DNYL . SPACE

So give me re-arrange 5 pairs like this without duplicating filters for maximum efficiency.

Sure! Here are five pairs of filters arranged for maximum efficiency, ensuring no duplicates across the selections:

Filter Pairs

1. Pair 1:
   • First Filter (IS): Sharpe Ratio
   • Second Filter (OOS): Net Profit
2. Pair 2:
   • First Filter (IS): CAGR/Max DD%
   • Second Filter (OOS): Profit Factor
3. Pair 3:
   • First Filter (IS): Stability
   • Second Filter (OOS): Consecutive Win/Loss
4. Pair 4:
   • First Filter (IS): Ret/DD Ratio
   • Second Filter (OOS): Open DD%
5. Pair 5:
   • First Filter (IS): Max DD%
   • Second Filter (OOS): R-Squared

Summary

• Each pair consists of one metric from the Importance Score (IS) category and one from the Out-of-Sample (OOS) category, ensuring no duplicates. This setup will help you efficiently narrow down your EA selection while covering various critical performance aspects.

Here are the recommended filter settings parameters for each pair to help you refine your EA selection process effectively:

Filter Settings Parameters

1. Pair 1:
   • IS: Sharpe Ratio
     • Setting: Minimum Sharpe Ratio (e.g., > 1.5)
   • OOS: Net Profit
     • Setting: Minimum Net Profit (e.g., > $1,000 or a percentage of capital)
2. Pair 2:
   • IS: CAGR/Max DD%
     • Setting: Minimum CAGR/Max DD% ratio (e.g., > 1.5)
   • OOS: Profit Factor
     • Setting: Minimum Profit Factor (e.g., > 1.5)
3. Pair 3:
   • IS: Stability
     • Setting: Minimum Stability (e.g., > 0.8)
   • OOS: Consecutive Win/Loss
     • Setting: Maximum Consecutive Losses (e.g., < 3)
4. Pair 4:
   • IS: Ret/DD Ratio
     • Setting: Minimum Ret/DD Ratio (e.g., > 1.5)
   • OOS: Open DD%
     • Setting: Maximum Open Drawdown Percentage (e.g., < 15%)
5. Pair 5:
   • IS: Max DD%
     • Setting: Maximum Drawdown (e.g., < 20%)
   • OOS: R-Squared
     • Setting: Minimum R-Squared (e.g., > 0.6)

Summary of Settings

• Each pair is designed with specific thresholds to ensure you select EAs that meet your performance criteria. You can adjust the numerical values based on your risk tolerance and trading goals. These settings will help you focus on strategies that are likely to perform well while managing risk effectively.

To ensure your trading strategy aligns with FTMO guidelines and efficiently trades XAUUSD while managing risk effectively, here’s how to set up, rank, and parameterize your strategies in StrategyQuant X (SQX):

Setup/Parameters in SQX:

1. Trade Sizing Setup:
   • Money Management Rules: Configure the strategy to use fixed fractional position sizing, targeting 1%-2% of account risk per trade.
   • Max Daily Risk Cap: Implement a daily risk cap in the money management module to prevent exceeding 3%-5% total risk exposure.
   • Volatility-Based Adjustment: Use ATR-based position sizing to dynamically adjust the lot size according to market conditions.
2. Stop Loss (SL) and Take Profit (TP):
   • SL Parameters: Set stop loss at 1.5-2x the ATR value to adapt to XAUUSD’s volatility.
   • TP Parameters: Configure TP at 2-3x ATR or use a predefined RRR (e.g., 1:2 or 1:3) in the setup.
   • Trailing Stop: Enable trailing stops based on ATR or a percentage of the move to capture gains while protecting profits.
3. Break-Even (BE) Settings:
   • BE Activation: Implement a rule that moves the SL to breakeven when the price moves in your favor by 0.5%-1%.
   • Dynamic BE: Use ATR-based calculations for adjusting the break-even point based on market volatility.
4. Entry Criteria:
   • Indicators: Configure indicators like RSI, CCI, or Moving Averages for signal generation.
   • Confirmation Filters: Include filters like ATR to ensure the market is in a trending state before taking trades.
   • Multi-Timeframe Analysis: Utilize an H4 timeframe for trend confirmation and an H1 for trade entries.
5. Exit Strategies:
   • Time-Based Exit: Set a maximum trade holding period (e.g., up to 5 days).
   • Signal-Based Exit: Add indicators like Super Trend or Parabolic SAR to trigger exit conditions.
   • End-of-Day Exit: Configure exits that close positions before significant economic releases or weekends.

Ranking Criteria in SQX:

To choose the most robust strategies for XAUUSD trading that align with FTMO rules, apply the following ranking criteria:

1. Performance Metrics:
   • CAGR/Max Drawdown: Set a threshold where CAGR/Max Drawdown > 1 for risk-adjusted performance.
   • Sharpe Ratio: Ensure a Sharpe Ratio > 2 for good risk-adjusted returns.
   • Profit Factor: Select strategies with a Profit Factor > 1.5 to ensure more winning trades compared to losing ones.
   • Stability: Use a stability metric of > 0.8 to identify strategies with consistent performance.
   • Open Drawdown: Filter out strategies with Open DD% exceeding 15%.
2. Risk Management Parameters:
   • Max Daily Loss Check: Filter out strategies that, during backtesting, exceed a 5% daily loss.
   • Max Drawdown Filter: Discard strategies with a maximum drawdown over 10% in backtests.
3. Consistency Metrics:
   • Consecutive Winning/Losing Trades: Rank strategies with greater than 2 consecutive winning trades and manage strategies that handle consecutive losses effectively.
   • Win Rate and Expectancy: Ensure that the strategy has a win rate that supports profitability, even if lower, as long as the RRR is high.
4. Adaptive Parameters:
   • Volatility Adaptation: Include strategies that adjust their entries, exits, or stop levels based on ATR changes to handle varying market conditions.
   • Market Regime Testing: Use SQX to test strategies in different market regimes (e.g., trending vs. ranging) to ensure adaptability.

By structuring and ranking strategies this way in SQX, you create a robust, compliant approach for trading XAUUSD under FTMO rules.

Yes, your approach is valid and makes sense. Here’s why using full data for ranking in Portfolio Master while focusing on good OOS performance during strategy selection is a balanced approach:

Why Rank with Full Data:

• Comprehensive Performance Overview: Using full data (both IS and OOS combined) for ranking gives you an overall picture of how the strategy performs across all available data. This helps to ensure that the strategy isn’t just optimized for a specific period.
• Balanced Metrics: Full data ranking helps capture metrics like profit factor, Sharpe ratio, and drawdown across the entire testing period, giving a more balanced evaluation of performance.
• Portfolio Cohesion: Using full data provides a consistent basis for ranking strategies, which helps build a well-rounded portfolio.

Focusing on Good OOS Performance:

• Robustness Check: Emphasizing good OOS performance ensures that the strategy has not been overfitted to the IS data and can perform well in unseen market conditions. This is crucial for assessing how the strategy is likely to behave in live trading.
• Validation of Stability: OOS performance indicates the adaptability and stability of a strategy when exposed to new data. It acts as a test for the real-world viability of the strategy.
• Reducing Overfitting Risk: Strategies that excel only in IS data may not perform well in live markets. By focusing on OOS, you’re filtering out those that might be overfitted and selecting those that are more robust.

Recommended Approach:

1. Rank Using Full Data:
   • Rank strategies based on key metrics like Profit Factor, Sharpe Ratio, Ret/DD Ratio, etc., using the complete dataset.
   • This gives you a holistic view and helps identify strategies that perform well consistently across the entire backtest.
2. Select Strategies with Strong OOS Performance:
   • After ranking, review the OOS performance of the top strategies to ensure they meet your criteria for robustness.
   • Prioritize strategies that maintain good metrics in the OOS period, such as reasonable drawdown, high Sharpe ratio, and stability.
   • Ensure that OOS results are consistent with the performance indicated by the full data to avoid surprises during live trading.

Conclusion:

Your approach of ranking with full data for an overall view, while focusing on good OOS performance during the final selection, is an effective method. It strikes a balance between comprehensive evaluation and ensuring real-world applicability. This process helps build a portfolio that is both diversified and resilient, minimizing the risks associated with overfitting and improving the likelihood of successful live trading performance.

To classify the values for each metric you mentioned, I’ll provide typical ranges for minimum (min) and maximum (max) values, indicating what is considered acceptable, optimal, and potentially problematic. These values are key for balancing performance and risk in strategy evaluation:

1. CAGR/Max DD% (Balances Growth vs. Risk)

• Min Acceptable: >0.6
• Optimal Range: 1.0 – 2.0
• High Performance: >2.0 (Be cautious if significantly above 2.5, as this could signal over-optimization)

2. Profit Factor (Key Profitability Measure)

• Min Acceptable: >1.4
• Optimal Range: 1.5 – 2.0
• High Performance: 2.0 – 3.0 (Values >3 could indicate overfitting)

3. Ret/DD Ratio (Return Relative to Risk)

• Min Acceptable: >4
• Optimal Range: 5 – 8
• High Performance: >8 (Approaching 10 or higher may indicate potential over-optimization)

4. Open DD% (Drawdown on Open Trades)

• Max Acceptable: <50%
• Optimal Range: 15% – 30%
• High Performance: <15% (Too low, e.g., <5%, with high returns can signal overfitting)

5. Stability (Smoothness of Equity Curve)

• Min Acceptable: >0.6
• Optimal Range: 0.8 – 0.9
• High Performance: >0.9 (Indicates very steady equity; if excessively high, check for potential curve-fitting)

6. Win/Loss Ratio (Secondary to Profitability)

• Min Acceptable: ~1.0 (50% win rate)
• Optimal Range: 1.5 – 2.0 (60-67% win rate)
• High Performance: >2.0 (Win rates >80% should be checked for risk-to-reward imbalance)

7. Fitness (Initial Strategy Validation)

• Min Acceptable: Variable, depending on the model, but ideally >1 in early tests.
• Optimal Use: Best for early-stage testing, less critical in final validation.

8. Open Drawdown % (Open DD%)

• Max Acceptable: <50%
• Optimal Range: 15% – 30%
• High Performance: <15% (Keep an eye out for high returns with very low drawdown, as this could indicate overfitting)

9. Max Drawdown % (Total Drawdown)

• Max Acceptable: <70% (for OOS data)
• Optimal Range: 20% – 35%
• High Performance: <20% (If too low with high returns, validate for potential over-optimization)

Summary of Classification Ranges:

• Min Acceptable: The minimum threshold for considering a strategy viable.
• Optimal Range: The sweet spot for good performance without high risk of over-optimization.
• High Performance: Indicates exceptional results; caution should be taken to ensure that metrics aren’t excessively high, which could be a sign of overfitting or unrealistic expectations.

Using these ranges, you can classify your strategies and set up your filters in StrategyQuant X to ensure you’re choosing robust and viable strategies that balance profitability with risk management.

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.

Yes, you can definitely use the filters you listed for building your trading portfolio. Applying these filters ensures that only the most robust and reliable strategies make it into your portfolio, creating a diversified and resilient set of strategies for trading. Here’s how to use these filters effectively when constructing a portfolio:

Steps for Using Filters to Create a Portfolio:

1. Apply the Filters to Select Strategies:
   • Use your filters (e.g., Profit Factor > 1.4, Ret/DD ratio > 4, Sharpe Ratio > 1.5, etc.) to narrow down your list of strategies.
   • Apply these filters consistently across all testing stages (IS, OOS, and different data precisions) to ensure that the selected strategies are robust and well-suited for real trading.
2. Select a Diverse Set of Strategies:
   • Mix Strategy Types: Include different types of strategies such as trend-following, mean reversion, breakout, and range trading to diversify your portfolio.
   • Multiple Timeframes: Ensure that your portfolio contains strategies that trade on different timeframes (e.g., H1, M30, M15, M5) to spread risk across various market conditions.
   • Trading Approaches: Include strategies with different entry and exit techniques (e.g., indicators, price action, volatility-based) for a balanced portfolio.
3. Ensure Correlation Is Low:
   • Check for correlation between the selected strategies. Including highly correlated strategies may increase the overall risk as they might behave similarly during specific market conditions.
   • Aim for strategies that complement each other, so that if one performs poorly, others may compensate for it.
4. Backtest the Portfolio:
   • Backtest the combined portfolio using your existing filters to evaluate the overall performance metrics such as profit factor, drawdown, Sharpe ratio, and stability.
   • Use a weighted approach for strategy allocation to balance exposure within the portfolio.
5. Simulate Portfolio with Out-of-Sample (OOS) Data:
   • Run the portfolio through OOS testing and check the combined performance metrics.
   • Ensure that the portfolio maintains acceptable levels of maximum drawdown, stability, and profitability.
6. Optimize Strategy Allocation:
   • Adjust the capital allocation for each strategy to balance the portfolio risk. For example, allocate more capital to strategies with a higher Sharpe ratio or a better Ret/DD ratio.
   • Ensure the overall portfolio drawdown stays within a risk-tolerant level that aligns with your trading goals.

Benefits of Using These Filters for Portfolio Creation:

• Consistency: Applying these filters helps ensure that your portfolio strategies are consistently high-quality, reducing the risk of underperformance.
• Robustness: By selecting strategies that meet your strict criteria across all stages of testing, your portfolio will be more adaptable to various market conditions.
• Risk Management: Including maximum drawdown and stability filters keeps your portfolio within manageable risk levels.

Final Tips:

• Monitor Portfolio Performance: After constructing your portfolio, continue to monitor its performance periodically and replace or adjust strategies as needed based on live or forward-test results.
• Forward Test: Always forward test the portfolio in a demo or live environment with minimal risk to confirm that it behaves as expected.

Using your existing filters as a benchmark for selecting strategies ensures that you build a strong, diversified portfolio capable of sustaining various market conditions.

ah yes i added these filters already in all step;

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%

Great! Including those filters across all steps is essential for narrowing down to the most robust strategies. Here’s how to use these filters effectively in your final review and comparison process:

Review of Applied Filters:

1. Profit Factor (IS) > 1.4:
   • Ensures that in-sample (IS) strategies generate at least $1.40 in profit for every $1.00 of loss.
   • Review Tip: Verify that this is consistently met across all steps, especially in OOS (out-of-sample) testing.
2. Ret/DD Ratio (Full) > 4:
   • A high return-to-drawdown ratio indicates strong risk-adjusted returns.
   • Review Tip: Ensure this holds up in OOS testing to confirm that the strategy isn’t just optimized for IS data.
3. Stability (IS) > 0.6:
   • Indicates a steady equity curve without significant fluctuations.
   • Review Tip: Check if the stability in OOS testing remains above this threshold for long-term performance assurance.
4. Open DD % (IS) < 50%:
   • Ensures that the strategy’s open drawdown doesn’t exceed 50%, which helps manage risk during active trades.
   • Review Tip: Confirm that OOS open drawdown remains under acceptable levels to avoid capital depletion in live trading.
5. CAGR/Max DD % (IS) > 0.6:
   • A ratio showing that the compound annual growth rate (CAGR) is sufficiently high relative to the maximum drawdown.
   • Review Tip: Ensure the ratio holds in OOS testing to validate that the strategy’s returns are consistent with the risk taken.
6. Sharpe Ratio (IS) > 1.5:
   • Confirms that the returns are substantial relative to the risk, indicating a strategy that compensates well for its volatility.
   • Review Tip: Compare this ratio in OOS to ensure that risk-adjusted returns are sustainable.
7. Max DD % (OOS) < 70%:
   • Ensures that the maximum drawdown during OOS does not exceed 70%, which helps to manage potential large losses during unseen data.
   • Review Tip: Confirm that the maximum drawdown remains under control when exposed to new data sets to avoid potential large drawdowns in live trading.

Next Steps for Final Review:

1. Cross-Check Consistency:
   • Verify that the filters are met in both IS and OOS testing. Highlight strategies that meet all criteria consistently across different testing steps.
2. Compare Step Results:
   • For each strategy that passes your filters, compare key metrics across all three steps (Selected TF, 1-minute data tick, Real tick data) to ensure minimal deviations.
3. Portfolio Diversification:
   • Build a final portfolio using strategies that pass your filters across all steps. Ensure that the portfolio includes a mix of strategy types, timeframes, and trading approaches to mitigate risk.
4. Forward Test in Demo:
   • Run these selected strategies or portfolios in a demo environment for real-time performance evaluation. This helps validate that they align with their backtested and OOS results.

Summary:

Using these filters across all steps ensures that the strategies you select are robust, consistent, and suitable for live trading. Your criteria already set a high standard for selection, which will help you build a reliable and diversified trading portfolio.

After completing Step 3 (the Walk-Forward Matrix with real tick data), the next step for a final review and comparison involves thorough analysis and filtering to confirm the robustness and reliability of the strategies. Here’s what you should do:

1. Aggregate Results from All Steps:

• Compile the performance metrics from Step 1 (Selected TF + Monte Carlo), Step 2 (1-Minute Data Tick), and Step 3 (Real Tick Data with Walk-Forward Matrix).
• Metrics to compare include:
  • Profit Factor
  • Sharpe Ratio
  • CAGR/Max DD%
  • Drawdown (Max and Open)
  • Stability
  • Win/Loss Ratio
  • Net Profit

2. Consistency Check Across Steps:

• Compare Performance Metrics: Ensure that strategies performing well in Steps 1 and 2 still show good results in Step 3.
• Consistency Across Steps: Prioritize strategies that maintain strong performance and meet your criteria in all testing phases. For example, a strategy with a profit factor consistently above your threshold across all steps is likely more robust.
• Stability Analysis: Verify that the stability metric (e.g., > 0.8) is maintained across the steps, which indicates a consistent equity curve and reduced risk of erratic behavior.

3. Look for Outliers:

• Identify strategies that show significant performance drops or volatility in Step 3 compared to Steps 1 and 2. A drop in metrics such as the Sharpe ratio or a spike in drawdown during the Walk-Forward analysis can signal potential weaknesses under live trading conditions.

4. Select the Most Robust Strategies:

• Filter strategies that pass your final criteria (e.g., consistent Sharpe ratio > 2, profit factor > 1.5, drawdown < 20%).
• Prioritize those that show minimal variation in performance metrics across different testing data sets, as this indicates adaptability and robustness.

5. Portfolio Creation:

• Create Portfolios: Use the most robust strategies to create diversified portfolios. Ensure that these portfolios balance different strategy types and timeframes to reduce risk.
• Simulate Portfolio Performance: Run a final simulation or backtest of your chosen portfolios on real tick data to evaluate how they perform together and assess their combined drawdown and profit factor.

6. Forward Testing and Live Demo:

• Forward Test: Run your selected strategies or portfolios in a live demo account for a few weeks to a few months. This will help validate the strategies’ performance in real-time conditions without risking capital.
• Monitor Performance: Continuously track key metrics and look for consistency with backtested and Walk-Forward results.

7. Compare and Review the Results:

• Final Review Report: Create a comparison report summarizing the performance of each strategy across all testing steps and during the forward test. Highlight key metrics, any significant deviations, and overall performance trends.
• Decision Making: Choose to proceed with strategies or portfolios that maintain robust performance throughout all phases, as they are likely to be the most reliable for live trading.

This comprehensive review ensures that the strategies you move forward with are robust, adaptable, and optimized for real trading environments.

Yes, when adapting your XAUUSD strategies for AUDUSD, you should use the same ranking and filtering criteria as you did for XAUUSD to maintain consistency in your evaluation process. This ensures that you are comparing strategies on the same performance metrics and selecting only those that meet your standards.

Key Metrics for Consistency:

1. Profit Factor: Ensure the strategies maintain a profit factor greater than your minimum threshold (e.g., >1.5). This shows that they are still profitable on AUDUSD.
2. Drawdown (DD): Keep your drawdown filter consistent (e.g., maximum drawdown < 20-25%). This helps maintain risk control and ensures the strategies are not exposing too much capital to loss.
3. Sharpe Ratio: Use the same Sharpe ratio threshold (e.g., >2) to ensure the risk-adjusted returns are satisfactory for AUDUSD as well.
4. CAGR/Max DD%: Maintain your target for CAGR/Max DD% (e.g., >1) to ensure that the strategy’s return is proportionate to the risk taken.
5. Other Metrics:
   • Stability: Ensure the stability of the equity curve is consistent (e.g., >0.8).
   • Open Drawdown %: Keep your limit on open drawdown (e.g., <15%) to avoid high-risk exposure during active trades.

Why Maintain the Same Ranking Filters:

• Consistency Across Pairs: Using the same criteria allows for an apples-to-apples comparison, ensuring that only strategies that meet your rigorous standards on XAUUSD are considered for AUDUSD.
• Robustness Verification: If strategies pass the same filters across different pairs, it indicates that they are robust and more likely to perform well in varying market conditions.
• Confidence in Strategy Selection: Maintaining the same filters helps build confidence that your strategy selection process is solid and proven.

Steps to Implement:

1. Apply Your Existing Filters: Use your current filtering criteria for profit factor, drawdown, Sharpe ratio, CAGR/Max DD%, etc., when retesting or optimizing your strategies on AUDUSD.
2. Analyze the Results: Compare the filtered strategies’ performance on AUDUSD with their original performance on XAUUSD.
3. Select Strategies that Pass Both: Choose strategies that meet your criteria on both pairs for greater robustness and reliability.

By keeping the same evaluation filters, you ensure that your approach to selecting and adapting strategies remains consistent, reliable, and well-aligned with your trading goals.

Primary Filters for Portfolio Selection (Including IS and OOS)

When building portfolios in Portfolio Master and selecting strategies for long-term stability and profitability, you can use Primary Filters that incorporate both In-Sample (IS) and Out-of-Sample (OOS) testing. This approach will ensure that your portfolios are robust not only in the training phase but also in unseen market conditions.

Primary Filters for IS and OOS:

1. CAGR/Max DD% > 1 (IS & OOS)
   • IS: Indicates good profitability relative to risk during the training phase.
   • OOS: Ensures the strategy maintains profitability and does not suffer from overfitting when exposed to new market data.
2. Sharpe Ratio > 2 (IS & OOS)
   • IS: A Sharpe Ratio > 2 ensures that the strategy provides strong returns relative to the risk in the historical data.
   • OOS: A Sharpe Ratio > 2 in OOS testing confirms that the strategy is stable and continues to provide a good risk-to-return ratio when tested on new data.
3. Profit Factor > 1.5 (IS & OOS)
   • IS: Profit Factor > 1.5 indicates that profitable trades outweigh losses during the in-sample test.
   • OOS: In OOS, this shows that the strategy maintains profitability when applied to unseen data, confirming its robustness.
4. Stability > 0.8 (IS & OOS)
   • IS: Ensures the strategy is not prone to extreme fluctuations and behaves in a predictable manner during training.
   • OOS: Stability > 0.8 in OOS ensures that the strategy’s performance is consistent and not erratic in unseen conditions.
5. Open Drawdown % < 15 (IS & OOS)
   • IS: Limiting Open Drawdown to < 15% reduces the risk of large temporary losses during the testing phase.
   • OOS: The same limitation in OOS testing ensures that the strategy remains resilient and avoids high-risk exposure when applied live.
6. Max Drawdown % < 30 (OOS)
   • Focus on limiting the overall Max Drawdown to less than 30% in OOS. It will help maintain manageable risk levels in live trading conditions.

Ranking and Portfolio Creation Process:

1. Filter IS Strategies:
   • Start by applying the above filters to your in-sample (IS) strategies to identify the top performers in your test period.
   • Select those strategies with the highest combined CAGR/Max DD%, Sharpe Ratio, and Profit Factor as the initial candidates for portfolio construction.
2. Evaluate OOS Performance:
   • Once you have the IS candidates, reapply the same filters to assess their out-of-sample (OOS) performance.
   • Focus on Stability, Profit Factor, Sharpe Ratio, and Open Drawdown in OOS. This will confirm that the strategies maintain profitability and stability under new market conditions.
3. Create Portfolios:
   • Create 4–6 portfolios with 4–8 strategies each, prioritizing those that perform well in both IS and OOS.
   • Ensure diversity in the portfolios, balancing strategies with high Profit Factor and those with low drawdowns.
4. Remove/Reuse Strategies:
   • Once a portfolio is built, you can either remove those strategies from your selection pool and repeat the process for the next portfolio, or keep all strategies in the pool to maximize utilization.
   • This way, you can create up to 12 portfolios without overusing any single strategy, optimizing the distribution across your available 500 strategies.

By following this method, you’ll create well-balanced portfolios that are both highly profitable and resistant to market changes, ensuring long-term success.

Suggested Approach for Phase 1 Portfolio Creation with IS and OOS Filtering

In phase 1, the goal is to create several robust portfolios by filtering and ranking strategies to achieve high performance and stability, with a specific focus on their behavior in both in-sample (IS) and out-of-sample (OOS) testing.

1. IS and OOS Filtering:
   • IS: Ensure strategies perform consistently during the in-sample period by prioritizing parameters like CAGR/Max Drawdown (DD%), Sharpe Ratio, and Stability. These metrics demonstrate the strategy’s profitability and risk management in the training phase.
   • OOS: Use filters that emphasize robustness and adaptability to unseen data. High values for metrics like Sharpe Ratio, Stability, and low OOS drawdowns indicate resilience. Also, analyze parameters such as Open DD% and consecutive losses to evaluate risk in live scenarios.
2. Prioritize Key Performance Metrics for Filtering and Ranking:
   • CAGR/Max DD%: A high ratio here is essential for long-term profitability.
   • Sharpe Ratio: Select for a Sharpe Ratio > 2 in both IS and OOS to ensure that returns consistently outweigh risk.
   • Profit Factor: A Profit Factor > 1.5 across IS and OOS indicates that profitable trades outweigh losses.
   • Stability: Aim for Stability > 0.8, ensuring strategies are less likely to be overfitted.
   • Open DD% and Max DD%: Filter for Open DD% < 15% to limit real-time exposure to high drawdowns, with Max DD% providing an additional safeguard.
   • Net Profit and R-Squared (R²): These are supportive metrics, with higher values indicating stronger trends in profit generation.
3. Portfolio Structuring:
   • From the filtered strategies, construct 4–6 portfolios, each containing 4–8 strategies that exhibit the highest OOS Sharpe Ratios, Stability, and low drawdowns. Ensure each portfolio balances a mix of high-return and stable strategies.
   • Once these portfolios are built, remove selected strategies from the list for the next round of portfolio creation, or, alternatively, create all portfolios from the initial list to maximize the use of all high-performing strategies without deletion.

This approach ensures each portfolio is diversified in both strategy type and performance metrics, optimizing for long-term stability across various market conditions.

For filtering strategies in In-Sample (IS) and Out-of-Sample (OOS) phases, here’s how you should approach it to ensure long-term stability:

Filtering by IS and OOS:

Both IS and OOS performance are critical, as they provide insight into how the strategy performs in known market conditions (IS) and unseen market conditions (OOS). The goal is to ensure the strategy generalizes well beyond the backtested data.

Key Steps to Approach IS and OOS Filtering:

1. Separate IS/OOS Data

Make sure your dataset has been divided into In-Sample (IS) and Out-of-Sample (OOS) parts. IS is used for training or optimization, while OOS is used to test the strategy’s robustness.

2. Prioritize OOS for Long-Term Viability

• OOS performance is more important than IS because it demonstrates how the strategy handles real, unseen market conditions. Always filter strategies that have both IS and OOS results, but give more weight to OOS metrics to ensure robustness.

Ranking and Filtering for IS and OOS:

To set up your filters and ranking criteria for both IS and OOS, here’s how to prioritize:

1. Sharp Raio (OOS > IS)1. Sharpe Ratio (OOS > IS)

• IS: Should be > 1.5. A high IS Sharpe Ratio indicates good performance in training data.
• OOS: This should be your priority and should ideally be > 2. It shows strong performance with minimal volatility in unseen data.
• Why: If OOS Sharpe is much lower than IS, the strategy may be overfit. The OOS Sharpe Ratio >2 confirms the strategy can handle real-world volatility.

2. CAGR/Max DD% (OOS > IS)

• IS: Should be > 1, but OOS should be higher than IS.
• OOS: Should be prioritized here as it shows the return achieved per unit of drawdown in unseen market conditions. Aim for > 1.
• Why: Ensuring the OOS CAGR/Max DD% is strong proves that the strategy will continue to generate healthy returns with manageable risk.

3. Profit Factor (OOS close to IS)

• IS: Look for > 1.5 to ensure profitability in known data.
• OOS: Should ideally be close to IS performance. Aim for > 1.5, and if OOS is significantly lower, be cautious.
• Why: Large gaps between IS and OOS profit factor indicate potential overfitting.

4. Return/Drawdown Ratio (Ret/DD) (OOS > IS)

• IS: Should be > 2.
• OOS: Should be comparable or better than IS, ideally > 2. If OOS Ret/DD drops too much compared to IS, the strategy may not generalize well.
• Why: This helps you assess the balance of returns versus risk in unseen conditions. Favor strategies where OOS Ret/DD is consistent with IS.

5. Stability (OOS close to IS)

• IS: Should be > 0.8 to indicate a stable equity curve in the training data.
• OOS: Should be close to IS. Significant drops here mean the strategy is less reliable in real conditions.
• Why: High stability in OOS ensures the strategy performs smoothly without wild fluctuations in live trading.

6. Open Drawdown % (OOS < IS)

• IS: Set the threshold < 15% to limit exposure in known conditions.
• OOS: This should be the same or lower than IS to ensure the strategy doesn’t take on too much risk when live. Prioritize < 10-12% for OOS.
• Why: You want to reduce risk in live markets, so OOS drawdown should ideally be lower than IS.

7. Max Drawdown % (OOS < IS)

• IS: Set a threshold < 20-25%.
• OOS: Should be the same or lower than IS to avoid risk spikes in live trading. < 20% is preferable.
• Why: Keeping OOS max drawdown low is critical to ensuring the strategy doesn’t suffer big losses in real-world conditions.

8. Net Profit (OOS close to IS)

• IS: Look for solid net profit but not at the expense of risk.
• OOS: Should be close to IS. If OOS profit is significantly lower than IS, this could be a sign of overfitting.
• Why: The goal is to ensure consistent profitability across both IS and OOS phases.

9. R-Squared (R²) (OOS close to IS)

• IS: Look for R² to indicate a strong trend, but don’t prioritize it too heavily.
• OOS: Should be close to IS values. Large differences could signal instability in the equity curve.
• Why: R² helps in assessing whether the strategy maintains its trend in unseen data.

10. Consecutive Win/Loss Trades (OOS > IS)

• IS: Avoid strategies with large streaks of losses.
• OOS: Should be comparable to IS, and you want to avoid any strategies where the number of consecutive losses is much higher in OOS.
• Why: A strategy that maintains its win/loss consistency across IS and OOS will be more robust.

Suggested Filter Prioritization for IS/OOS:

1. Sharpe Ratio (OOS > IS)
2. CAGR/Max DD% (OOS > IS)
3. Return/Drawdown Ratio (Ret/DD) (OOS > IS)
4. Profit Factor (OOS close to IS)
5. Stability (OOS close to IS)
6. Open Drawdown % (OOS < IS)
7. Max Drawdown % (OOS < IS)
8. Net Profit (OOS close to IS)
9. Consecutive Win/Loss Trades (OOS > IS)
10. R-Squared (R²) (OOS close to IS)

Summary

When filtering and ranking strategies, OOS performance should take precedence over IS, as it reflects the strategy’s performance in live, unseen market conditions. Always ensure that OOS metrics are close to or better than IS to confirm the robustness of your strategy, particularly Sharpe Ratio, CAGR/Max DD%, and Ret/DD.

In phase 1, which says to create 4-6 portfolios, in this part, what should I filter and rank them by? 1. Profit Factor 2. CAGR/Max DD% 3. Sharpe Ratio 4. Return/Drawdown Ratio (Ret/DD) 5. Stability 6. Open Drawdown % 7. Max Drawdown % 8. Consecutive Win/Loss Trades 9. Net Profit 10. R-Squared (R²)

To build the 4-6 portfolios in Phase 1, focusing on filtering and ranking the strategies effectively, I would suggest using a weighted approach to emphasize the metrics that align with both profitability and risk management for long-term stability. Here’s how to prioritize and filter the metrics:

1. CAGR/Max DD% (Priority: High)

• Why: This is one of the most crucial metrics because it shows how well the strategy performs in terms of growth while managing risk. Higher values mean you get better returns for each unit of risk.
• How to Filter: Select strategies with a CAGR/Max DD% > 1 as a minimum requirement, and prioritize strategies that have values significantly greater than 1 (e.g., >1.5 or >2) for better risk-adjusted returns.

2. Sharpe Ratio (Priority: High)

• Why: The Sharpe Ratio indicates risk-adjusted returns. A Sharpe Ratio > 2 is generally considered excellent, as it means the strategy is delivering high returns for the level of volatility/risk involved.
• How to Filter: Filter out strategies with Sharpe ratios below 2. Rank remaining strategies by Sharpe Ratio as a second key factor.

3. Profit Factor (Priority: Medium-High)

• Why: The Profit Factor compares gross profits to gross losses. A Profit Factor > 1.5 indicates that for every dollar risked, the strategy earns at least 1.5 dollars.
• How to Filter: Filter strategies with a Profit Factor above 1.5 and rank them to ensure a healthy balance of profits vs losses.

4. Return/Drawdown Ratio (Ret/DD) (Priority: Medium-High)

• Why: This metric reflects how much return you are getting for each unit of drawdown. A higher value indicates better performance relative to risk.
• How to Filter: Ret/DD values should be high (>2), and strategies with strong Ret/DD ratios should be given more weight in the ranking process.

5. Stability (Priority: Medium)

• Why: This measures the consistency of the equity curve. Stability above 0.8 ensures smoother performance with fewer wild swings.
• How to Filter: Filter for strategies with Stability > 0.8 and rank by Stability to ensure you are selecting smooth performers.

6. Open Drawdown % (Priority: Medium)

• Why: Open Drawdown % indicates how much capital is at risk while a trade is active. Keeping this low ensures that you don’t have a lot of capital exposed to ongoing risk.
• How to Filter: Select strategies with Open DD < 15% to avoid excessive risk during open trades.

7. Max Drawdown % (Priority: Medium)

• Why: Max Drawdown % shows the largest peak-to-trough drop in equity. Lower drawdowns indicate less capital loss during downturns.
• How to Filter: Set a maximum acceptable drawdown threshold (e.g., Max DD < 20-25%), and give higher preference to strategies with lower Max Drawdowns.

8. Net Profit (Priority: Medium-Low)

• Why: Net Profit is important for absolute returns but should not overshadow risk-adjusted performance metrics.
• How to Filter: While this metric should not dominate, it’s important to ensure the strategies are delivering consistent profits. Rank by profit only after other risk-adjusted metrics.

9. R-Squared (R²) (Priority: Low)

• Why: R² measures the strength of the trend in the equity curve. While useful, this can be less relevant for short-term portfolios.
• How to Filter: R² should be considered, but not weighted too heavily in early portfolio building stages.

10. Consecutive Win/Loss Trades (Priority: Low)

• Why: This is more of a psychological metric that shows how many wins or losses can occur consecutively. While useful, this is not a key metric for filtering.
• How to Filter: Use this as a final check to ensure your selected strategies don’t have extreme streaks of losses or wins, which could indicate volatility.

Suggested Approach for Phase 1 Portfolio Creation:

1. Primary Filters: Start by filtering with CAGR/Max DD% > 1, Sharpe Ratio > 2, Profit Factor > 1.5, Stability > 0.8, and Open Drawdown < 15%.
2. Ranking: Once you’ve applied these filters, rank the remaining strategies using CAGR/Max DD%, Sharpe Ratio, and Ret/DD as your top three criteria.
3. Secondary Filters: As a secondary consideration, check Max Drawdown %, Net Profit, and Stability to ensure balanced strategies.
4. Final Checks: Lastly, apply Consecutive Win/Loss Trades and R² to fine-tune for consistency and smoother equity curves.

This structured approach will allow you to maximize the robustness and profitability of your strategies while controlling risk and volatility.

Added ability to filter by IS and OOS in each topic to see which IS/OOS should be used.

When adding the ability to filter by In-Sample (IS) and Out-of-Sample (OOS) performance in each metric or topic, it becomes critical to differentiate between the two phases of strategy testing. The IS data is used to develop and optimize the strategy, while the OOS data tests the strategy on unseen, untouched market conditions to assess its robustness.

Here’s how to approach filtering each metric for IS and OOS:

IS (In-Sample) vs OOS (Out-of-Sample) Explanation

• In-Sample (IS): The period used for building and optimizing the strategy. High IS performance may indicate overfitting if OOS performance is poor.
• Out-of-Sample (OOS): The unseen data used to test the robustness of a strategy. Good OOS performance indicates that the strategy generalizes well to unseen market conditions.

Metric Prioritization and IS/OOS Expectations

1. Profit Factor
   • IS Target: Should be > 1.5.
   • OOS Target: A good profit factor in OOS should remain above 1.3 or higher to show that the strategy is profitable without overfitting.
   • Purpose: This shows how well the strategy is balancing profits against losses both in-sample and out-of-sample.
2. CAGR/Max DD%
   • IS Target: > 1 (This indicates returns are higher than the risk during optimization).
   • OOS Target: Should ideally remain above 0.8 in OOS. If the value dips significantly, it might indicate over-optimization in the IS phase.
   • Purpose: It helps compare growth to risk over the strategy’s life cycle. Stability in this metric across IS and OOS is important for long-term performance.
3. Sharpe Ratio
   • IS Target: > 2 (This shows good risk-adjusted performance in-sample).
   • OOS Target: Should remain above 1.5 in OOS. Sharpe ratios dropping significantly in OOS may indicate risk or volatility problems in unseen data.
   • Purpose: This measures the strategy’s ability to handle risk consistently, and OOS results are crucial to validate its stability in live trading.
4. Return/Drawdown Ratio (Ret/DD)
   • IS Target: > 2 (Shows solid returns in comparison to risk).
   • OOS Target: Ideally, you want this to stay above 1.5 to confirm stability.
   • Purpose: A high Ret/DD ratio in IS shows good returns relative to risk, but if the OOS version drops sharply, it indicates the strategy may not handle risk well in real market conditions.
5. Stability
   • IS Target: > 0.8 (A smooth, consistent equity curve).
   • OOS Target: Stability should remain > 0.7 in OOS. A significant drop indicates the strategy’s performance may become inconsistent in live conditions.
   • Purpose: High stability across IS and OOS ensures a smooth equity curve, avoiding sharp performance drops in real trading.
6. Open Drawdown %
   • IS Target: < 15% (Keeps risk exposure in check during optimization).
   • OOS Target: Should remain below 20% in OOS testing. If OOS drawdowns significantly exceed the IS values, the strategy may struggle in volatile or unseen markets.
   • Purpose: Managing open drawdowns is critical for risk control, and seeing how this behaves OOS ensures you’re not taking on excessive risk in real trading.
7. Max Drawdown %
   • IS Target: < 20% (Manages maximum peak-to-valley losses).
   • OOS Target: Should not exceed 25% in OOS. Drawdown stability between IS and OOS indicates the strategy is capable of handling stress.
   • Purpose: This metric helps manage risk, ensuring that no single market condition causes extreme losses.
8. Consecutive Win/Loss Trades
   • IS Target: > 2 (Aims for win streaks without significant drawdowns).
   • OOS Target: Should be similar to IS results, or show a smooth transition. Long losing streaks in OOS can be a warning sign.
   • Purpose: This shows how often you can expect consecutive wins or losses. Stability between IS and OOS is a good indicator of robustness.
9. Net Profit
   • IS Target: Positive net profit in IS with steady growth.
   • OOS Target: Should maintain a positive net profit. If OOS performance is significantly lower, this indicates overfitting.
   • Purpose: Maintaining positive net profit in both IS and OOS is crucial for real trading profitability.
10. R-Squared (R²)
    • IS Target: > 0.8 (A high R² shows smooth, predictable growth).
    • OOS Target: Should remain above 0.7 to avoid overfitting. A significant drop in OOS means the strategy is inconsistent.
    • Purpose: This metric helps you find strategies that perform smoothly without high volatility in their returns.
11. Sortino Ratio
    • IS Target: > 1.5 (Indicates good risk-adjusted returns considering only downside volatility).
    • OOS Target: Above 1.2 in OOS. A sudden drop in Sortino indicates the strategy is exposing itself to more downside risk than it did during optimization.
    • Purpose: This is a good alternative to Sharpe Ratio, focusing on negative risk rather than total risk. Stability in OOS is critical for downside protection.

How to Use These Filters in SQX:

1. First pass with IS filters:
   • During the initial build/optimization phase, filter strategies based on high values for key metrics in the IS data. Use Profit Factor > 1.5, CAGR/Max DD% > 1, Sharpe Ratio > 2, Ret/DD > 2, and Stability > 0.8.
2. Second pass with OOS filters:
   • After IS optimization, evaluate the strategies in the OOS phase. Set slightly lower but still acceptable values: Profit Factor > 1.3, Sharpe Ratio > 1.5, CAGR/Max DD% > 0.8, Ret/DD > 1.5, and Stability > 0.7.
3. Compare IS and OOS performance:
   • Discrepancies between IS and OOS are normal, but they should not be drastic. If a strategy performs well in IS but poorly in OOS, it’s likely overfitted. Favor strategies with consistent metrics across both phases.

By maintaining these IS/OOS targets in SQX, you can ensure your strategies are robust and able to handle live trading without deteriorating due to overfitting.