backtesting-trading-strategies

To backtest a trading strategy using historical data, run the provided Python script with parameters for strategy type, symbol, period, and capital. It applies signals to historical data and outputs performance metrics along with equity curves.

Evaluating trading strategies generates performance metrics including Total Return, CAGR, Sharpe, Sortino, and Calmar ratios. It also calculates risk measures like Maximum Drawdown and Value at Risk to assess strategy stability.

Yes, you can optimize parameters for built-in strategies like SMA crossover, EMA, RSI, MACD, and Bollinger Bands. Passing custom JSON parameters to the script allows parameterized backtests to improve robustness across assets.

Yes, the backtesting framework supports fetching historical data for cryptocurrencies. It utilizes the yfinance dependency to download historical price data for assets like BTC-USD across specified timeframes.

The best way to compare multiple trading strategies is running parameterized backtests across different assets and timeframes. The framework exports equity curves and trade logs, enabling side-by-side performance and risk analysis.

Pandas and matplotlib are required for backtesting trading strategies to manipulate historical price data arrays and visualize equity curves. They enable exporting chart-ready visual outputs for reporting and decision making.