BinanceBacktesting/BinanceBacktestingPython at main · GeniusMathematicsConsultants/BinanceBacktesting · GitHub

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.optimize import minimize

data = pd.read_csv("Binance_BTCUSDT_d.csv")
data = data.iloc[::-1] #reverses order of dates
close = np.array(data.close)

def moving_avg(close, index, days, alpha):
    # float values allowed for days for use with optimization routines.
    partial = days - np.floor(days)
    days = int(np.floor(days))

    weights = [alpha**i for i in range(days)]
    av_period = list(close[max(index - days + 1, 0): index+1])

    if partial > 0:
        weights = [alpha**(days)*partial] + weights
        av_period = [close[max(index - days, 0)]] + av_period

    return np.average(av_period, weights=weights)

def calculate_strategy(close, short_days, long_days, alpha, start_offset, threshold):
    strategy = [0]*(len(close) - start_offset)
    short = [0]*(len(close) - start_offset)
    long = [0]*(len(close) - start_offset)
    boughtorsold = 1
    for i in range(0, len(close) - start_offset):
        short[i] = moving_avg(close, i + start_offset, short_days, alpha)
        long[i] = moving_avg(close, i + start_offset, long_days, alpha)
        if short[i] >= long[i]*(1+threshold) and boughtorsold != 1:
            boughtorsold = 1
            strategy[i] = 1
        if short[i] <= long[i]*(1-threshold) and boughtorsold != -1:
                boughtorsold = -1
                strategy[i] = -1

    return (strategy, short, long)

def price_strategy(strategy, close, short_days, long_days, alpha, start_offset):
    cash = -close[start_offset]  # subtract initial purchase cost
    bought = 1
    for i in range(0, len(close) - start_offset):
        #print(strategy[i])
        if strategy[i] == 1:
            # Note the factor of 2 is due to selling and shorting
            cash = cash - 2*close[i + start_offset]
            bought = 1
        if strategy[i] == -1:
            cash = cash + 2*close[i + start_offset]
            bought = -1
    # Sell at end
    if bought == 1:
        cash = cash + close[-1]
    if bought == -1:
        cash = cash - close[-1]
    return cash

def graph_strategy(close, strategy, short, long, start_offset):
    x = list(range(0, len(close) - start_offset))
    plt.figure(0)
    plt.plot(x, close[start_offset:], label = "BTCUSDT")
    plt.plot(x, short, label = "short_av")
    plt.plot(x, long, label = "long_av")
    buyidx = []
    sellidx = []
    for i in range(len(strategy)):
        if strategy[i] == 1:
            buyidx.append(i)
        elif strategy[i] == -1:
            sellidx.append(i)
    plt.scatter(buyidx, [0]*len(buyidx), label = "Buy", marker="|")
    plt.scatter(sellidx, [0]*len(sellidx), label = "Sell", marker="|")
    plt.title('Moving average crossover')
    plt.xlabel('Timestep')
    plt.ylabel('Price')
    plt.legend()

def plot_param(x, close, start_offset, param_index, param_values):
    profit = []
    x2 = x.copy()
    for value in param_values:
        x2[param_index] = value
        short_days = x2[0]
        long_days = x2[1]
        alpha = x2[2]
        threshold = x2[3]
        (strat, short, long) =  calculate_strategy(close, short_days, long_days, alpha, start_offset, threshold)
        profit.append(price_strategy(strat, close, short_days, long_days, alpha, start_offset))
    plt.figure(param_index+1)
    param_names = ["short_days", "long_days", "alpha", "threshold"]
    name = param_names[param_index]
    plt.title('Strategy profit vs ' + name)
    plt.xlabel(name)
    plt.ylabel('Profit')
    plt.plot(param_values, profit, label = "Profit")

def evaluate_params(x, close, start_offset):
    short_days = x[0]
    long_days = x[1]
    alpha = x[2]
    threshold = x[3]
    (strat1, short, long) =  calculate_strategy(close, short_days, long_days, alpha, start_offset, threshold)
    profit = price_strategy(strat1, close, short_days, long_days, alpha, start_offset)
    return -profit #Since we minimise

#Initial strategy parameters.
short_days = 15
long_days = 50
alpha = 1
start_offset = long_days
threshold = 0.05

#short_days = 10
#long_days = 30
#alpha = 0.75
#alpha = 0.92
#threshold = 0.02

x = [short_days, long_days, alpha, threshold]

#Price strategy
(strat1, short, long) =  calculate_strategy(close, short_days, long_days, alpha, start_offset, threshold)
profit = price_strategy(strat1, close, short_days, long_days, alpha, start_offset)
print("Strategy profit is: " + str(profit))
print("Buy and hold profit is: " + str(close[-1] - close[start_offset]))

#Graph strategy
graph_strategy(close, strat1, short, long, start_offset)

#Graph parameter dependence
plot_param(x, close, start_offset, 2, np.arange(0.7, 1, 0.02))
plot_param(x, close, start_offset, 3, np.arange(0.01, 0.1, 0.001))
plot_param(x, close, start_offset, 0, np.arange(5, long_days, 1))
plot_param(x, close, start_offset, 1, np.arange(short_days, 50, 1))

#Optimization
#x0 = [short_days, long_days, alpha, threshold]
#start_offset = 70

#bnds = ((1, start_offset), (1, start_offset), (0.001,1), (0,None))
#cons = ({'type': 'ineq', 'fun': lambda x:  x[1] - x[0]})

#result = minimize(evaluate_params, x0, args=(close, start_offset), bounds=bnds, constraints=cons, method='BFGS')
#print(result)
#evaluate_params(result.x, close, start_offset)