DNYL . SPACE

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.

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.

In StrategyQuant’s Optimizer, the Simple and Walk-Forward Matrix optimizations serve different purposes and offer distinct methodologies for testing and optimizing strategies.

1. Simple Optimization

• Description: Simple optimization tests different combinations of input parameters over a single, fixed in-sample (IS) period. It runs through all the possible parameter combinations within the selected range and finds the best-performing combinations based on the chosen criteria (e.g., profit factor, Sharpe ratio, etc.).
• Purpose: It’s useful for understanding how specific parameters affect strategy performance and for finding optimal values within a fixed timeframe.
• Process:
  • Runs on a fixed period of historical data (in-sample).
  • Tests all combinations of input variables (e.g., moving average length, stop-loss values).
  • Ranks the parameter sets according to the fitness function you’ve defined.
• Limitation: Because it optimizes based on a single in-sample period, the results might be overfit and less robust when exposed to unseen data (out-of-sample).

2. Walk-Forward Matrix Optimization

• Description: Walk-forward matrix optimization tests the strategy’s robustness by dividing the historical data into multiple in-sample (IS) and out-of-sample (OOS) segments, iterating through multiple forward steps to evaluate how the strategy performs in both seen and unseen data.
• Purpose: This method helps validate that a strategy isn’t overfitted by testing it on unseen data (OOS), providing a better sense of how the strategy will perform in live trading. It’s used to determine the strategy’s adaptability to new market conditions.
• Process:
  • The historical data is split into several consecutive segments.
  • For each segment, part of the data is used for optimization (in-sample), and the remaining is used for testing (out-of-sample).
  • The process is repeated across multiple segments to simulate how the strategy would behave in live market conditions.
  • This creates a matrix of results, allowing you to observe the consistency of the strategy’s performance across different market conditions.
• Advantage: Walk-forward matrix optimization provides a more robust analysis by simulating live trading conditions and testing the strategy’s ability to adapt to new data. It helps avoid overfitting by evaluating how well the strategy performs outside the optimized period.

Key Differences:

• Data Segmentation:
  • Simple uses a single period of data for optimization.
  • Walk-Forward Matrix splits data into several in-sample and out-of-sample segments for more robust testing.
• Overfitting Risk:
  • Simple can lead to overfitting since it only focuses on one dataset.
  • Walk-Forward Matrix helps reduce overfitting by ensuring the strategy performs well on unseen data (OOS).
• Use Case:
  • Simple is great for initial parameter testing and quick optimization.
  • Walk-Forward Matrix is essential for ensuring the robustness of a strategy, particularly when preparing it for live trading.

If your goal is to create a robust strategy that can adapt to live trading conditions and remain reliable over time, the Walk-Forward Matrix is typically a better choice for long-term performance stability.

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.

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.

To filter and select the best strategies from your 300+ strategies in Portfolio Master, follow a systematic approach that aligns with your ranking criteria and ensures that only the most robust and stable strategies are included in the final portfolio. Here’s a step-by-step guide to filtering:

1. Apply Your Ranking Criteria

Use the criteria you’ve already set for ranking individual strategies: – CAGR/Max DD% > 1 – Sharpe Ratio > 2 – Profit Factor > 1.5 – Stability > 0.8 – Open DD% < 15%

This initial filter should remove many strategies that don’t meet the baseline performance requirements.

2. Check In-Sample (IS) and Out-of-Sample (OOS) Performance

• IS Testing: Review the performance of each strategy during the in-sample period to ensure that it was optimized correctly.
• OOS Testing: More importantly, assess the performance during out-of-sample testing to ensure the strategy is robust in unseen market conditions. Strategies with a high OOS Sharpe ratio and low OOS drawdowns are more likely to perform well in live trading.

3. Evaluate Strategy Correlation

• Use correlation analysis to ensure that strategies in your portfolio are not highly correlated. Highly correlated strategies expose you to similar risks across multiple strategies, increasing the chances of simultaneous drawdowns.
• Keep strategies with low or negative correlation in your portfolio to achieve diversification and reduce risk.

4. Monte Carlo Simulations

• Perform Monte Carlo stress tests on your selected strategies to assess how they handle market noise and randomness. Strategies that consistently perform well under Monte Carlo simulations are more robust.
• Focus on strategies with a low chance of ruin and those that maintain good metrics like Sharpe ratio and Profit Factor in the simulated results.

5. Examine Trade Frequency and Conditions

• Look for strategies that maintain consistent trade frequency and avoid excessive overtrading, which can increase transaction costs and slippage.
• Ensure that strategies follow a balance of trend-following, breakout, and swing trading methodologies (as you’ve chosen) to reduce dependency on a single market condition.

6. Filter for Drawdown Recovery

• Evaluate how fast each strategy recovers from drawdowns. Strategies that recover quickly are generally more reliable for long-term use. Use the drawdown recovery time as an additional filter.

7. Forward Testing

• Take your top strategies and conduct a small-scale forward testing or paper trading to see how they behave in current market conditions. Live market dynamics often differ from historical data, so this step is crucial for final validation.

8. Select a Balanced Portfolio

• After narrowing down the best-performing strategies, use Portfolio Master to combine them into an optimized portfolio with diverse market exposures and timeframes (H1/H4, M15/H4, etc.).
• Make sure the combined strategies adhere to your risk management rules, such as RRR > 1:3, duplicate orders, closing positions on Fridays, and break-even trailing stops.

9. Monitor Ongoing Performance

• Continue to monitor the performance of your portfolio once it’s live, making adjustments only if necessary. Pay attention to how strategies perform during different market phases (trending, ranging, volatile) to ensure long-term stability.

By following this structured filtering process, you’ll be able to select a portfolio of strategies that not only meet your criteria but are also robust, diversified, and ready for live trading.

Yes, it is possible to find or build a strategy that meets these criteria, but it will depend on several factors, including the quality of your data, the optimization process, and the strategy type.

Here’s a breakdown of what each criterion implies and how achievable it is:

1. CAGR/Max DD% > 1:

• Achievability: This is a reasonable and important target, as it ensures your strategy produces more growth than risk. You can achieve this through proper risk management, consistent profits, and by minimizing drawdowns.
• Tuning Tips: Focus on risk control, such as using tight stop losses and proper position sizing (e.g., your 0.01 lot per $100 account balance rule).

2. Sharpe Ratio > 2:

• Achievability: A Sharpe Ratio above 2 is challenging but realistic, especially with a well-optimized strategy. It implies high risk-adjusted returns with low volatility.
• Tuning Tips: To achieve this, focus on strategies that reduce large fluctuations in returns. Use techniques like trailing stops, break-even exits, and conservative risk management. Avoid strategies with excessive drawdowns or those that rely on highly volatile instruments without proper hedging.

3. Profit Factor > 1.5:

• Achievability: This is a common target for profitable trading strategies. A profit factor above 1.5 indicates that your gross profits are at least 1.5 times larger than your gross losses.
• Tuning Tips: Optimize entry/exit rules, avoid overtrading, and focus on strategies that aim for higher reward-to-risk ratios, such as your target of RRR > 1:3.

4. Stability > 0.8:

• Achievability: Stability near 0.8 is a realistic goal for most well-performing strategies. It shows a relatively smooth and reliable equity curve, which is necessary for long-term performance.
• Tuning Tips: Stability can be enhanced by reducing the frequency of large drawdowns, fine-tuning exit rules, and optimizing over a range of market conditions (using IS and OOS testing).

5. Open DD% < 15%:

• Achievability: This is achievable but can be difficult if you’re targeting high returns. Controlling open drawdown is crucial to keep the account safe from volatile market swings.
• Tuning Tips: You can keep open drawdown in check by adjusting trade size, using dynamic stop-loss management (e.g., ATR-based stops), or employing partial exits as trades move into profit.

Finding Such a Strategy:

1. Backtesting and Optimization: You’ll likely need extensive backtesting with 20 years of M1 data (as you’re already using) to find strategies that meet all these criteria. Sequential optimization across timeframes (H1, H4, etc.) can help refine strategies for long-term stability.
2. Strategy Combination: You may need to combine trend following, breakout, and swing trading methods, as these tend to perform well in various market conditions.
3. Fine-Tuning Parameters: Parameters like risk management rules, trailing stops, and exit timing (closing trades before Friday or after 5 days) will play a key role in managing drawdowns and ensuring the other metrics hold up.

It will take work, but it is possible to find a strategy that fits within these boundaries. Be prepared for iterative testing and refinement in StrategyQuant X to optimize across these performance metrics.

Great! Now that we’re moving forward with the Trend + Breakout + Swing Trading combination, here’s the plan to proceed:

Steps for Optimization and Backtesting:

1. Initial Setup:
   • Main Timeframe: H1 (for active signals).
   • Sub Timeframe: H4 (for confirmation).
   • Open Duplicate Orders allowed, with signals from Trend Trading (ADX, Ichimoku, MACD), Breakout (Bollinger Bands, ATR, Momentum), and Swing Trading (RSI, Fractals, MACD).
   • Implement trailing stop and break-even with ATR or Parabolic SAR.
   • Ensure RRR > 1:3 and close all trades by Friday.
2. Optimization:
   • Risk and Lot Size Management: 0.01 lots per $100 balance, with risk percentage on the account.
   • Sequential optimization for additional timeframes like M30/H4, M15/H4, M5/H4 to explore robustness across different market conditions.
   • Focus on finding the optimal parameters for indicators like ATR and Ichimoku to maximize profitability across both trending and volatile markets.
3. Backtesting:
   • Using your M1 data over 20 years, run the backtest for IS (In-Sample) and OOS (Out-of-Sample) periods.
   • Analyze performance metrics: Profit Factor, RRR, Stability, Drawdown, and Win/Loss ratio.
4. Validation:
   • Post-backtest, validate on OOS data to ensure consistency and reliability in live market conditions.

A Sharpe ratio greater than 1 is a positive indicator for a trading strategy, but when it’s over 2, it signifies even better performance. Here’s a breakdown:

• Sharpe Ratio > 1: Indicates that the strategy is generating more returns than the risk it’s taking. A ratio above 1 means the excess returns (returns above the risk-free rate) are higher than the volatility of the strategy’s returns.
• Sharpe Ratio > 2: This is considered excellent performance. A Sharpe ratio over 2 means the strategy is delivering significantly higher returns relative to the amount of risk (volatility) it’s exposed to. It suggests the strategy is not only profitable but also doing so with low volatility and risk. This kind of performance is desirable for long-term strategies, as it indicates a strong balance between risk and reward.

What a Sharpe Ratio Over 2 Means:

1. Efficient Risk Management: The strategy takes relatively low risk compared to the high returns it generates.
2. Low Volatility: The equity curve should be relatively smooth, with fewer large drawdowns.
3. Consistency: It’s a sign of strong risk-adjusted returns—the strategy is not reliant on a few lucky trades but generates returns consistently over time.

In summary, a Sharpe ratio over 2 indicates an outstanding risk-reward balance, and such a strategy is highly desirable, especially if the ratio remains consistent across both In-Sample (IS) and Out-of-Sample (OOS) testing. However, always double-check for overfitting or data snooping—make sure that this performance translates well in OOS and live trading conditions.

i just building and got some prototype but i asking u: Profit factor, Ret/DD ratio, Stability, Fitness, Open DD%, Win/Loss ratio and CAGR/Max DD%. Rank these values ​​from most important to least important that I should consider, including the analysis of both IS and OOS at each stage.

When evaluating a trading strategy, especially in StrategyQuant X (SQX), the metrics you mentioned are all important but serve different purposes. Ranking them depends on your specific goals (e.g., minimizing risk, maximizing returns, or focusing on long-term sustainability). Here’s a ranking from most important to least important, considering both In-Sample (IS) and Out-of-Sample (OOS) stages:

1. CAGR/Max DD% (Compound Annual Growth Rate / Maximum Drawdown Ratio)

• Why It’s Important: This is a key metric for long-term growth and sustainability of the strategy. It balances how much the strategy grows versus how much it risks. A high CAGR with a low Max Drawdown ensures that the strategy is generating returns without taking excessive risk.
• IS/OOS Consideration: A good ratio in both IS and OOS suggests that the strategy is scalable and robust across different market environments.
  • IS: Focus on optimizing this metric to ensure strong growth.
  • OOS: Ensure consistency; a large drop in OOS could indicate overfitting.

2. Profit Factor

• Why It’s Important: Profit Factor (gross profit divided by gross loss) is essential for understanding the overall profitability of the strategy. A Profit Factor greater than 1 indicates a profitable system, with values above 1.5 or 2 being ideal.
• IS/OOS Consideration: Profit Factor must remain solid in OOS testing. If it drops significantly OOS, the strategy may be curve-fitted.
  • IS: Aim for a strong value, but don’t prioritize it at the expense of risk management.
  • OOS: Focus on stability and a consistent profit factor.

3. Ret/DD Ratio (Return-to-Drawdown Ratio)

• Why It’s Important: This metric balances returns against drawdowns. A high Ret/DD ratio indicates that the strategy generates good returns relative to the risk (drawdown) it takes.
• IS/OOS Consideration: It’s important in both IS and OOS stages because it ensures that your strategy’s performance isn’t reliant on taking high risks. Look for consistency across both.
  • IS: Focus on maximizing returns while keeping drawdowns minimal.
  • OOS: The ratio should not degrade significantly, ensuring stability.

4. Open Drawdown % (Open DD%)

• Why It’s Important: Open DD% tracks the maximum floating loss during a trade that hasn’t closed yet. This is crucial for understanding the worst-case scenarios and how your strategy behaves in adverse conditions. High Open DD% could signal that the strategy lets trades run into significant losses before closing them.
• IS/OOS Consideration: In OOS, you want to see a manageable Open DD%. A high Open DD% in OOS could indicate riskier trades.
  • IS: Optimize to keep Open DD% within acceptable risk limits.
  • OOS: Check if the Open DD% stays controlled in unseen data.

5. Stability

• Why It’s Important: Stability measures how consistent the equity curve is over time. A stable equity curve is essential for long-term profitability and avoiding emotional stress from sharp equity fluctuations.
• IS/OOS Consideration: Stability should be maintained across both IS and OOS phases to ensure that the strategy isn’t over-optimized to one specific market condition.
  • IS: High stability is desirable, but watch out for overfitting.
  • OOS: Check if stability remains intact without large drops in equity.

6. Win/Loss Ratio

• Why It’s Important: While the Win/Loss ratio is helpful, it doesn’t paint a full picture. A strategy with a lower win rate can still be profitable if the winning trades are larger than the losing ones. It’s more useful when combined with other metrics like Profit Factor or Return-to-Drawdown.
• IS/OOS Consideration: While optimizing, focus less on having a high Win/Loss ratio and more on profitability. The Win/Loss ratio may vary but shouldn’t drastically change between IS and OOS phases.
  • IS: It’s fine if the Win/Loss ratio isn’t extremely high as long as the strategy is profitable.
  • OOS: Ensure the ratio doesn’t drop significantly.

7. Fitness

• Why It’s Important: Fitness is an overall measure used by SQX to rank strategies based on multiple performance metrics. It’s a good indicator in the early stages of strategy generation but can be less reliable than specific metrics like CAGR/Max DD% or Profit Factor for deep analysis.
• IS/OOS Consideration: While a high fitness score is useful for identifying strong candidates in genetic evolution, don’t rely on it too heavily in OOS testing. Focus more on the other key metrics.
  • IS: A high fitness score helps in the strategy-building process.
  • OOS: Use other metrics to verify performance rather than fitness alone.

Stage-Specific Considerations:

In-Sample (IS) Analysis:

• In the IS phase, you are primarily focusing on optimizing your strategy.
  • Prioritize CAGR/Max DD%, Profit Factor, and Ret/DD Ratio. These three metrics ensure that you have a strategy that is growing profitably with manageable risk.
  • Pay attention to Stability and Open DD% next, as they will indicate how smooth your strategy’s equity curve is and whether you can tolerate the drawdowns during open trades.
  • Win/Loss Ratio is less critical here, as it can be misleading if isolated from other factors. A lower Win/Loss ratio can be fine as long as the profits outweigh losses.
  • Fitness is helpful early on but don’t let it overshadow more significant performance metrics.

Out-of-Sample (OOS) Analysis:

• In the OOS phase, your focus shifts to robustness and ensuring the strategy is not overfitted to past data.
  • Look for consistency in CAGR/Max DD%, Profit Factor, and Ret/DD Ratio between IS and OOS. If these metrics significantly degrade, it’s a sign of overfitting.
  • Stability becomes more crucial here. A strategy that maintains stability in OOS is likely to perform well in live markets.
  • Open DD% should be well-managed, and ideally, it should not increase dramatically in OOS testing.
  • The Win/Loss Ratio can fluctuate, but it should not collapse in OOS. Ensure that profitable trades remain larger than losing ones.

Final Ranking Summary:

1. CAGR/Max DD% (Balances growth vs. risk, essential for long-term viability)
2. Profit Factor (Key profitability measure)
3. Ret/DD Ratio (Measures return relative to risk)
4. Open DD% (Manages risk on open trades)
5. Stability (Ensures a smooth equity curve)
6. Win/Loss Ratio (Helpful but secondary to profitability metrics)
7. Fitness (Useful early but less important for final validation)

This ranking should help guide your focus when optimizing and validating strategies in SQX. Let me know if you want to dive deeper into any specific aspect of the metrics!