Backtest trading strategies with historical data, performance metrics, and risk analysis
Installation
npx claude-plugins install @jeremylongshore/claude-code-plugins-plus/trading-strategy-backtester
Contents
Folders: commands, skills
Files: LICENSE, README.md
Documentation
Comprehensive backtesting framework for trading strategies with historical data analysis, performance metrics, and parameter optimization.
Features
Strategy Library
- Moving Average Crossover
- RSI Overbought/Oversold
- MACD Signal Line
- Breakout Trading
- Mean Reversion
- Momentum Trading
- Pairs Trading
- Grid Trading
Performance Metrics
- Total Return & Win Rate
- Sharpe & Sortino Ratios
- Maximum Drawdown
- Profit Factor
- Calmar Ratio
- Recovery Factor
Risk Analysis
- Value at Risk (VaR)
- Conditional VaR
- Consecutive Losses
- Ulcer Index
- Risk-Adjusted Returns
Installation
/plugin install trading-strategy-backtester@claude-code-plugins-plus
Usage
/backtest-strategy
Testing moving average strategy:
- Symbol: BTC/USDT
- Period: 1 year
- Capital: $10,000
- Parameters: 50/200 MA
Commands
| Command | Description | Shortcut |
|---|---|---|
/backtest-strategy | Run backtest | bs |
/optimize-parameters | Parameter optimization | op |
/compare-strategies | Strategy comparison | cs |
/walk-forward | Walk-forward analysis | wf |
Strategies
Moving Average
- Golden/Death Cross signals
- Customizable periods
- Multiple MA types
RSI Strategy
- Oversold/Overbought levels
- Divergence detection
- Dynamic thresholds
MACD Strategy
- Signal line crossovers
- Histogram analysis
- Zero-line crosses
License
MIT License
Built for traders by Intent Solutions IO
Included Skills
This plugin includes 1 skill definition:
backtesting-trading-strategies
|
View skill definition
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:
pip install pandas numpy yfinance matplotlib
Optional for advanced features:
pip install ta-lib scipy scikit-learn
Instructions
Step 1: Fetch Historical Data
python {baseDir}/scripts/fetch_data.py --symbol BTC-USD --period 2y --interval 1d
Data is cached to {baseDir}/data/{symbol}_{interval}.csv for reuse.
Step 2: Run Backtest
Basic backtest with default parameters:
python {baseDir}/scripts/backtest.py --strategy sma_crossover --symbol BTC-USD --period 1y
Advanced backtest with custom parameters:
# Example: backtest with specific date range
python {baseDir}/scripts/backtest.py \
--strategy rsi_reversal \
--symbol ETH-USD \
--period 1y \
--capital 10000 \
--params '{"period": 14, "overbought": 70, "oversold": 30}'
Step 3: Analyze Results
Results are saved to {baseDir}/reports/ incl