backtesting-trading-strategies

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

Optional for advanced features:

set -euo pipefail
pip install ta-lib scipy scikit-learn

Instructions

Fetch historical data (cached to ${CLAUDE_SKILL_DIR}/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 ${CLAUDE_SKILL_DIR}/reports/ -- includes *_summary.txt (performance metrics), *_trades.csv (trade log), *_equity.csv (equity curve data), and *_chart.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
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 ${CLAUDE_SKILL_DIR}/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 ${CLAUDE_SKILL_DIR}/references/errors.md for common issues and solutions.

Examples

See ${CLAUDE_SKILL_DIR}/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