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Stock Trading Backtest Framework

A Python framework for backtesting trading strategies with support for multiple order types and technical indicators.

Features

Trading Operations

  • Market Orders (Buy/Sell)
  • Limit Orders
  • GTC (Good Till Cancelled) Orders
  • Short
  • Commission Handling
    • Flat Fee
    • Percentage
    • Per Share

Technical Indicators

  • Simple Moving Average (SMA)
  • Exponential Moving Average (EMA)
  • Relative Strength Index (RSI)
  • Bollinger Bands
  • MACD (Moving Average Convergence Divergence)
  • Crossover Detection
  • Volume Weighted Average Price (VWAP)
  • Average True Range (ATR)

Portfolio Management

  • Position Tracking
  • Transaction History
  • Portfolio Valuation
  • Cash Management
  • Performance Metrics

Example

import pyBacktest as pbt
from pyBacktest.strategy import Strategy
from pyBacktest.utils import calculateSMA, analyzeResults, calculateMACD
from pyBacktest.tradeTypes import TradeType
from datetime import datetime
import pandas as pd

class SMACross(Strategy):
    def setup(self) -> None:
        self.has_position = False
        self.entry_price = 0
        self.sma20 = calculateSMA(self.data['Close'], 20)
        self.sma50 = calculateSMA(self.data['Close'], 50)

    def step(self, row: pd.Series) -> None:
        if row.name not in self.sma20.index or row.name not in self.sma50.index:
            return

        if self.backtest.cash>row['Close'] and self.sma20[row.name] > self.sma50[row.name]:
            self.has_position = True
            self.entry_price = row['Close']
            self.backtest.trade(TradeType.BUY, int(self.backtest.cash*0.95/row["Close"]), row['Close'], "DAY")
        elif self.has_position and self.sma20[row.name] < self.sma50[row.name]:
            self.has_position = False
            self.backtest.trade(TradeType.SELL, self.current_position, row['Close'], row.name)

class MACDStrategy(Strategy):
    def setup(self) -> None:
        self.macd, self.signal, self.histogram = calculateMACD(self.data['Close'])

    def step(self, row: pd.Series) -> None:
        if row.name not in self.macd.index:
            return

        if self.histogram[row.name] > 0:
            numShares = int(self.backtest.cash / row['Close'])
            trade_cost = self.backtest.calculate_trade_cost(TradeType.BUY, numShares, row['Close'])
            if self.backtest.cash >= trade_cost:
                self.backtest.trade(TradeType.BUY, numShares, row['Close'], "DAY")
        elif self.histogram[row.name] < 0 and self.backtest.getPosition() > 0:
            numShares = self.backtest.getPosition()
            trade_cost = self.backtest.calculate_trade_cost(TradeType.SELL, numShares, row['Close'])
            if self.backtest.cash >= trade_cost:
                self.backtest.trade(TradeType.SELL, numShares, row['Close'], row.name)

if __name__ == "__main__":
    sma_backtest = pbt.Backtest(
        strategy=SMACross(),
        ticker='AAPL',
        commision=1.00,
        startDate=datetime(2020, 1, 1),
        endDate=datetime(2024, 1, 1), 
        cash=10000
    )
    
    macd_backtest = pbt.Backtest(
        strategy=MACDStrategy(),
        ticker='AAPL',
        commision=1.00,
        startDate=datetime(2020, 1, 1),
        endDate=datetime(2024, 1, 1), 
        cash=10000
    )
    
    sma_results = sma_backtest.run()
    macd_results = macd_backtest.run()
    
    analyzeResults(sma_results)
    analyzeResults(macd_results)
    
    comparison = pbt.utils.compareBacktests(sma_results, macd_results)
    print("\nComparison of SMA and MACD Strategies:")
    print(f"Final Value Difference: ${comparison['final_value_diff']:,.2f}")
    print(f"Total Return Difference: {comparison['total_return_diff']:.2f}%")
    print(f"Number of Transactions Difference: {comparison['num_transactions_diff']}")
    print(f"Better Strategy: {comparison['better_strategy']}")