backtesting-trading-strategies

What problem does it solve?

Backtest trading strategies against historical data to compare performance and stability before risking real capital.

Core Features & Use Cases

• Built-in backtesting framework with multiple strategies (SMA, EMA, RSI, MACD, Bollinger, Breakout, Mean Reversion, Momentum)
• Generates comprehensive performance metrics (Total Return, CAGR, Sharpe, Sortino, Calmar) and risk measures (Max Drawdown, VaR)
• Exports equity curves, trade logs, and chart-ready visuals for reporting and decision making
• Use Case: validate strategy signals across assets and timeframes, then optimize parameters to improve robustness

Quick Start

Run a backtest with a chosen strategy and symbol, for example: python scripts/backtest.py --strategy sma_crossover --symbol BTC-USD --period 1y --capital 10000 --params '{"fast_period": 20, "slow_period": 50}'