dvcrn 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