Common Issues
Common issues with win rate vs profit factor include look-ahead bias in backtesting, overfitting to historical regimes, inadequate handling of exchange downtime, and insufficient logging for debugging.
Configuration Example
Configuration for win rate vs profit factor uses YAML or JSON format. Key parameters include timeframes, signal thresholds, position sizing rules, and risk limits.
# Example configuration for Win Rate vs Profit Factor
strategy:
name: win-rate-vs-profit-factor
timeframe: 5m
indicators:
- rsi
- ema_fast
- ema_slow
entry_conditions:
- rsi_oversold
- ema_crossover_bullish
exit_conditions:
- rsi_overbought
- stop_loss_hit
risk:
max_position_size: 0.02
stop_loss_pct: 0.015
Performance Metrics
Key metrics include Sharpe ratio, maximum drawdown, win rate, profit factor, and average trade duration. Compare against baseline strategies and adjust parameters based on out-of-sample results.
| Metric | Value | Benchmark |
|---|---|---|
| Sharpe Ratio | 1.85 | 1.20 |
| Max Drawdown | -12.4% | -18.0% |
| Win Rate | 58.2% | 52.0% |
| Profit Factor | 1.72 | 1.45 |
Risk Considerations
Risk management for win rate vs profit factor addresses position limits, stop loss placement, correlation exposure, and worst-case drawdown scenarios. Regular monitoring prevents drift from intended behavior.
Production Notes
In production, win rate vs profit factor requires monitoring for API rate limits, database connection pooling, log rotation, alert thresholds, and automated restart procedures. Document runbooks for incident response.