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