 +518,7 @@ def select_Time_AMA(stock_data, stockName,Avg): #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 + print 't:', t, ' signal:', signal tradeTimes += 1 # 成本 From 464257f7c2cadaa1382c7e00e60195bd2a40fa89 Mon Sep 17 00:00:00 2001 From: lishulincug <1627041882@qq.com> Date: Thu, 18 Jan 2018 10:42:14 +0800 Subject: [PATCH 19/67] Update macd_back_test.py --- trade_process/strategy/macd_back_test.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/trade_process/strategy/macd_back_test.py b/trade_process/strategy/macd_back_test.py index 8c71d1b..db5d3e0 100644 --- a/trade_process/strategy/macd_back_test.py +++ b/trade_process/strategy/macd_back_test.py @@ -144,7 +144,8 @@ def select_Time_MA(stock_data, stockName,Avg): bBuySignal = True signals.append(signal) if signal != 0: - print 't:', t, ' signal:', signal + + print 't:', str(stock_data.ix[t, 'Date']), ' signal:', signal tradeTimes += 1 @@ -222,7 +223,7 @@ def select_Time_MACD(stock_data, stockName,Avg): bBuySignal = True signals.append(signal) if signal != 0: - print 't:', t, ' signal:', signal + print 't:', str(stock_data.ix[t, 'Date']), ' signal:', signal tradeTimes += 1 @@ -302,7 +303,7 @@ def select_Time_DMA(stock_data, stockName,Avg): bBuySignal = True signals.append(signal) if signal != 0: - print 't:', t, ' signal:', signal + print 't:', str(stock_data.ix[t, 'Date']), ' signal:', signal tradeTimes += 1 @@ -384,7 +385,7 @@ def select_Time_TRIX(stock_data, stockName,Avg): bBuySignal = True signals.append(signal) if signal != 0: - print 't:', t, ' signal:', signal + print 't:', str(stock_data.ix[t, 'Date']), ' signal:', signal tradeTimes += 1 @@ -456,7 +457,7 @@ def select_Time_Mix(stock_data, stockName,Avg): signals.append(signal) if signal != 0: - print 't:', t, ' signal:', signal + #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' \ @@ -518,7 +519,7 @@ def select_Time_AMA(stock_data, stockName,Avg): #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 + #print 't:', i, ' signal:', signal tradeTimes += 1 # 成本 From 619f0cef55caf7a9b94953b89b244d614cdbf71d Mon Sep 17 00:00:00 2001 From: lishulin <1627041882@qq.com> Date: Sun, 21 Jan 2018 23:03:48 +0800 Subject: [PATCH 20/67] =?UTF-8?q?=E6=8B=A9=E6=97=B6=E7=AD=96=E7=95=A5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 6 +- cleastsq.pyc | Bin 2978 -> 2954 bytes cwavelet.pyc | Bin 2468 -> 2448 bytes data_process/__init__.pyc | Bin 313 -> 134 bytes data_process/data_calcute.pyc | Bin 3930 -> 3918 bytes data_process/data_get.pyc | Bin 3331 -> 3307 bytes data_process/download_stock.pyc | Bin 4923 -> 4902 bytes init.pyc | Bin 4154 -> 4029 bytes stock_trader.py | 15 +- stockdata/GAOpt.txt | 71 ++++ stockdata/GAOpt1.txt | 3 + stockdata/IgnoreListStock.txt | 0 trade_process/__init__.pyc | Bin 168 -> 164 bytes trade_process/efund_mail2.py | 91 +++-- trade_process/efund_mail2.pyc | Bin 14798 -> 15101 bytes trade_process/fund_zf.py | 2 +- trade_process/fund_zf.pyc | Bin 6073 -> 6057 bytes trade_process/strategy/__init__.pyc | Bin 146 -> 144 bytes trade_process/strategy/macd_back_test.py | 230 +++++++---- trade_process/strategy/macd_back_test.pyc | Bin 13976 -> 19090 bytes trade_process/strategy/macd_live_test.pyc | Bin 6728 -> 8234 bytes trade_process/strategy/tread_tracking.pyc | Bin 11544 -> 11497 bytes ...2\345\220\215\347\273\223\346\236\234.txt" | 361 +++++++++--------- util/MyLogger.pyc | Bin 2314 -> 2294 bytes util/__init__.pyc | Bin 247 -> 243 bytes util/codeConvert.pyc | Bin 2967 -> 2923 bytes util/commons.pyc | Bin 813 -> 809 bytes util/helper.pyc | Bin 755 -> 743 bytes util/stockutil.pyc | Bin 1267 -> 1247 bytes ...2\345\220\215\347\273\223\346\236\234.txt" | 187 +++++++++ 30 files changed, 674 insertions(+), 292 deletions(-) create mode 100644 stockdata/GAOpt.txt create mode 100644 stockdata/GAOpt1.txt create mode 100644 stockdata/IgnoreListStock.txt create mode 100644 "\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" diff --git a/README.md b/README.md index 13adba4..852f572 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,7 @@ - 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 \ No newline at end of file diff --git a/cleastsq.pyc b/cleastsq.pyc index 9b48bb88ba0914728d3e9290fc37c81246076f44..d93af085ac8bf6e940bdbb1df12e1c8ea0c0bf81 100644 GIT binary patch delta 161 zcmZ1^-X+e?{F#?4w?1JbJ1>in5(5K+t5uAFv1dq(duEA8Y0~5~tn!;ZS#lY%%U!@G cyos$Ei|}S=_F33W+5DM9p9zcl$rjvM0MAA&IsgCw delta 205 zcmeAYUnI`X{F#?)+3mQE?7S>uY77hvu2wMy#-1TDX{C87hG25C0=MX9YnF0GJo1yR jS-2)YX5ENeiR9${Y)N>eH~X+R;V-1@TsAYwWJ diff --git a/cwavelet.pyc b/cwavelet.pyc index 5448e13223b6b48fcfa5a607c4007fe60a30e33e..db0000b3b27c781ab38c22b356c4015bc60f895d 100644 GIT binary patch delta 135 zcmZ1?JVBV9`7q_xxg<6K delta 171 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@@ -2,6 +2,7 @@ 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 if __name__ == '__main__': stockList = ['000725', '000783', '002167', '002505', '002600', '300315', '600000', '600011', '600048', '601001'] ChinaStockIndexList = [ @@ -14,10 +15,12 @@ "000016", # 上证50 "000012", # 国债指数 ] - code = [['002963', 'egold'], ['003321', 'eoil'], ['004744', 'eGEI'], ['110003', 'eSSE50'], ['110020', 'HS300'], - ['110031', 'eHSI'], ['161130', 'eNASDAQ100'], ['110028', 'anxinB'], ['110022', 'eConsumption '], - ['161125', 'SPX500']] + 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']] #均线策略 - macdmain(code) - stock_trader_main(code) - macd_live_main(code) \ No newline at end of file + macd=macdmain(code) + print macd + main1() + # stock_trader_main(code) + # macd_live_main(code) \ No newline at end of file diff --git a/stockdata/GAOpt.txt b/stockdata/GAOpt.txt new file mode 100644 index 0000000..7bef97c --- /dev/null +++ b/stockdata/GAOpt.txt @@ -0,0 +1,71 @@ +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 diff --git a/stockdata/GAOpt1.txt b/stockdata/GAOpt1.txt new file mode 100644 index 0000000..191ae63 --- /dev/null +++ b/stockdata/GAOpt1.txt @@ -0,0 +1,3 @@ +['110022', 'eConsumption '] +['MA', 'MACD', 'DMA', 'TRIX', 'AMA'] +[[[4, 50, 20], 47.274876600000027], [[5, 10, 37], 69.115635200000014], [[10, 41, 97], 11.771062300000013], [[12, 40, 94], 11.802638000000016], [[1, 39, 21], -95.177160000000001]] \ No newline at end of file diff --git a/stockdata/IgnoreListStock.txt b/stockdata/IgnoreListStock.txt new file mode 100644 index 0000000..e69de29 diff --git a/trade_process/__init__.pyc b/trade_process/__init__.pyc index a67d3a0b3e804de9defaba7dcab7048111c981a1..c6e600f170470461a5add69f21e175998054198a 100644 GIT binary patch delta 37 scmZ3%xP+0N`70.9*30max': str(todaydata[1] )+str('<=')+str( rmax*jizhipoints['5daysmax'][1])}) # def change_fenxi(change): - if(sum(change)/len(change)>=0.5): + if(sum(change)/len(change)>=0.46): sell_point.append({'mean5up':round(sum(change)/len(change),5)}) - elif(sum(change)/len(change)<=-0.5): + elif(sum(change)/len(change)<=-0.46): buy_points.append({'mean5up':round(sum(change)/len(change),5)}) big=[];small=[] @@ -291,27 +293,27 @@ def celue(funddata,jizhipoints): elif (change[i]<0): small.append(change[i]) if len(big)>=4 and change[0]<0.2 : - sell_point.append({'go up 4days':big}) + 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':small}) + 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']] - 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 = [] for i in code: # save(strfundcode=i ,numdays=365*1) - sb=fenxi(strfundcode=i, numdays=425 * 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=425 * 1) + sb=fenxi(strfundcode=i, numdays=365 * 1) if sb: buysell.append([i,sb]) return buysell @@ -370,7 +372,7 @@ def send_email(text): # 邮件正文内容 # print str(text[0]) - realText = 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]) + 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')) @@ -443,11 +445,15 @@ def deb_print(): M = now[12:14] S = now[15:] # print now - #print H,M, S + print H,M, S return H,M, S def check_time(H, M,S): - if(H == "14" and int(M) == 35 and S == "20"):#(H == "14" and M == "08" and S == "10") or + 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()) @@ -475,35 +481,25 @@ def check_time(H, M,S): # 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(): - a=1 - 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 14:35 '+'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() -def run(): + +def main1(): now = time.strftime("%y-%m-%d %H:%M:%S", time.localtime()) H = now[9:11] M = now[12:14] S = now[15:] - all_fund_list = main_zf() - if(int(H) ):#>= 19(H == "14" and M == "08" and S == "10") or + 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='' + 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) @@ -512,17 +508,33 @@ def run(): st = ' 买 ' for j in i[1]['buy']: for h in j: - st += h + str(j[h])+ ' ' + 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])+ ' ' + 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' + 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) - main1() + 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 @@ -546,7 +558,8 @@ def run(): # if st != '': # itchat.send(str(i[0]) + st, 'filehelper') # itchat.send(str(i[0]) + st,toUserName='@9f545f6d5c3f89aa63956c3f386733232f7176569f71a26f477909c02828d735') - run() + all_fund_list = main_zf() + main1() # f.close() # itchat.send("@fil@%s" % 'buysell.txt', 'filehelper') # for (name, fund_id) in ids.items(): diff --git a/trade_process/efund_mail2.pyc b/trade_process/efund_mail2.pyc index 0339ddb71ceed6212b18e2d145147420c1d4faf7..bdd70e38e5efa331dfd25b0a31f73cd7ee7e1ba6 100644 GIT binary patch delta 4513 zcmbtYZETxY6+ZX1<8S9XdE>-&oMdU;bV<{uN$5s5nx;**+;S{?Hgrw8Yi~j zKq_oJDcXK9K)T}xY(K_I7#kv6YpK!z4a5)rY`_mHA%UhfbQBQk3Sz4I2Z`t0*G`>? zIS5+6r}yLBbI!f@IUl#bPXB8;X#1z%6aRtn)CDU1vEkp(;Y)u{TnWA?T3!{i)wD|V zR2R)gxFHfmhM@6COMc1rC9Sy+%*P*^?Cm>wc5)4KJ-~lyzgVJmK!k-J^`3;*~5RTS=KpGCUNBSCIieLBg!2IDR#1#zz6eua;L8 zIgLu1w^8a9k?~TzmNHdj)~UHF3SfPqQ(f9AmA@+Qxr4F)z-#zu&aNzAlfaOzR$?J3 z3u9_1Ue6}8dW}-|1dbx>q2#E>Qw;^{jT)M_Q>sxb&dRJufAC?gDAHO0`79Nkd;FWg zPR1j(fX7(+h!i!u~KZ@swwSNZO(udm(t>37lsW1f7(yS5{ooR#&y zF>sTxi3f#W4*2Z`Rd;`yDIBNnz=ZG@62K*eK&Y5ET-sg0FI_E12 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zopC1tC7X{i@$eE*Gnqr+8h#~`laCAXO;!{vB%o~baX}qsJZd)ci&hcz)PAwac#Hu1 IyF=U%08=nm5dZ)H delta 315 zcmZ3fzf+%``7g?0T&-dZj6FkQ(n|AE48defaY=r1_T)HmsmWIv zcM?)0xA_}E373=9meRxt*~o*^-5rFkiaU^1q-BtJQOVz?y$Jmn6R diff --git a/trade_process/strategy/macd_back_test.py b/trade_process/strategy/macd_back_test.py index db5d3e0..f16e920 100644 --- a/trade_process/strategy/macd_back_test.py +++ b/trade_process/strategy/macd_back_test.py @@ -5,11 +5,14 @@ import pandas as pd import matplotlib.pyplot as plt -import os +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 +reload(sys) +sys.setdefaultencoding('utf-8') # AVR_SHORT = 12 # AVR_LONG = 40 @@ -100,7 +103,7 @@ def self_adaptive_ma(stock_data,Avg): # MA指标择时 (回测） -def select_Time_MA(stock_data, stockName,Avg): +def select_Time_MA(stock_data, stockName,*Avg): AVR_SHORT = Avg[0] AVR_LONG = Avg[1] AVR_vLONG = Avg[2] @@ -121,7 +124,8 @@ def select_Time_MA(stock_data, stockName,Avg): signals = [0]*(ma_list[1]+1) tradeTimes = 0 - bBuySignal = True + bBuySignal = True + currentSignal=[] for t in range(ma_list[1]+1, len(close_price)): signal = SIGNAL_DEFAULT @@ -144,15 +148,15 @@ def select_Time_MA(stock_data, stockName,Avg): 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, 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] + 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_MA'] = signals - + return [(now_money+now_count * close_price[-1]-start_money)/start_money*100,currentSignal] # fig = plt.figure(facecolor='white') # left, width = 0.1, 0.8 # rect1 = [left, 0.5, width, 0.4] @@ -173,7 +177,7 @@ def select_Time_MA(stock_data, stockName,Avg): #stock_data.sort('date', ascending=False, inplace=True) # MACD指标择时 (回测） -def select_Time_MACD(stock_data, stockName,Avg): +def select_Time_MACD(stock_data, stockName,*Avg): AVR_SHORT = Avg[0] AVR_LONG = Avg[1] AVR_vLONG = Avg[2] @@ -200,7 +204,8 @@ def select_Time_MACD(stock_data, stockName,Avg): signals = [0]*(ma_list[1]+1) tradeTimes = 0 - bBuySignal = True + bBuySignal = True + currentSignal=[] for t in range(ma_list[1]+1, len(close_price)): signal = SIGNAL_DEFAULT @@ -225,13 +230,13 @@ def select_Time_MACD(stock_data, stockName,Avg): 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, 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] + 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_MACD'] = signals - + return [(now_money+now_count * close_price[-1]-start_money)/start_money*100,currentSignal] # fig = plt.figure(facecolor='white') # left, width = 0.1, 0.8 # rect1 = [left, 0.5, width, 0.4] @@ -251,10 +256,10 @@ def select_Time_MACD(stock_data, stockName,Avg): # 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 + # return dif_price, dea_price, macd_price # DMA指标择时 (回测） -def select_Time_DMA(stock_data, stockName,Avg): +def select_Time_DMA(stock_data, stockName,*Avg): AVR_SHORT = Avg[0] AVR_LONG = Avg[1] AVR_vLONG = Avg[2] @@ -280,7 +285,8 @@ def select_Time_DMA(stock_data, stockName,Avg): signals = [0]*(ma_list[1]+1) tradeTimes = 0 - bBuySignal = True + bBuySignal = True + currentSignal=[] for t in range(ma_list[1]+1, len(close_price)): signal = SIGNAL_DEFAULT @@ -305,13 +311,13 @@ def select_Time_DMA(stock_data, stockName,Avg): 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, 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] + 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] # fig = plt.figure(facecolor='white') # left, width = 0.1, 0.8 # rect1 = [left, 0.5, width, 0.4] @@ -332,7 +338,7 @@ def select_Time_DMA(stock_data, stockName,Avg): #stock_data.sort('date', ascending=False, inplace=True) # DMA指标择时 (回测） -def select_Time_TRIX(stock_data, stockName,Avg): +def select_Time_TRIX(stock_data, stockName,*Avg): AVR_SHORT = Avg[0] AVR_LONG = Avg[1] start_money = 100000000 @@ -341,7 +347,6 @@ def select_Time_TRIX(stock_data, stockName,Avg): # 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 @@ -358,15 +363,14 @@ def select_Time_TRIX(stock_data, stockName,Avg): 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 + 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: @@ -387,16 +391,16 @@ def select_Time_TRIX(stock_data, stockName,Avg): 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, 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] + 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] # 组合择时指标 (回测） -def select_Time_Mix(stock_data, stockName,Avg): +def select_Time_Mix(stock_data, stockName,*Avg): AVR_SHORT = Avg[0] AVR_LONG = Avg[1] start_money = 100000000 @@ -460,12 +464,12 @@ def select_Time_Mix(stock_data, stockName,Avg): #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' \ + 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): +def select_Time_AMA(stock_data, stockName,*Avg): AVR_SHORT = Avg[0] AVR_LONG = Avg[1] percentage = 0.1 @@ -519,17 +523,17 @@ def select_Time_AMA(stock_data, stockName,Avg): #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:', i, ' signal:', signal + # print 't:', str(stock_data.ix[i, 'Date']), ' signal:', signal tradeTimes += 1 # 成本 - print u'盈利', record_sale + now_count * close_price[-1] - record_buy + # print stockName[0],stockName[1],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] + 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 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): @@ -557,7 +561,52 @@ def getMAStrategy(stockCsvPath, stockName, strategyName='MACD'): 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,funcname,func,stock_data, stockName): + datarange=[[1,20],[6,60],[10,100]] + datarange=datarange[:numParm] + genome = G1DList.G1DList(numParm) + genome.evaluator.set(func(fintness,funcname,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(45) + ga.setGenerations(45) + ga.setCrossoverRate(0.8) + ga.setMutationRate(0.2) + 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: @@ -586,12 +635,19 @@ def run(stockCsvPath, stockName): select_Time_AMA(stock_data, stockName) print '\n' +def match(select_method,stockName,lines): + contmatch=[] + for line in lines: + if line != '\n': + if(stockName[0] == line.split('|')[1])and(select_method == line.split('|')[0]): + contmatch.append([line.split('|')[0],line.split('|')[3]]) + return contmatch def macdmain(stockList): 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 @@ -602,40 +658,72 @@ def macdmain(stockList): # stockCsvNewPath = stockName + '_macd.csv' # processEMA(stockCsvPath, stockCsvNewPath) # stock_data = pd.read_csv(stockCsvPath) - fundlist = efund_mail2.get_histrydata(stockName, 365) + stockTxtNewPath = "./stockdata\\"+ 'GAOpt.txt' + fundlist = efund_mail2.get_histrydata(stockName, 190) stock_data = pd.DataFrame(fundlist[::-1], columns=['Date', 'Adj Close', 'countclose', 'change']) # self_adaptive_ma(stock_data) - Avg=[4,12,40] - Avg1=[3,11,40] - print u'-- MA 策略 --' - select_Time_MA(stock_data, stockName,Avg1) - - 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) - + Avg=([4,12,40]) + Avg1=([3,8,40]) + # select_method=['MA','MACD','DMA','TRIX','AMA'] + select_method = [ 'DMA','MA','MACD','TRIX'] + 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')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('gb2312')+'|') + f.write(str(best[0]).split('[')[1].split(']')[0]+'|'+str(best[1])) + f.write('\n') + f.close() + Avg = best[0] + else: + # AvgParm.append([int(contmatch[0].split(',')[0]),int(contmatch[0].split(',')[1]),int(contmatch[0].split(',')[2])]) + Avg = [int(contmatch[0][1].split(',')[0]),int(contmatch[0][1].split(',')[1]),int(contmatch[0][1].split(',')[2])] + 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 if __name__ == "__main__": print "main begin" stockList=['000725','000783','002167','002505','002600','300315','600000','600011','600048','601001'] downloadAllHistoryAShareData(stockList) - #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: diff --git 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