kfox
7bd6be64a4
Some checks are pending
Documentation / build-docs (push) Waiting to run
Tests / test (macos-latest, 3.11) (push) Waiting to run
Tests / test (macos-latest, 3.12) (push) Waiting to run
Tests / test (macos-latest, 3.13) (push) Waiting to run
Tests / test (macos-latest, 3.14) (push) Waiting to run
Tests / test (ubuntu-latest, 3.11) (push) Waiting to run
Tests / test (ubuntu-latest, 3.12) (push) Waiting to run
Tests / test (ubuntu-latest, 3.13) (push) Waiting to run
Tests / test (ubuntu-latest, 3.14) (push) Waiting to run
261 lines
8.3 KiB
Markdown
261 lines
8.3 KiB
Markdown
# Backtesting Guide
|
|
|
|
This guide explains how to use the backtesting feature to evaluate trading strategies on historical data.
|
|
|
|
## Running a Backtest
|
|
|
|
1. Navigate to the **Backtesting** page
|
|
2. Configure your backtest in the form:
|
|
- **Strategy**: Select a strategy from the dropdown (required)
|
|
- **Symbol**: Trading pair to test (e.g., BTC/USD)
|
|
- **Exchange**: Data source exchange (e.g., coinbase)
|
|
- **Timeframe**: Data timeframe (1m, 5m, 15m, 1h, 4h, 1d) - 1h recommended for most strategies
|
|
- **Start Date**: Beginning of test period (required)
|
|
- **End Date**: End of test period (required)
|
|
- **Initial Capital**: Starting capital in USD (default: $100)
|
|
- **Slippage (%)**: Expected slippage percentage (default: 0.1%)
|
|
- **Fee Rate (%)**: Trading fee percentage (default: 0.1%)
|
|
3. Click **Run Backtest**
|
|
4. A progress overlay will appear showing the backtest is running
|
|
5. An operations panel will show the running backtest with status
|
|
6. Wait for completion (you'll receive a success notification)
|
|
7. Review results in the **Backtest Results** section below
|
|
|
|
## Understanding Results
|
|
|
|
The backtest results include:
|
|
|
|
- **Total Return**: Overall percentage return
|
|
- **Sharpe Ratio**: Risk-adjusted return metric (higher is better)
|
|
- **Sortino Ratio**: Downside risk-adjusted return (higher is better)
|
|
- **Max Drawdown**: Largest peak-to-trough decline
|
|
- **Win Rate**: Percentage of profitable trades
|
|
- **Total Trades**: Number of trades executed
|
|
- **Final Value**: Portfolio value at end of backtest
|
|
|
|
## Exporting Results
|
|
|
|
After a backtest completes, you can export the results:
|
|
|
|
1. In the backtest results section, find the export buttons
|
|
2. **Export CSV**:
|
|
- Click **Export CSV** button
|
|
- Downloads a CSV file with all trades from the backtest
|
|
- File includes: timestamp, side, price, quantity, value
|
|
3. **Export PDF**:
|
|
- Click **Export PDF** button
|
|
- Generates a comprehensive PDF report
|
|
- Includes charts, metrics, and trade analysis
|
|
|
|
Both exports are automatically named with the current date for easy organization.
|
|
|
|
## Advanced Backtesting Features
|
|
|
|
### Walk-Forward Analysis
|
|
|
|
Walk-forward analysis provides robust parameter optimization by using rolling windows:
|
|
|
|
1. **Training Period**: Strategy parameters are optimized on training data (e.g., 90 days)
|
|
2. **Testing Period**: Optimized parameters are tested on out-of-sample data (e.g., 30 days)
|
|
3. **Rolling Window**: Window advances by step size (e.g., 30 days) and process repeats
|
|
|
|
This method prevents overfitting and provides more realistic performance estimates than single-period optimization.
|
|
|
|
**Benefits**:
|
|
- Prevents overfitting to historical data
|
|
- Tests strategy robustness across different market conditions
|
|
- Provides confidence intervals for parameter estimates
|
|
- Validates strategy performance on unseen data
|
|
|
|
### Monte Carlo Simulation
|
|
|
|
Monte Carlo simulation tests strategy robustness by running thousands of random scenarios:
|
|
|
|
- **Random Parameter Variation**: Tests strategy performance across parameter ranges
|
|
- **Statistical Analysis**: Provides distribution of returns, Sharpe ratios, and drawdowns
|
|
- **Confidence Intervals**: Shows expected performance ranges (e.g., 95% confidence)
|
|
- **Risk Assessment**: Identifies worst-case scenarios and tail risks
|
|
|
|
Use Monte Carlo simulation to:
|
|
- Validate strategy robustness
|
|
- Assess parameter sensitivity
|
|
- Understand potential downside risks
|
|
- Estimate performance under various market conditions
|
|
|
|
## Parameter Optimization
|
|
|
|
Parameter optimization allows you to automatically find the best strategy parameters using multiple algorithms:
|
|
|
|
- **Grid Search**: Exhaustive search across parameter grid (best for small parameter spaces)
|
|
- **Bayesian Optimization**: Efficient exploration using Gaussian process (best for expensive evaluations)
|
|
- **Genetic Algorithms**: Evolutionary search that finds good solutions efficiently
|
|
|
|
Optimization metrics include:
|
|
- Sharpe Ratio (risk-adjusted returns)
|
|
- Total Return
|
|
- Maximum Drawdown
|
|
- Win Rate
|
|
|
|
When available via the backend API, you'll be able to:
|
|
- Select parameters to optimize
|
|
- Set parameter ranges
|
|
- Choose optimization method
|
|
- View optimization progress
|
|
- Compare optimization results
|
|
|
|
## Interpreting Metrics
|
|
|
|
- **Sharpe Ratio > 1**: Good risk-adjusted returns
|
|
- **Max Drawdown < 20%**: Acceptable risk level
|
|
- **Win Rate > 50%**: More winning than losing trades
|
|
|
|
# Backtesting Guide
|
|
|
|
Learn how to test your trading strategies on historical data.
|
|
|
|
## What is Backtesting?
|
|
|
|
Backtesting is the process of testing a trading strategy on historical data to evaluate its performance before risking real money.
|
|
|
|
## Running a Backtest
|
|
|
|
1. Navigate to Backtest view
|
|
2. Select a strategy
|
|
3. Configure backtest parameters:
|
|
- **Start Date**: Beginning of test period
|
|
- **End Date**: End of test period
|
|
- **Initial Capital**: Starting capital
|
|
- **Symbol**: Trading pair to test
|
|
- **Timeframe**: Data timeframe
|
|
4. Click "Run Backtest"
|
|
5. Wait for completion
|
|
6. Review results
|
|
|
|
## Backtest Parameters
|
|
|
|
### Time Period
|
|
|
|
- **Start Date**: When to begin the backtest
|
|
- **End Date**: When to end the backtest
|
|
- **Duration**: Length of test period
|
|
- Longer periods provide more reliable results
|
|
|
|
### Capital Settings
|
|
|
|
- **Initial Capital**: Starting amount (e.g., $10,000)
|
|
- **Currency**: Base currency (USD, EUR, etc.)
|
|
|
|
### Market Settings
|
|
|
|
- **Symbol**: Trading pair (BTC/USD, ETH/USD, etc.)
|
|
- **Timeframe**: Data granularity (1m, 5m, 1h, 1d)
|
|
- **Exchange**: Historical data source
|
|
|
|
## Understanding Results
|
|
|
|
### Performance Metrics
|
|
|
|
- **Total Return**: Overall profit/loss percentage
|
|
- **Final Capital**: Ending portfolio value
|
|
- **Sharpe Ratio**: Risk-adjusted return measure
|
|
- **Sortino Ratio**: Downside risk-adjusted return
|
|
- **Max Drawdown**: Largest peak-to-trough decline
|
|
- **Win Rate**: Percentage of profitable trades
|
|
|
|
### Trade Analysis
|
|
|
|
- **Total Trades**: Number of trades executed
|
|
- **Winning Trades**: Number of profitable trades
|
|
- **Losing Trades**: Number of unprofitable trades
|
|
- **Average Win**: Average profit per winning trade
|
|
- **Average Loss**: Average loss per losing trade
|
|
- **Profit Factor**: Ratio of gross profit to gross loss
|
|
|
|
### Charts
|
|
|
|
- **Equity Curve**: Portfolio value over time
|
|
- **Drawdown Chart**: Drawdown periods
|
|
- **Trade Distribution**: Win/loss distribution
|
|
- **Monthly Returns**: Performance by month
|
|
|
|
## Realistic Backtesting
|
|
|
|
Crypto Trader includes realistic backtesting features:
|
|
|
|
### Slippage
|
|
|
|
Slippage simulates the difference between expected and actual execution prices.
|
|
|
|
- **Default**: 0.1% for market orders
|
|
- **Configurable**: Adjust based on market conditions
|
|
- **Market Impact**: Larger orders have more slippage
|
|
|
|
### Fees
|
|
|
|
Trading fees are automatically included:
|
|
|
|
- **Maker Fees**: For limit orders (typically 0.1%)
|
|
- **Taker Fees**: For market orders (typically 0.2%)
|
|
- **Exchange-Specific**: Fees vary by exchange
|
|
|
|
### Order Execution
|
|
|
|
- **Market Orders**: Execute at current price + slippage
|
|
- **Limit Orders**: Execute only if price reaches limit
|
|
- **Partial Fills**: Large orders may fill partially
|
|
|
|
## Parameter Optimization
|
|
|
|
Optimize strategy parameters for better performance:
|
|
|
|
1. Select strategy
|
|
2. Choose parameters to optimize
|
|
3. Set parameter ranges
|
|
4. Select optimization method:
|
|
- **Grid Search**: Test all combinations
|
|
- **Genetic Algorithm**: Evolutionary optimization
|
|
- **Bayesian Optimization**: Efficient parameter search
|
|
5. Run optimization
|
|
6. Review results and select best parameters
|
|
|
|
## Best Practices
|
|
|
|
1. **Use Sufficient Data**: Test on at least 6-12 months of data
|
|
2. **Avoid Overfitting**: Don't optimize too aggressively
|
|
3. **Test Multiple Periods**: Verify performance across different market conditions
|
|
4. **Consider Fees**: Always include realistic fees
|
|
5. **Check Slippage**: Account for execution costs
|
|
6. **Validate Results**: Compare with paper trading
|
|
|
|
## Limitations
|
|
|
|
Backtesting has limitations:
|
|
|
|
- **Past Performance**: Doesn't guarantee future results
|
|
- **Market Conditions**: Markets change over time
|
|
- **Data Quality**: Results depend on data accuracy
|
|
- **Execution**: Real trading may differ from simulation
|
|
|
|
## Exporting Results
|
|
|
|
Export backtest results for analysis:
|
|
|
|
1. Click "Export Results"
|
|
2. Choose format:
|
|
- **CSV**: For spreadsheet analysis
|
|
- **PDF**: For reports
|
|
3. Save file
|
|
|
|
## Troubleshooting
|
|
|
|
**No results?**
|
|
- Check date range has data
|
|
- Verify symbol is correct
|
|
- Check strategy parameters
|
|
|
|
**Unrealistic results?**
|
|
- Verify fees are enabled
|
|
- Check slippage settings
|
|
- Review data quality
|
|
|