name	backtesting-trading-strategies
description	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	Read, Write, Edit, Grep, Glob, Bash(python:*)
version	2.0.0
author	Jeremy Longshore <[email protected]>
license	MIT

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/ 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:

python {baseDir}/scripts/optimize.py \
  --strategy sma_crossover \
  --symbol BTC-USD \
  --period 1y \
  --param-grid '{"fast_period": [10, 20, 30], "slow_period": [50, 100, 200]}'

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	`fast_period`, `slow_period`
`ema_crossover`	Exponential MA crossover	`fast_period`, `slow_period`
`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 {baseDir}/config/settings.yaml:

data:
  provider: yfinance
  cache_dir: ./data

backtest:
  default_capital: 10000
  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 {baseDir}/references/errors.md for common issues and solutions.

Examples

See {baseDir}/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

来源：jeremylongshore/claude-code-plugins-plus-skills · MIT 许可 · 许可原文 · MD 为来源页保存的历史版本