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