backtesting-trading-strategies
Backtest crypto and traditional trading strategies against historical data. Calculates performance metrics (Sharpe, Sortino, max drawdown), generates equity curves, and optimizes strategy parameters. Use when user wants to test a trading strategy, validate signals, or compare approaches. Trigger with phrases like "backtest strategy", "test trading strategy", "historical performance", "simulate trades", "optimize parameters", or "validate signals".
Allowed Tools
Provided by Plugin
trading-strategy-backtester
Backtest trading strategies with historical data, performance metrics, and risk analysis
Installation
This skill is included in the trading-strategy-backtester plugin:
/plugin install trading-strategy-backtester@claude-code-plugins-plusClick to copy
Instructions
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
Optional for advanced features:
set -euo pipefail
pip install ta-lib scipy scikit-learn
Instructions
- Fetch historical data (cached to
${CLAUDESKILLDIR}/data/for reuse):
python ${CLAUDE_SKILL_DIR}/scripts/fetch_data.py --symbol BTC-USD --period 2y --interval 1d
- Run a backtest with default or custom parameters:
python ${CLAUDE_SKILL_DIR}/scripts/backtest.py --strategy sma_crossover --symbol BTC-USD --period 1y
python ${CLAUDE_SKILL_DIR}/scripts/backtest.py \
--strategy rsi_reversal \
--symbol ETH-USD \
--period 1y \
--capital 10000 \ # 10000: 10 seconds in ms
--params '{"period": 14, "overbought": 70, "oversold": 30}'
- Analyze results saved to
${CLAUDESKILLDIR}/reports/-- includessummary.txt(performance metrics),trades.csv(trade log),equity.csv(equity curve data), andchart.png(visual equity curve). - Optimize parameters via grid search to find the best combination:
python ${CLAUDE_SKILL_DIR}/scripts/optimize.py \
--strategy sma_crossover \
--symbol BTC-USD \
--period 1y \
--param-grid '{"fast_period": [10, 20, 30], "slow_period": [50, 100, 200]}' # HTTP 200 OK
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
================================================================================
BACKTEST RESULTS: SMA CROSSOVER
BTC-USD | [start_date] to [end_date]
================================================================================
PERFORMANCE | RISK
Total Return: +47.32% | Max Drawdown: -18.45%
CAGR: +47.32% | VaR (95%): -2.34%
Sharpe Ratio: 1.87 | Volatility: 42.1%
Sortino Ratio: 2.41 | Ulcer Index: 8.2
--------------------------------------------------------------------------------
TRADE STATISTICS
Total Trades: 24 | Profit Factor: 2.34
Win Rate: 58.3% | Expectancy: $197.17
Avg Win: $892.45 | Max Consec. Losses: 3
================================================================================
Supported Strategies
| Strategy | Description | Key Parameters |
|---|---|---|
sma_crossover |
Simple moving average crossover | fastperiod, slowperiod |
ema_crossover |
Exponential MA crossover | fastperiod, slowperiod |
rsi_reversal |
RSI overbought/oversold | period, overbought, oversold |
macd |
MACD signal line crossover | fast, slow, signal |
bollinger_bands |
Mean reversion on bands | period, std_dev |
breakout |
Price breakout from range | lookback, threshold |
mean_reversion |
Return to moving average | period, z_threshold |
momentum |
Rate of change momentum | period, threshold |
Configuration
Create ${CLAUDESKILLDIR}/config/settings.yaml:
data:
provider: yfinance
cache_dir: ./data
backtest:
default_capital: 10000 # 10000: 10 seconds in ms
commission: 0.001 # 0.1% per trade
slippage: 0.0005 # 0.05% slippage
risk:
max_position_size: 0.95
stop_loss: null # Optional fixed stop loss
take_profit: null # Optional fixed take profit
Error Handling
See ${CLAUDESKILLDIR}/references/errors.md for common issues and solutions.
Examples
See ${CLAUDESKILLDIR}/references/examples.md for detailed usage examples including:
- Multi-asset comparison
- Walk-forward analysis
- Parameter optimization workflows
Files
| File | Purpose |
|---|---|
scripts/backtest.py |
Main backtesting engine |
scripts/fetch_data.py |
Historical data fetcher |
scripts/strategies.py |
Strategy definitions |
scripts/metrics.py |
Performance calculations |
scripts/optimize.py |
Parameter optimization |
Resources
- yfinance - Yahoo Finance data
- TA-Lib - Technical analysis library
- QuantStats - Portfolio analytics