When certain performance metrics are "too high" in a bad way, it could indicate potential issues with the strategy that may not be sustainable in live trading. Here’s what it means and what to watch out for:
1. Overfitting
- Definition: Overfitting occurs when a strategy is too closely tailored to historical data, capturing noise and specific market idiosyncrasies that are unlikely to repeat in the future.
- Symptoms:
- Extremely high Profit Factor (e.g., significantly above 2.5–3) or Sharpe Ratio (e.g., above 3–4).
- In-sample (IS) performance is excellent, but out-of-sample (OOS) or live trading results are inconsistent or poor.
- Risk: Overfitted strategies may perform exceptionally well during backtests but fail when exposed to new market data or different conditions.
- Solution: Verify with OOS testing and Walk-Forward Analysis. Include Monte Carlo simulations to ensure the strategy can handle variations.
2. Unrealistic Drawdown or Return Ratios
- Definition: When metrics like CAGR/Max DD% or Ret/DD ratio are exceptionally high, they might reflect unrealistic expectations.
- Symptoms:
- A CAGR/Max DD% ratio much greater than 2 or a Ret/DD ratio significantly higher than 10 can be warning signs.
- Risk: Such ratios may indicate that the strategy is leveraging risk in ways that won’t hold up under normal market conditions. It could signal a dependency on rare, high-profit events that are unlikely to occur consistently.
- Solution: Reassess risk parameters and ensure that the strategy’s profitability isn’t driven by a few outlier trades.
3. Abnormally High Profit Factor
- Definition: A profit factor that is too high (e.g., above 3 or 4) may signal that the strategy is excessively optimized for historical price patterns.
- Symptoms:
- The strategy might have extremely low drawdown and very high winning trades, which may not be sustainable.
- Risk: This could mean that the strategy depends on perfect market conditions or specific historical events that may not happen again.
- Solution: Test the strategy on different market conditions and instruments to ensure it isn’t tied to unique historical data quirks.
4. Excessive Win Rate
- Definition: An abnormally high win rate (e.g., 90% or more) could suggest that the strategy has a small average win compared to its average loss or that it takes excessive risks to avoid losses.
- Symptoms:
- The win-to-loss ratio may be high, but the profit factor and Sharpe ratio may not align accordingly.
- Risk: A high win rate strategy may suffer from large drawdowns or losses in the rare events when it does lose, affecting long-term sustainability.
- Solution: Evaluate the risk-reward ratio of the strategy and ensure that it has a reasonable balance between average win size and average loss.
5. Low Drawdown with High Returns
- Definition: While low drawdown with high returns is desirable, it can be a red flag if the drawdown is suspiciously low (e.g., <5%) while returns are exceptionally high.
- Symptoms:
- The strategy may appear almost too perfect, indicating that it could be optimized for very specific historical conditions.
- Risk: This is often a sign of over-optimization where the strategy's parameters have been excessively tailored to past data.
- Solution: Introduce randomness using Monte Carlo analysis and run extensive OOS testing to ensure the strategy can adapt to different market conditions.
How to Address “Too High” Metrics:
- Use Cross-Market Testing: Run the strategy on different instruments to ensure it performs well outside its original dataset.
- Increase OOS Testing Periods: Lengthen OOS testing to ensure the strategy’s edge holds over diverse conditions.
- Reduce Parameter Sensitivity: Simplify the strategy’s rules or increase the parameter ranges to make the strategy less dependent on specific conditions.
- Monte Carlo Simulations: Perform these to simulate potential market conditions and check for performance consistency under random variables.
- Walk-Forward Matrix: Use a Walk-Forward Matrix to test the strategy's adaptability over multiple forward periods.
Final Thought:
High performance metrics are not inherently bad, but when they reach extreme values, they could signal underlying weaknesses like overfitting or unrealistic optimization. To ensure the strategy is robust, it’s important to test thoroughly, maintain simplicity, and validate with realistic, forward-looking data.