multisymbol.py

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

from positionhandling import positionHandler
import loader

api = tradeapi.REST()

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

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

cashBalanceList = []

dateList = loader.getDateList()

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

while barIterator < timeSteps:
    data = loader.get_data(barIterator)
    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

                # Calculates required position size
                targetPositionSize = int(cashBalance / (price / positionSizing))

                backtester.placeOrder(
                    symbol,
                    targetPositionSize,
                    "buy",
                    "market",
                    "gtc")  # Market order to open position

        else:
            openPosition = backtester.returnOpenPosition(symbol)

            # Market order to fully close position
            backtester.placeOrder(
                symbol, openPosition, "sell", "market", "gtc")
    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