DNYL . SPACE

In backtesting against 20 years of gold data, if my cost is around $1000, should I be concerned with the 2003-2010 period, since gold prices were significantly different after 2010, or should I add more cost for this test?

When backtesting against 20 years of gold data with a cost of $1000, the period from 2003 to 2010 warrants special attention for a few reasons:

Why Focus on 2003–2010?

1. Market Regime Change:
   • Gold underwent a major bull market during 2003–2010, rising from approximately $300 to over $1200 by the end of the period.
   • After 2010, gold experienced new price regimes, including the 2011 peak and subsequent corrections. These changes may mean that strategies working well before 2010 might fail in later, more volatile markets.
2. Cost Significance:
   • A $1000 cost might have represented a larger relative percentage of the price during 2003–2010 (e.g., around 5%-10% of gold’s price) compared to post-2010 periods when gold prices have often been above $1500.
3. Volatility Differences:
   • The volatility structure of gold has evolved over time. Pre-2010, trends were stronger and more directional, while post-2010 has seen more erratic and range-bound movements.

Should You Be Concerned?

Yes, you should carefully evaluate this period because:

1. Disproportionate Impact:
   • If a strategy heavily depends on the trends of 2003–2010, its robustness in post-2010 periods (with more range-bound action) could be in question.
   • Costs may have a larger impact on profitability for strategies built in this regime compared to more recent data.
2. Regime Bias:
   • A strategy optimized for pre-2010 conditions may overfit to that regime and not generalize well for the flatter periods seen later.
3. Risk of Misleading Results:
   • Even if the backtest shows strong performance overall, the pre-2010 period may dominate the results due to high trends and low relative costs.

Adjusting Costs for Realism

To ensure realistic testing:

• Increase the Cost:
  • You can apply a scaled transaction cost to simulate the higher relative impact of costs during periods when gold prices were lower. For example:
    • Use a percentage-based cost relative to price (e.g., 0.2% of the price per trade) instead of a fixed $1000.
  • Alternatively, segment the backtest into multiple periods and apply different costs for each based on gold’s price levels.
• Separate Analysis:
  • Evaluate performance metrics for 2003–2010 and post-2010 separately. This will help you determine if the strategy is overly reliant on the earlier period’s trends.

Practical Steps:

1. Segment Backtest Results:
   • Divide results into multiple time frames (e.g., 2003–2010, 2011–2020, and 2021–present) and analyze performance metrics separately for each.
2. Simulate Robust Costs:
   • Apply higher costs for lower price periods and re-run the backtest to evaluate its robustness across varying market conditions.
3. Evaluate Strategy Adaptability:
   • Check how the strategy performs during flat or range-bound periods (e.g., 2012–2015) compared to strong trends (e.g., 2005–2010).
4. Compare Across Assets:
   • Test the strategy on other metals like silver or correlated assets. This helps assess whether the strategy is robust to different price and volatility structures.

Conclusion:

You should absolutely scrutinize the 2003–2010 period. Adjusting costs based on relative price levels and segmenting your backtest will provide better insight into your strategy’s robustness. Additionally, focusing on OOS performance in post-2010 regimes is critical to avoid overfitting to historical bull markets.

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.

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.

When retesting strategies from XAUUSD on a new pair like AUDUSD, you should first consider retesting with the original parameters before adjusting them. Here’s why, and the next steps if adjustments are needed:

Initial Retesting Without Adjustments:

• Purpose: This helps you identify which strategies are naturally robust and can perform well across different currency pairs without any changes. It acts as a preliminary filter.
• Benefits:
  • Saves Time: Quickly narrows down strategies that might be applicable to AUDUSD.
  • Robustness Check: Strategies that perform well on both pairs without adjustments are likely more reliable in various market conditions.
• Approach: Load the strategies and run them on AUDUSD historical data to evaluate their performance metrics (e.g., profit factor, drawdown, Sharpe ratio).

Adjusting Parameters for Retest:

• When to Adjust: If you find that many strategies are underperforming or only partially meet your criteria, consider retesting with adjusted parameters to better match the characteristics of AUDUSD.
• How to Adjust:
  • Fine-Tune Key Parameters: Adjust strategy-specific inputs like moving average periods, stop loss, take profit, or trailing stop distances to align with AUDUSD’s volatility and behavior.
  • Adjust Risk Settings: AUDUSD may have different average volatility and trading volume compared to XAUUSD, so tweaking risk parameters can help improve performance.
• Benefits:
  • Better Suitability: Tailors the strategies more specifically to AUDUSD.
  • Potential Performance Improvement: Strategies that did not perform well in the initial retest may show significant improvement after adjustments.

Recommended Workflow:

1. Retest Without Adjustments: Start with a simple retest to identify strategies that work out-of-the-box on AUDUSD.
2. Review Metrics: Analyze the retest results and focus on strategies that meet a minimum performance threshold.
3. Adjust and Retest:
   • If you find strategies that are close to being successful, adjust their parameters and retest.
   • Use optimization to find the best set of parameters, but be careful to avoid overfitting.

Key Considerations:

• Balance: Adjusting parameters can enhance performance, but too much adjustment risks overfitting. Aim for a balance that maintains general strategy robustness.
• Start Small: Test parameter adjustments on a smaller set of top-performing strategies before applying them to a larger group.

In summary, start with a straightforward retest without changes. If needed, proceed to fine-tune the strategies’ parameters for AUDUSD to enhance performance and adaptability.

To adapt your 30,000 XAUUSD strategies to another currency pair like AUDUSD, here’s a breakdown of the best approach for each option:

Option A: Retest

• Explanation: Run the existing XAUUSD strategies on AUDUSD data without changing any parameters.
• Pros:
  • Quick Validation: Gives an initial indication of which strategies perform well on a different pair.
  • Robustness Check: Helps identify strategies that are adaptable to different market conditions.
• Cons:
  • No Optimization: Strategies may not perform as well without adjustments specific to AUDUSD’s characteristics.
• Best Use Case: Use this approach to filter out strategies that work well on both pairs without modification.

Option B: Directly Optimize

• Explanation: Use the XAUUSD strategies as a base and optimize their parameters for AUDUSD.
• Pros:
  • Better Performance: Allows the strategies to adapt their parameters to AUDUSD’s unique behavior.
  • Fine-Tuning: Ensures that strategies are more specifically suited for the new pair.
• Cons:
  • Time-Consuming: Optimization takes time and computing power.
  • Risk of Overfitting: There’s a chance of over-optimization, making the strategy less robust in live trading.
• Best Use Case: Use this approach when you need to adapt high-potential strategies to a new pair and improve their performance.

Option C: New Build from Existing Strategies for Initial Strategy

• Explanation: Use insights and structures from the XAUUSD strategies to build new initial strategies for AUDUSD.
• Pros:
  • Customization: Allows for incorporating lessons learned from XAUUSD strategies while tailoring them for AUDUSD.
  • Better Adaptability: The resulting strategies can be optimized specifically for AUDUSD from the start.
• Cons:
  • Takes Longer: Building and testing new strategies requires more time and effort than retesting or direct optimization.
• Best Use Case: Ideal when you need the most tailored and potentially robust strategies for AUDUSD.

Recommendation:

1. Start with Option A (Retest): Quickly filter out adaptable strategies to see which ones perform well on AUDUSD without any modifications.
2. Proceed to Option B (Directly Optimize): Optimize the top-performing strategies from the retest phase to enhance their performance on AUDUSD.
3. Use Option C (New Build): If you find that most strategies require significant adjustments, build new initial strategies using the structures and successful elements from the XAUUSD strategies.

This approach allows you to balance speed, adaptability, and performance to create a robust set of strategies for AUDUSD.

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.

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.

then i got 400 from builds type : Exit everday 23:00, Exit on Friday 23:40, Exit Rules no fix exit day or time but hold not over 10 day and Repeat/Duplicate orders trade. from base H1/H4 TF, so can i use Sequantial optimization by +30% -30% with 10 step for another TF like M30/H4 M15/H4 and M5/H4. what do you think i can get some great results ?

Using sequential optimization by adjusting parameters +30% and -30% with 10 steps for testing other timeframes like M30/H4, M15/H4, and M5/H4 is a smart approach to explore the robustness and adaptability of your strategy across different timeframes. Here’s what to expect and consider:

Benefits of Sequential Optimization:

1. Broader Testing Range: By adjusting the parameters within the 30% range, you’ll ensure that you’re not just fine-tuning for one specific set of market conditions but testing for variability across different timeframes.
2. Identify Timeframe Sensitivity: You’ll be able to see how your strategy behaves with different levels of granularity (e.g., faster-moving timeframes like M5 vs slower ones like M30). This can help you identify if your strategy is overly reliant on a specific timeframe or whether it’s flexible across multiple.
3. Refining Exit Strategies: Since your strategies include time-based exits (e.g., exiting at 23:00 daily or on Fridays at 23:40), sequential optimization will allow you to see if different exit timings yield better results on shorter timeframes. The hold-not-over-10-days rule may work differently on lower timeframes where market volatility plays a bigger role.
4. Exploration of New Opportunities: By shifting to M30/H4, M15/H4, and M5/H4, you’ll open your strategy to new trading opportunities as faster timeframes capture more price fluctuations. This could either increase profitability or increase risk, depending on how well your strategy manages shorter-term price swings.

Things to Watch Out for:

1. Overfitting: The more timeframes and parameters you optimize, the higher the risk of overfitting. To avoid this, focus on keeping core elements of the strategy consistent and avoid over-optimizing for one specific timeframe.
2. Higher Volatility on Lower Timeframes: When moving to lower timeframes (like M15 or M5), strategies can be more prone to whipsaws and false signals due to increased market noise. Sequential optimization can help adjust, but ensure your risk management rules (like SL, TP, and trailing stops) are adaptable to these faster movements.
3. Execution Speed and Slippage: Shorter timeframes (M15 and M5) might experience more slippage and execution issues in live trading. Make sure that the strategy can handle the increased trade frequency without degrading profitability.
4. Strategy Robustness: Pay attention to how the strategy behaves during out-of-sample testing after the optimization across different timeframes. You’re looking for a strategy that performs consistently across multiple timeframes, not just optimized for one.

Expected Results:

• You might find that your strategy performs better on intermediate timeframes like M30/H4 or M15/H4 because these timeframes strike a balance between fast execution and less noise.
• M5/H4 can be more volatile, and while it might open more trading opportunities, it could also increase drawdowns or risk of false signals. Sequential optimization can help fine-tune it to still be effective.

In summary, using sequential optimization with a +30%/-30% range across timeframes like M30/H4, M15/H4, and M5/H4 is a strong approach to test the adaptability of your strategy. If applied well, you could discover new opportunities on lower timeframes while maintaining a solid performance on H1/H4.

Additional Tip:

• Walk-forward optimization after sequential optimization can further ensure robustness by simulating how the strategy would adapt over time in live markets. This can be especially useful for testing how the strategy would react in different timeframes.