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backtest.py
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import pandas as pd
import plotlyTest as plt
import numpy as np
import copy
#tohlct
class bt:
"""
By default: \n
Long is from candle.high to candle.low \n
Short is from candle.low to candle.high \n
"""
order = {
"type" : None,
"order": {
"coinAmt" : 0,
"usdtAmt" : 0,
"enterPrice" : 0,
"exitPrice" : 0,
"entryTime" : "",
"exitTime" : ""
}
}
candleColumns = {}
i=0
leverage = 1
def __columnCtrl(self, data):
for column in data :
try:
tmp = self.candleColumns[column]
except:
self.candleColumns[column] = self.i
self.i += 1
def __appendMain(self, data):
self.mainData = pd.concat([self.mainData, data.drop(['openTime'], axis=1)], axis=1, join='inner')
self.__columnCtrl(data)
def __parseCondition(self, condition):
arr = condition.split()
row = None
for i in range(len(arr)):
try:
row = str(self.candleColumns[arr[i]])
arr[i] = 'self.dataNP[' + row + '][column]'
except:
continue
return ' '.join(arr)
def __init__(self, candles, portfolio=0) :
self.candles = candles.copy()
self.portfolio = portfolio
self.mainData = candles.copy()
self.orderList = []
for column in self.candles:
if column == 'openTime' or column == 'closeTime' :
continue
self.candles[column] = pd.to_numeric(self.candles[column], downcast="float")
self.mainData[column] = pd.to_numeric(self.mainData[column], downcast="float")
# plt.plotCandles(self.candles)
self.__columnCtrl(candles)
tempDF = self.candles.copy()
tempDF['temp'] = False
self.long = pd.DataFrame({
'entry': tempDF['temp'],
'exit' : tempDF['temp']
})
self.short = pd.DataFrame({
'entry': tempDF['temp'],
'exit' : tempDF['temp']
})
del(tempDF)
def initNumPy(self):
self.dataNP = ((self.mainData).to_numpy()).transpose()
self.npRow, self.npColumn = np.shape(self.dataNP)
def setPortfolio(self, start=10, lev=1):
self.portfolio = start
self.leverage = lev
def setLineSeriesData(self, data):
"""
Pass 'openTime' and all lineSeries
"""
self.lineSeriesData = data.copy()
self.__appendMain(data)
def appendLineSeriesData(self, data):
self.lineSeriesData = pd.concat([self.lineSeriesData, data], axis=1, join='inner')
self.__appendMain(data)
self.mainData.dropna().reset_index(drop=True)
def defineOrder(self, entryCondition, exitCondition, stoploss=1, longFlag = True):
# condition = 'open < mcb and close > mcb'
"""
orderType = 'long' or 'short'
"""
if longFlag :
orderType = 'long'
orderFlag = 1
else :
orderType = 'short'
orderFlag = -1
entryCondition = self.__parseCondition(entryCondition)
exitCondition = self.__parseCondition(exitCondition)
for column in range(self.npColumn):
if self.order["type"] is None and eval(entryCondition):
if longFlag:
# entryPrice = self.dataNP[2][column]
entryPrice = self.dataNP[4][column]
else :
# entryPrice = self.dataNP[3][column]
entryPrice = self.dataNP[4][column]
self.order['type'] = orderType
self.order['order']['coinAmt'] = (self.portfolio*self.leverage)/entryPrice
self.order['order']['usdtAmt'] = self.portfolio
self.order['order']['enterPrice'] = entryPrice
self.order['order']['entryTime'] = self.dataNP[0][column]
self.long.iloc[column,0] = True
continue
if self.order["type"] is not(None) :
# print('no entry')
exitPrice = self.dataNP[4][column]
entryPrice = self.order['order']['enterPrice']
pnl = (entryPrice - exitPrice)*100 / entryPrice
pnlS = (exitPrice - entryPrice)*100 / entryPrice
# print("Entry at "+ str(self.order['order']['enterPrice']))
# print(str(self.order['type'])+" "+str(pnl))
if (longFlag and pnl > stoploss) or (not(longFlag) and pnlS > (stoploss)):
# print('enter '+str(self.order['type'])+' stop price with pnl '+ str(pnl))
self.order['order']['exitPrice'] = exitPrice
self.order['order']['exitTime'] = self.dataNP[0][column]
coinsAmt = self.order['order']['coinAmt']
entryPrice = self.order['order']['enterPrice']
self.portfolio = orderFlag * ((coinsAmt * exitPrice) - (self.order['order']['usdtAmt'] * self.leverage)) + self.order['order']['usdtAmt']
profit = self.portfolio - self.order['order']['usdtAmt']
coinChange = exitPrice - self.order['order']['enterPrice']
currentOrder = copy.deepcopy(self.order)
currentOrder['profit'] = profit
currentOrder['profitPercent'] = profit * 100 / self.order['order']['usdtAmt']
currentOrder['coinChange'] = coinChange
self.orderList.append(currentOrder)
del(currentOrder)
self.order['type'] = None
self.order['order']['coinAmt'] = 0
self.order['order']['usdtAmt'] = self.portfolio
self.order['order']['enterPrice'] = 0
self.order['order']['exitPrice'] = 0
self.order['order']['entryTime'] = ""
self.order['order']['exitTime'] = ""
self.long.iloc[column,1] = True
if self.order["type"] is not(None) and eval(exitCondition):
if longFlag:
# exitPrice = self.dataNP[3][column]
exitPrice = self.dataNP[4][column]
else :
# exitPrice = self.dataNP[2][column]
exitPrice = self.dataNP[4][column]
self.order['order']['exitPrice'] = exitPrice
self.order['order']['exitTime'] = self.dataNP[0][column]
coinsAmt = self.order['order']['coinAmt']
entryPrice = self.order['order']['enterPrice']
self.portfolio = orderFlag * ((coinsAmt * exitPrice) - (self.order['order']['usdtAmt'] * self.leverage)) + self.order['order']['usdtAmt']
profit = self.portfolio - self.order['order']['usdtAmt']
coinChange = exitPrice - self.order['order']['enterPrice']
currentOrder = copy.deepcopy(self.order)
currentOrder['profit'] = profit
currentOrder['profitPercent'] = profit * 100 / self.order['order']['usdtAmt']
currentOrder['coinChange'] = coinChange
self.orderList.append(currentOrder)
del(currentOrder)
self.order['type'] = None
self.order['order']['coinAmt'] = 0
self.order['order']['usdtAmt'] = self.portfolio
self.order['order']['enterPrice'] = 0
self.order['order']['exitPrice'] = 0
self.order['order']['entryTime'] = ""
self.order['order']['exitTime'] = ""
self.long.iloc[column,1] = True
def printCandles(self):
"""
plots candles using plotly.py
"""
print(self.candles)
def getOrderList(self):
"""
Returns list of orders in pandas Dataframe
"""
return pd.json_normalize(self.orderList)
def plotCandles(self):
plt.initSubPlots(1, 1)
plt.plotCandles(self.candles)
def plotLineSeries(self):
plt.plotLines(self.lineSeriesData)
def plotOrders(self):
buySeries = self.long['entry']
sellSeries = self.long['exit']
openTimeSeries = self.mainData['openTime']
buyDF = pd.DataFrame({
'openTime': openTimeSeries,
'buy': buySeries
})
sellDF = pd.DataFrame({
'openTime': openTimeSeries,
'sell': sellSeries
})
buyDF = buyDF[buyDF['buy'] == True]
sellDF = sellDF[sellDF['sell'] == True]
buyDF['openTime'] = pd.to_datetime(buyDF['openTime']/1000, unit='s')
sellDF['openTime'] = pd.to_datetime(sellDF['openTime']/1000, unit='s')
buyTime = buyDF['openTime'].to_numpy()
sellTime = sellDF['openTime'].to_numpy()
# print(buyTime)
plt.plotBuy(buyTime)
plt.plotSell(sellTime)
del(buyDF, sellDF, buyTime, sellTime)
def showPlot(self,file="output/plot.html"):
plt.showPlot(file)
if __name__ == "__main__":
import getData
candles = getData.getCandles(
symbol="BTCUSDT",
leng='1 day',
time=1,
klineType="FUTURES"
)
df = pd.DataFrame(candles)
df = df.drop([7,8,9,10,11], axis=1)
df.columns = ['openTime', 'open', 'high', 'low', 'close', 'volume', 'closeTime']
from indis.panda_hccp import HCCP
class strategy(bt):
def __init__(self, candles, portfolio=0):
super().__init__(candles, portfolio)
self.hccp = HCCP(self.candles)
self.setLineSeriesData(self.hccp)
def plotHccp(self):
self.plotLineSeries()
strat = strategy(df,10)
strat.initNumPy()
strat.defineOrder('open < mcb and close > mcb', 'high > mct')
strat.defineOrder('close > sct and open < sct' , 'low < mcb', longFlag=False)
df = strat.getOrderList()
print(strat.portfolio)
df.to_csv('orders.csv')
strat.plotCandles()
strat.plotLineSeries()
strat.plotOrders()
strat.showPlot()