DNYL . SPACE

To incorporate these elements into your SQX setup for handling maximum drawdown effectively, here are the specific parameters and considerations:

1. Portfolio Diversification:

• Multi-Strategy Approach: In SQX, build and test different strategies that target various market conditions (e.g., trend-following, breakout, mean-reversion). This diversification helps balance drawdowns since each strategy may perform differently during various market phases.
• Asset Selection: Include other assets besides XAUUSD if feasible, such as major currency pairs, to spread risk and reduce correlation.
• Correlation Analysis: Use SQX’s built-in tools to analyze the correlation between strategies. Select a mix with low correlations to ensure the portfolio’s drawdown is less affected by any single market movement.

2. Regular Monitoring:

• Backtesting and Walk-Forward Analysis: Regularly backtest and run walk-forward optimization to see how drawdown patterns evolve over different timeframes. This ensures strategies are resilient and maintain drawdowns below the 10% threshold.
• Out-of-Sample Testing: Test strategies with out-of-sample data to confirm they perform well beyond just in-sample data, helping to validate their robustness in managing drawdown.
• Monte Carlo Simulations: Utilize Monte Carlo analysis in SQX to stress test strategies under various market conditions, identifying those with a consistent maximum drawdown below the desired level.

3. Adaptive Stop Loss:

• ATR-Based Stop Loss: In SQX, configure adaptive stop losses based on a multiple of the ATR (e.g., 1.5x-2x). This makes the stop loss dynamic and better suited to market conditions, allowing trades to have room during high volatility while tightening in calm periods.
• Trailing Stop Loss: Implement a trailing stop loss feature that follows the price as it moves in your favor. Use a percentage of the ATR or a fixed percentage (e.g., 1%) that updates as the price increases.
• Break-Even Adjustments: Set rules for moving the stop loss to break-even after the trade has gained a certain number of pips or a percentage of the ATR. This reduces the risk of a profitable trade turning into a loss.

SQX Parameter Settings for Maximum Drawdown Control:

• Maximum Drawdown Filter: Configure the “Max Drawdown” filter during strategy generation to discard any strategy that shows a drawdown over 10% in backtests.
• Risk Control Rule: Add a rule that limits the number of open trades or caps exposure based on total equity or margin.
• Trailing Stop Mechanism: Set up rules in the strategy logic for trailing stops that trigger once a predefined profit threshold is reached (e.g., trailing starts at 1 ATR profit).
• Portfolio Drawdown Limitation: Use SQX Portfolio Master to set an equity stop for the portfolio, ensuring the combined strategies don’t push drawdown above 10%.

By using these techniques and parameters, you create a structured approach to control drawdown effectively, ensuring strategies are optimized for real-world trading and compliant with risk management standards like those required for FTMO.

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.

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.

Compare with my last step. Now I have more than 500 strategies ready to enter [Portfolio Master] mode by selecting the number of strategies between 4-8 in each Port. I want to know that if I set the values​​above, select the best one from what SQX offers, and create 1 Port, then delete it from the existing list to create it, so that all 500 strategies can be used almost completely, even if they are not as good as the first one I got. And/or I will create it only once from 500 and filter from the above list, selecting the best values ​​to create each Port, so that I will have a total of 12 good portfolios as you suggest, without deleting any of them. Please provide your comments.

Given your setup with more than 500 strategies and the intention to build 12 solid portfolios, here’s a detailed comparison of the two approaches you’re considering:

Option 1: Create a Single Portfolio and Delete Strategies After Use

• Process: You filter your strategies based on the set criteria (CAGR/DD%, Sharpe Ratio, etc.), create a portfolio using the best 4-8 strategies, and then remove the used strategies from the list.
• Advantages:
  • Focus on Highest Quality: By selecting only the top-performing strategies for each portfolio, you ensure that each portfolio is built with the best available options.
  • No Duplication: Strategies used in one portfolio won’t be repeated, which diversifies the approaches and reduces over-reliance on specific strategies.
  • Cleans the List: Removing strategies ensures that you always have a fresh pool for the next portfolio.
• Disadvantages:
  • Risk of Running Out of Quality: As you progress, there may be a diminishing number of high-quality strategies available for later portfolios, potentially forcing you to accept weaker strategies.
  • More Manual Work: You need to keep track of what’s been used and make adjustments after each portfolio creation, which could be time-consuming.

Option 2: Filter and Create Multiple Portfolios without Deleting Strategies

• Process: You filter your strategies according to the same criteria and create 12 portfolios without deleting any strategies, allowing overlap and repeated use across portfolios.
• Advantages:
  • Maximize Usage: You get the most out of your 500 strategies, as none are permanently removed, allowing you to pick the best for each new portfolio.
  • Less Pressure on Strategy Availability: By not deleting strategies, you can create multiple portfolios with some repetition, ensuring that each portfolio still has top-performing strategies.
  • Faster Process: You won’t need to track what’s already been used, simplifying the portfolio creation.
• Disadvantages:
  • Over-Reliance on Certain Strategies: Repeating strategies across portfolios may lead to over-reliance on certain approaches, which could expose all portfolios to similar risks, especially during market drawdowns.
  • Lower Diversification: Even though you have multiple portfolios, repeating strategies means the portfolios may not be as diversified as they could be.

Recommendation

For your goal of building 12 high-quality portfolios, I’d suggest combining aspects of both approaches:

1. Phase 1: Start by creating 4-6 portfolios using the best available strategies, without deleting any of them. This ensures you’re maximizing the top performers for multiple portfolios.
2. Phase 2: For the remaining portfolios, introduce more diversity by removing the strategies already used from your selection pool, ensuring fresh strategies are prioritized.

This blended approach balances strategy quality with portfolio diversity, giving you strong portfolios while maximizing your use of all 500 strategies. In this way, you ensure that you maintain both performance and diversification across the entire portfolio set.

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.

Do you know all the filters / ranking (like profit factor, cagr/dd%, return/dd, ..) to mean profitable or good trend within SQX?

Yes, in StrategyQuant X (SQX), various performance metrics and ranking filters are used to assess whether a strategy is profitable and stable over time. Here’s a list of the most important metrics along with what they indicate and how they can be used for filtering/ranking:

Key Metrics for Evaluating Profitability and Stability

1. Profit Factor
   • What it measures: The ratio of gross profits to gross losses.
   • Good value: A Profit Factor greater than 1.5 is generally considered good, with values over 2 indicating strong performance.
   • Purpose: Filters out strategies that earn significantly more than they lose.
2. CAGR/Max Drawdown % (Compounded Annual Growth Rate / Max Drawdown)
   • What it measures: It compares the strategy’s annualized returns to the maximum drawdown (risk).
   • Good value: A ratio greater than 1 is a positive indicator, meaning the returns are higher than the risk (drawdown).
   • Purpose: Filters strategies that balance returns and risk effectively. Higher values indicate more sustainable growth relative to risk.
3. Return/Drawdown Ratio (Ret/DD)
   • What it measures: Another way to compare the strategy’s return against the drawdown.
   • Good value: The higher, the better. Values above 2 are typically desirable.
   • Purpose: Similar to CAGR/Max DD%, this shows how well the strategy performs against its worst losses.
4. Sharpe Ratio
   • What it measures: The Sharpe Ratio measures risk-adjusted returns, or how much excess return the strategy generates per unit of risk.
   • Good value: A Sharpe Ratio above 1 is considered good, while above 2 is excellent.
   • Purpose: Helps identify strategies that have consistent returns relative to risk.
5. Stability
   • What it measures: Measures the consistency of a strategy’s equity curve (how stable the growth is over time).
   • Good value: A value greater than 0.8 is typically considered good.
   • Purpose: Filters strategies that have steady performance without sharp fluctuations.
6. Open Drawdown %
   • What it measures: The percentage of drawdown that is still open (unrealized loss).
   • Good value: Keeping Open DD% below 15% is often desirable.
   • Purpose: Ensures that strategies don’t leave too much risk open during trades, keeping the drawdowns controlled.
7. Max Drawdown %
   • What it measures: The largest peak-to-valley loss in equity.
   • Good value: Max DD% below 20% is usually acceptable for longer-term strategies, with lower being better for risk-averse traders.
   • Purpose: Filters strategies that limit their risk exposure during downturns.
8. Consecutive Win/Loss Trades
   • What it measures: The maximum number of consecutive winning or losing trades.
   • Good value: This metric is mostly a secondary consideration but can show streaks in winning or losing.
   • Purpose: Can be used to avoid strategies that have long losing streaks.
9. CAGR (Compounded Annual Growth Rate)
   • What it measures: The annualized percentage growth of your account or equity curve.
   • Good value: The higher the better, though it should be balanced with risk measures.
   • Purpose: Shows the growth potential of the strategy.
10. R-Squared (R²)
    • What it measures: Shows the statistical fit of the strategy’s equity curve to a straight line. The closer to 1, the more stable the strategy’s growth.
    • Good value: Above 0.8 is ideal for long-term stability.
    • Purpose: Filters strategies with consistent upward growth.
11. Kelly Criterion
    • What it measures: Calculates the optimal position sizing based on past performance.
    • Good value: A higher Kelly value indicates better risk-adjusted returns, but too high may suggest over-leveraging.
    • Purpose: Guides sizing to maximize growth while managing risk.
12. Net Profit
    • What it measures: The total profit after deducting losses and costs.
    • Good value: Positive and stable net profit over time is crucial, but it should be compared against risk metrics.
    • Purpose: Ensures the strategy is actually making money.
13. Sortino Ratio
    • What it measures: Similar to the Sharpe Ratio but only considers downside risk (negative returns).
    • Good value: A value above 1 is good, and higher values indicate better risk-adjusted performance focusing on negative volatility.
    • Purpose: Helps focus on strategies that minimize downside volatility while delivering good returns.

Filtering Strategies in SQX

• Primary Filters:
  • Sharpe Ratio > 2: Helps find strategies with good risk-adjusted returns.
  • CAGR/Max DD% > 1: Ensures returns are higher than drawdowns.
  • Profit Factor > 1.5: Ensures profits are sufficiently higher than losses.
• Secondary Filters:
  • Open DD% < 15: Ensures drawdowns are within acceptable limits.
  • Stability > 0.8: Ensures a smooth and steady equity curve.
• Other Considerations:
  • Max Drawdown %: This is crucial for risk management, as it can help avoid strategies with sharp drawdowns.
  • Return/Drawdown: Ensures that the strategy’s returns justify the risk it takes.

By using a combination of these filters, especially focusing on Sharpe Ratio, Stability, Profit Factor, and drawdowns, you can filter out strategies that are robust, profitable, and capable of performing well in live trading.

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.

this OOS should be > ?

 

When assessing out-of-sample (OOS) performance, the following benchmarks can help determine if a strategy is robust enough for live trading:

1. OOS Sharpe Ratio:

• A Sharpe ratio greater than 1 indicates that the strategy has delivered excess returns relative to its volatility.
• A Sharpe ratio of greater than 2 is considered excellent, suggesting strong performance and favorable risk-adjusted returns.

2. OOS Drawdown:

• Aim for a maximum drawdown of less than 15%. This benchmark helps ensure that the strategy does not experience severe losses that could jeopardize the trading account.
• Additionally, the recovery time from drawdown is important; faster recovery is generally preferable.

3. Profit Factor:

• A profit factor greater than 1.5 is generally favorable. This means that the strategy makes 1.5 times more profit than it loses.

4. Consistency:

• Look for consistency in OOS performance metrics, such as stable returns and low volatility, over a sufficient sample size (e.g., at least 6-12 months of trading).

Summary:

• OOS Sharpe Ratio > 1 (ideally > 2)
• Maximum OOS Drawdown < 15%
• Profit Factor > 1.5

Meeting these benchmarks can indicate that the strategy is likely to perform well in live trading.

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.

When focusing on long-term stability for your strategy, aside from CAGR/Max DD% and Sharpe Ratio, the most critical metric to consider is Profit Factor. However, other important factors like Stability and Open Drawdown (DD) also play key roles in assessing the reliability of your strategy. Let’s explore these metrics:

1. Profit Factor:

• What it Measures: The ratio of gross profit to gross loss. For example, a profit factor of 1.5 means that for every dollar lost, you are making 1.5 dollars in profit.
• Importance: A higher profit factor (typically > 1.5) is crucial for long-term viability. It shows that the strategy generates more profit than loss over time.
• Why It’s Key for Long-Term Stability: It directly reflects the strategy’s overall profitability, indicating whether it can sustain profitability through different market conditions.
• Ideal Target: Profit Factor above 1.5 is solid, while a value of 2.0 or more is very strong.

2. Stability of Returns (Stability):

• What it Measures: The consistency of your strategy’s performance over time. It looks at the smoothness of the equity curve.
• Importance: Stability indicates how reliable the strategy is at generating returns without significant fluctuations.
• Why It’s Key for Long-Term Stability: A stable equity curve suggests that your strategy can withstand various market environments without experiencing wild swings in performance.
• Ideal Target: Aim for a stability score close to 1, where 1 represents a perfectly straight equity curve.

3. Open Drawdown (Open DD%):

• What it Measures: The maximum unrealized loss during a trade (before it’s closed).
• Importance: While Max Drawdown focuses on the worst drawdown for closed trades, Open DD% shows how much risk your strategy takes while trades are still open.
• Why It’s Key for Long-Term Stability: High open drawdowns can cause emotional stress and can put your account at risk, especially during volatile periods. Keeping open drawdowns low ensures your strategy can withstand market swings without large floating losses.
• Ideal Target: Aiming for Open DD% < 10-15% is generally considered good for long-term sustainability.

Summary of Metrics (Besides CAGR/Max DD% and Sharpe Ratio):

1. Profit Factor (Most Important): Reflects the overall profitability of your strategy. Target >1.5, ideally >2.
2. Stability: Measures how smooth and reliable your equity curve is. Aim for a stability score near 1.
3. Open Drawdown %: Keep this value as low as possible to avoid large floating losses. Aim for <10-15%.

Focusing on these three metrics will help confirm your strategy’s long-term stability and profitability, making it robust enough to withstand different market conditions.

what’s the most priority betwee CAGR/Max DD% and Sharp ratio ?

When prioritizing between CAGR/Max DD% and Sharpe Ratio, it depends on your strategy’s goals and risk tolerance. Here’s how to assess their importance:

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

• What it Measures: The growth of your account over time (CAGR) relative to the worst peak-to-trough decline (Max Drawdown).
• Importance: It tells you how much risk (drawdown) you’re taking to achieve returns. A higher CAGR/Max DD means your returns are efficient in relation to the risk you’ve taken.
• When it’s Critical:
  • When you aim for long-term, steady growth with controlled risk.
  • If you prioritize minimizing drawdowns, which is crucial for strategies that need to stay resilient in volatile markets (like your long-term trading approach).

2. Sharpe Ratio:

• What it Measures: The return per unit of risk (volatility). It measures how well your strategy compensates you for the risk taken (volatility of returns).
• Importance: A higher Sharpe ratio (above 1, or ideally above 2) means the strategy offers a better risk-adjusted return. It’s a good metric for comparing different strategies to see which one generates smoother returns relative to volatility.
• When it’s Critical:
  • When you care about consistent performance and want to compare risk-adjusted returns between strategies.
  • If your strategy experiences large fluctuations in returns, the Sharpe ratio helps determine if the higher returns are worth the risk (volatility).

Comparison in Your Strategy:

• CAGR/Max DD%: Since you are building a strategy for long-term use, minimizing drawdowns (while still growing your account) is a key consideration. This metric is more focused on the capital preservation aspect and will be vital in avoiding large losses that could wipe out significant portions of your account.
• Sharpe Ratio: It’s essential for ensuring that your strategy has consistent returns relative to risk. However, if your drawdowns are well-managed (as per the focus on risk control), the Sharpe Ratio becomes secondary in importance.

Which is More Important?

For your strategy goals (focused on long-term performance and risk management): 1. CAGR/Max DD% should be the top priority because it balances growth with minimizing drawdowns. 2. Sharpe Ratio is important but secondary to CAGR/Max DD%. You should still aim for a Sharpe ratio above 1 (preferably 2+), but don’t compromise your drawdown management to chase a higher Sharpe ratio.

Conclusion:

Focus on maximizing CAGR/Max DD% to ensure your strategy can grow steadily with minimal drawdowns. A high Sharpe Ratio (above 2) is an excellent goal but secondary to ensuring that your strategy survives long-term market swings with controlled drawdowns.

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.