Agent skill · data analytics · aaaaqwq
backtesting-trading-strategies
Validate trading strategies against historical data before risking real capital. This skill provides a complete backtesting framework with 8 built-in
Why this skill is useful
Adds a complete backtesting framework with executable scripts for fetching data, running backtests, and optimizing parameters that enhance trading strategy validation.
What it needs
Requires matplotlib, numpy, pandas, yfinance installed locally. About 3k tokens when loaded. Last updated 2026-08-06. 83 stars on the source repository.
What this skill does
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: Optional for advanced features: Instructions Step 1: Fetch Historical Data Data is cached to {baseDir}/data/{symbol}{interval}.csv for reuse. Step 2: Run Backtest Basic backtest with default parameters: Advanced backtest with custom parameters: Step 3: Analyze Results Results are saved to {baseDir}/reports/ including: summary.txt - Performance metrics trades.csv - Trade log equity.csv - Equity curve data chart.png - Visual equity curve Step 4: Optimize Parameters Find optimal parameters via grid search: Output Performance Metrics Metric Description -------- ------------- Total Return Overall percentage gain/loss CAGR Compound annual growth rate Sharpe Ratio Risk-adjusted return (target: >1.5) Sortino Ratio Downside risk-adjusted return Calmar Ratio Return divided by max drawdown Risk Metrics Metric Description -------- ------------- Max Drawdown Largest peak-to-trough decline VaR (95%) Value at Risk at 95% confidence CVaR (95%) Expected loss beyond VaR Volatility Annualized standard deviation Trade Statistics Metric Description -------- ------------- Total Trades Number of round-trip trades Win Rate Percentage of profitable trades Profit Factor Gross profit divided by gross loss Expectancy Expected value per trade Example Output Supported Strategies Strategy Description Key Parameters ---------- ------------- ---------------- smacrossover Simple moving average crossover fastperiod, slowperiod emac …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aaaaqwq/trading-strategy-backtester