backtesting-trading-strategies
Installation
Summary
Backtest trading strategies against historical data with performance metrics and parameter optimization.
- Includes 8 pre-built strategies (SMA, EMA, RSI, MACD, Bollinger Bands, Breakout, Mean Reversion, Momentum) for crypto and traditional assets
- Calculates comprehensive metrics: Sharpe, Sortino, Calmar ratios, max drawdown, VaR, volatility, win rate, and profit factor
- Supports parameter grid search optimization to find best strategy combinations
- Generates equity curves, trade logs, and performance summaries saved to reports directory
- Requires pandas, numpy, yfinance; optional ta-lib and scipy for advanced analysis
SKILL.md
Backtesting Trading Strategies
Overview
Validate trading strategies against historical data before risking real capital. This skill provides a complete backtesting framework with 8 built-in strategies, comprehensive performance metrics, and parameter optimization.
Key Features:
- 8 pre-built trading strategies (SMA, EMA, RSI, MACD, Bollinger, Breakout, Mean Reversion, Momentum)
- Full performance metrics (Sharpe, Sortino, Calmar, VaR, max drawdown)
- Parameter grid search optimization
- Equity curve visualization
- Trade-by-trade analysis
Prerequisites
Install required dependencies:
set -euo pipefail
pip install pandas numpy yfinance matplotlib
Related skills