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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:

1. Use Cross-Market Testing: Run the strategy on different instruments to ensure it performs well outside its original dataset.
2. Increase OOS Testing Periods: Lengthen OOS testing to ensure the strategy’s edge holds over diverse conditions.
3. Reduce Parameter Sensitivity: Simplify the strategy’s rules or increase the parameter ranges to make the strategy less dependent on specific conditions.
4. Monte Carlo Simulations: Perform these to simulate potential market conditions and check for performance consistency under random variables.
5. 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.

If your results exceed the values I recommended, that’s an excellent indication that you have exceptionally robust and high-performing strategies. Here’s what you should do next to make the most of these outstanding strategies:

1. Prioritize Top-Performing Strategies:

• Select the Best Strategies: Prioritize strategies that exceed all or most of the values stated. For example, if a strategy has a Ret/DD ratio significantly above 4, a profit factor above 2, and a Sharpe ratio above 2, it’s a top candidate for inclusion in your portfolio.
• Diversify Within Top Performers: Ensure that your high-performing strategies aren’t too correlated with each other. A diversified set of high-quality strategies will help mitigate risk.

2. Create High-Performance Portfolios:

• Balance Strategy Types: Build a portfolio that combines different types of strategies (e.g., trend-following, mean reversion, breakout) to create a more resilient and balanced approach.
• Allocate Capital Strategically: Allocate more capital to strategies that have proven consistent and robust over all testing phases, but keep an eye on overall drawdown to avoid overexposure.
• Test Portfolio Stability: Run portfolio-level backtests and simulations to check for overall performance metrics like cumulative profit factor, Sharpe ratio, and maximum drawdown.

3. Stress Test and Validation:

• Monte Carlo Stress Testing: Perform Monte Carlo simulations to see how the portfolio performs under various simulated market conditions. This will help confirm that the strategies maintain high performance even with slight data variations or unexpected market behavior.
• Out-of-Sample (OOS) and Forward Testing: Ensure the strategies that exceed your stated metrics continue to perform well in OOS data and in live or demo forward testing.

4. Evaluate Long-Term Performance:

• Assess Stability and Adaptability: Check if the top-performing strategies maintain their edge over longer timeframes and during different market conditions (e.g., trending vs. ranging markets).
• Monitor Key Metrics: Continuously track the profit factor, Ret/DD ratio, Sharpe ratio, drawdown percentages, and CAGR/Max DD% to ensure these metrics remain favorable over time.

5. Prepare for Live Trading:

• Simulate Live Trading: Before deploying your portfolio for real capital, simulate live trading with historical data as a final check.
• Set Risk Management Rules: Establish stop-loss, take-profit, and position sizing rules that align with your high-performing strategy metrics.

6. Performance Monitoring:

• Regular Performance Review: Once live trading starts, monitor your strategies closely to see if they continue to exceed expectations. Adjust your portfolio as needed based on performance changes.
• Rebalance Portfolio: Periodically review and rebalance your portfolio to ensure it continues to contain only top-performing strategies that exceed your target metrics.

Benefits of Strategies That Exceed Your Metrics:

• High Reliability: Strategies with metrics that exceed your thresholds indicate a strong and reliable edge in the market.
• Risk Mitigation: Exceeding drawdown and stability requirements means these strategies are safer and less likely to encounter major losses.
• Scalability: Such strategies may allow for greater position sizing or capital allocation due to their high return-to-risk ratios.

Final Thoughts:

If your results are consistently exceeding these values, you are in a very strong position for creating an elite portfolio of trading strategies. Ensure that you maintain rigorous validation and risk management practices as you move forward to capitalize on these high-performing strategies.

Profit factor (IS) > 1.4

Ret/DD ratio (full) > 4

Stability (is) > 0.6

open dd % (is) < 50%

cagr/max dd % (is) > 0.6

sharp ratio (is) > 1.5

max dd % (OOs) < 70%

To maximize the potential of your strategies using these filters, you should aim for the highest achievable values within these constraints to ensure robust and well-performing strategies. Here’s a refined approach to push for optimal values in each metric:

Optimizing the Filters for Portfolio Selection:

1. Profit Factor (IS) > 1.4:
   • Goal: Aim for a profit factor of at least 1.5 or higher. The higher the profit factor, the more profitable the strategy is relative to its losses. Strive for strategies that exceed 1.5 and ideally reach 2.0 or above for optimal performance.
2. Ret/DD Ratio (Full) > 4:
   • Goal: Maximize this ratio as much as possible. The higher the Ret/DD ratio, the better the strategy’s return relative to its drawdown. Look for strategies with values significantly above 4, aiming for 5-6 or higher if possible.
3. Stability (IS) > 0.6:
   • Goal: Seek strategies with stability above 0.8. Higher stability indicates a smoother equity curve and less variability in performance.
4. Open DD % (IS) < 50%:
   • Goal: While < 50% is acceptable, aim for open drawdown values below 30% to maintain better risk control. The lower, the better, as it indicates less capital is exposed during active trades.
5. CAGR/Max DD % (IS) > 0.6:
   • Goal: Aim for this ratio to be above 1.0. This ensures that the strategy’s annualized return is greater than its maximum drawdown, indicating good performance relative to risk.
6. Sharpe Ratio (IS) > 1.5:
   • Goal: Look for strategies with a Sharpe ratio above 2.0, as this indicates strong risk-adjusted returns. A Sharpe ratio above 2.0 suggests consistent and significant returns compared to the strategy’s risk.
7. Max DD % (OOS) < 70%:
   • Goal: Ideally, target strategies with OOS max drawdown below 40-50% for more secure, sustainable performance. This ensures the strategy remains resilient even under unseen market conditions.

Tips to Maximize Each Metric:

• Optimization and Fine-Tuning: Conduct sequential optimizations and Monte Carlo simulations to find parameter combinations that yield the highest metrics within your constraints.
• Select Strategies with High-Ranking Metrics: Rank your strategies by each filter’s value and prioritize those that excel in multiple categories.
• Diversify by Strategy Type: Include various strategy types that perform well across different market conditions to enhance overall portfolio performance.

Suggested Ranking and Prioritization:

1. Ret/DD Ratio – As a primary filter, focus on maximizing this to ensure high returns relative to drawdowns.
2. Profit Factor and Sharpe Ratio – These metrics ensure profitability and risk-adjusted performance.
3. Stability and Drawdown Metrics – Balance stability and drawdown values to select strategies that are consistent and safe.
4. CAGR/Max DD % – This ratio should be used as a secondary filter to confirm that the selected strategies provide a good return for the risk taken.

By aiming for values significantly higher than your minimum thresholds, you can create a portfolio that is both high-performing and robust, capable of adapting to different market conditions while maintaining acceptable risk levels.