a multi-symbol back-tester based on position handler

multisymbol.py

import matplotlib.pyplot as plt
from datetime import datetime
import alpaca_trade_api as tradeapi

api = tradeapi.REST(key_id=<your key id>,secret_key=<your secret key>)

backtester = positionHandler(startingBalance=10000,liveTrading=False) # Using position handler from previous article

backtestSymbolList = ["SPY","AAPL","BBRY"]
positionSizing = 0.33

cashBalanceList = []

timeSteps = len(dateList) # It can be helpful to have a list of the dates associated with their respective bars
barIterator = 0

while barIterator < timeSteps:
	for symbol in backtestSymbolList:
		# Historical data input has to be adjusted for your own data pipeline
		# Simple moving average cross strategy
		price = data[symbol]["close"]
		SMA20 = data[symbol]["SMA20"]
		SMA50 = data[symbol]["SMA50"]
		
		if SMA20 > SMA50:
			openPosition = backtester.returnOpenPosition(symbol)
			
			if openPosition = 0:
				cashBalance = backtester.cashBalance
			
				targetPositionSize = cashBalance / (price / positionSizing) # Calculates required position size
				
				backtester.placeOrder(symbol,targetPositionSize,"buy","market","gtc") # Market order to open position
		
		else:
			openPosition = backtester.returnOpenPosition(symbol)
			
			backtester.placeOrder(symbol,openPosition,"sell","market","gtc") # Market order to fully close position
	cashBalance.append(backtester.cashBalance)
	
tradeHistory = backtester.tradeHistory
positionHistory = backtester.positionHistory
finalBalance = backtester.cashBalance


# Defines the plot for each trading symbol
f, ax = plt.subplots()
f.suptitle(symbol)

timeList = []
for date in dateList:
	timeList.append(datetime.strptime(date,'%Y-%m-%dT%H:%M:%SZ'))
timeList = np.array(timeList)
# Plots market data and indicators
ax.plot(timeList,cashBalanceList,label=symbol,color="black")

# Add functions to analyse performance over time and calculate metrics