Strategy Comparison Metrics

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Configuration Example

Configuration for strategy comparison metrics uses YAML or JSON format. Key parameters include timeframes, signal thresholds, position sizing rules, and risk limits.

# Example configuration for Strategy Comparison Metrics
strategy:
  name: strategy-comparison-metrics
  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

Common Issues

Common issues with strategy comparison metrics include look-ahead bias in backtesting, overfitting to historical regimes, inadequate handling of exchange downtime, and insufficient logging for debugging.

Important: Always validate strategy comparison metrics logic on out-of-sample data before live deployment. Paper trading reveals execution issues that backtests miss.

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.

MetricValueBenchmark
Sharpe Ratio1.851.20
Max Drawdown-12.4%-18.0%
Win Rate58.2%52.0%
Profit Factor1.721.45

Risk Considerations

Risk management for strategy comparison metrics addresses position limits, stop loss placement, correlation exposure, and worst-case drawdown scenarios. Regular monitoring prevents drift from intended behavior.

Important: Always validate strategy comparison metrics logic on out-of-sample data before live deployment. Paper trading reveals execution issues that backtests miss.

Testing Approach

Testing strategy comparison metrics includes unit tests for signal logic, integration tests for exchange interaction, and backtests against historical data with realistic slippage modeling.