BackTest.py

BackTest.py

# -*- coding: utf-8 -*-
import poloniex
import time
import datetime
from sklearn import tree
import matplotlib.pyplot as plt

def main():
    # --トレーニング用パラメータ
    theNumberOfTrainData=29   #トレーニングデータ数
    theNumberOfTrainAndKyoushiSet=30  #トレーニングデータと教師データのセット数
    studyTrialTimes = 200 #予測のトライアル数(予測結果がばらつくので1回の予測結果を出すために実施する試行回数(取りあえずこのままにしておいてください。)

    # --バックテストパラメータ
    plotGraphFlag = 1  # バックテスト結果の資金グラフをプロットしますか？yes:1。no:0
    initialFund = 1000  # 初期資金
    spread = 0  #取引スプレット(手数料)
    backTestDays = 30*2 # バックテスト期間(日)

    dateBTC, dataBTC = getDataPoloniex()
    dateBTC.reverse()
    dataBTC.reverse()
    data = changeData(dataBTC)

    print('-----------------------------------------')
    print('バックテストの実施')
    print('-----------------------------------------')
    print('※過去のデータから資金上昇率を算出します。上昇シグナル時に現物で買い。下降シグナル時は何もしません。ポジションは1日ごとに決裁します。')
    seitouritsuUp, seitouritsuDown, increasedFundRatio, increasedBTCPriceRatio, simOutputStr = backTest(data, dateBTC, dataBTC, theNumberOfTrainData,theNumberOfTrainAndKyoushiSet, studyTrialTimes, initialFund, spread, backTestDays, plotGraphFlag)
    print(simOutputStr)

def backTest(data, dateBTC, dataBTC, theNumberOfTrainData,theNumberOfTrainAndKyoushiSet, studyTrialTimes, initialFund, spread, backTestDays, plotGraphFlag):
    realValue = []
    predictionValue = []
    fund = [initialFund]
    pastDay = 0
    seitouUp = 0
    seitouDown = 0
    for trainStartDay in range(backTestDays, 0, -1):
        y_Prediction, y = predictionTommorowBTCForBackTest(data, trainStartDay, theNumberOfTrainData, theNumberOfTrainAndKyoushiSet, studyTrialTimes)
        realValue.append(y)
        predictionValue.append(y_Prediction)
        pastDay += 1
        if y_Prediction == y:
            if y_Prediction == 1:
                seitouUp += 1
                fund.append(fund[pastDay - 1] * (1 + abs(data[trainStartDay - 1]) - spread))
            else:
                seitouDown += 1
                #---fund.append(fund[pastDay - 1] * (1 + abs(data[trainStartDay - 1]) - spread))
                fund.append(fund[pastDay - 1])
        else:
            if y_Prediction == 1:
                fund.append(fund[pastDay - 1] * (1 - abs(data[trainStartDay - 1]) - spread))
            else:
                #---fund.append(fund[pastDay - 1] * (1 - abs(data[trainStartDay - 1]) - spread))
                fund.append(fund[pastDay - 1])

    # ----バックテスト結果出力
    outputStr = '・トレーニングデータ数: ' + str(theNumberOfTrainData) +'\n'
    outputStr += '・トレーニングデータと教師データのセット数: ' + str(theNumberOfTrainAndKyoushiSet)+'\n'
    outputStr += '・バックテスト期間: 過去' + str(backTestDays) + '日\n'
    seitouritsuUp = float(seitouUp) / sum(predictionValue)
    seitouritsuDown = float(seitouDown) / (backTestDays - sum(predictionValue))
    outputStr += '・正答率(上昇時): ' + str(round(seitouritsuUp * 100,1)) + '%' + '\n'
    outputStr += '・正答率(下降時): ' + str(round(seitouritsuDown * 100,1)) + '%' + '\n'
    trainStartDay=0
    dataBTCOnBackTest = dataBTC[trainStartDay:trainStartDay+backTestDays+1]
    dataBTCOnBackTest.reverse()
    increasedFundRatio=(fund[-1]-fund[0])/fund[0]
    increasedBTCPriceRatio=(dataBTCOnBackTest[-1]-dataBTCOnBackTest[0])/dataBTCOnBackTest[0]
    outputStr += '・資金上昇率: ' + str(round(increasedFundRatio*100,1)) + '% (初期資金は$' +str(fund[0])+'。最終資金は$' +str(fund[-1])+'。)' + '\n'
    outputStr += '・BTC価格上昇率: ' + str(round(increasedBTCPriceRatio*100,1)) + '% (初期BTC価格は$' +str(dataBTCOnBackTest[0])+'。最終BTC価格は$' +str(dataBTCOnBackTest[-1])+'。)' + '\n'

    # ----資金推移グラフ
    if(plotGraphFlag):
        # ----Plot Fund
        dateBTCOnBackTest = dateBTC[trainStartDay:trainStartDay + backTestDays + 1]
        dateBTCOnBackTest.reverse()
        fig1, ax1 = plt.subplots(figsize=(11, 6))
        p1, = ax1.plot(dateBTCOnBackTest, fund, '-ob')
        ax1.set_title("Simulation with past data")
        ax1.set_xlabel("Day")
        ax1.set_ylabel("Fund[$]")
        plt.grid(fig1)
        # ----Plot BTC Price
        ax2 = ax1.twinx()
        p2, = ax2.plot(dateBTCOnBackTest, dataBTCOnBackTest, '-or')
        ax2.set_ylabel('BTC price[$]')
        ax1.legend([p1, p2], ["Fund", "BTC price"], loc="upper left")
        plt.show(fig1)
    return seitouritsuUp, seitouritsuDown, increasedFundRatio, increasedBTCPriceRatio, outputStr

def getDataPoloniex():
    polo = poloniex.Poloniex()
    polo.timeout = 2
    chartUSDT_BTC = polo.returnChartData('USDT_BTC', period=polo.DAY, start=time.time() - polo.DAY * 500, end=time.time())
    tmpDate = [chartUSDT_BTC[i]['date'] for i in range(len(chartUSDT_BTC))]
    date = [datetime.datetime.fromtimestamp(tmpDate[i]).date() for i in range(len(tmpDate))]
    data = [float(chartUSDT_BTC[i]['open']) for i in range(len(chartUSDT_BTC))]
    return date ,data

def changeData(data):
    newData=[]
    for i in range(0, len(data) - 1):
        newData.append(float(data[i] - data[i + 1]) / data[i + 1])
    newData.append(0)
    return newData

def preparationTrainAndKyoushiSets(data,trainStartDay,theNumberOfTrainData,theNumberOfTrainAndKyoushiSet):
    train_X = []
    train_y = []
    for i in range(0,theNumberOfTrainAndKyoushiSet):
        train_X.append(data[trainStartDay+1+i:trainStartDay+theNumberOfTrainData+1+i])
        train_y.append([int(0 < data[trainStartDay+i])])
    return train_X, train_y

def predictionTommorowBTCForBackTest(data, trainStartDay, theNumberOfTrainData,theNumberOfTrainAndKyoushiSet, studyTrialTimes):
    train_X, train_y = preparationTrainAndKyoushiSets(data, trainStartDay, theNumberOfTrainData,theNumberOfTrainAndKyoushiSet)
    X=data[trainStartDay:trainStartDay+theNumberOfTrainData]
    y = int(0 < data[trainStartDay-1])
    y_PredictionTommorow=[]
    for i in range(0, theNumberOfTrainAndKyoushiSet):
        clf = tree.DecisionTreeClassifier()
        clf.fit(train_X, train_y)
        y_PredictionTommorow.append(clf.predict([X])[0])
    upOrDownRatio = sum(y_PredictionTommorow) * 1.0 / len(y_PredictionTommorow)
    return int(upOrDownRatio>0.5),y

if __name__ == "__main__":
    main()