DNYL . SPACE

A. Building…

Create 10 strategies using Genetic Evolution.
1.1 Breakout Strategy
1.2 Day Trading Strategy
1.3 Grid Trading
1.4 Multi-Timeframe Divergence Strategy
1.5 News Trading
1.6 Oscillator Reversion and Momentum Burst Strategy
1.7 Range Trading Strategy
1.8 Scalping Strategy
1.9 Swing Trading
1.10 Trend Trading Strategy

Use TF H1/H4 – Opened Timeframe – Ranking Profit > 1.4 Ret/DD > 4 Min trade per month > 2 – No Cross check / Robustness test
** Market Entry Method: Enter at market / Reverse / Stop / Limit pending
*** Filter each step by 10 entries per market entry method, totaling 40 in one main group, and a total of 400.
**** Enable Exit types: MoveSL2BE / SL2BE add pips / Profit target / Stop loss / Trailing stop / Trailing activation / Exit rules
***** Trading option: Friday Exit 23:00
****** Money management: Risk fixed % of account

B. Melting…

Backtest all 400 results against 10 currency pairs, separating the timeframe into 5 TFs.
2.1 H1/H4 – M30/H4 – M15/H1 – M5/H1 – M15/M30
2.2 Aggregate the results and perform Automatic Retest by changing the Precision to 1-minute data tick simulation, but maintain the original trading settings.
2.3 Add Monte Carlo trades manipulation and Sequential optimization testing.
2.4 Save the results to the @Blender folder.
2.5 Perform another Automatic Retest using only the Walk-Forward Matrix.
2.6 Save the results to the @Melted folder.

C. Ending…

Load all files from @Blender into the Initial Population to create a new set of EA, repeating the process 10 times (10 currency pairs).
3.1 Create 3 new steps: Build Martingale / ATM / Everyday Exit
3.2 Automatic Retest with the original trading settings at 3 timeframes (H1/H4 – M30/H4 – M15/H1), using 2 Monte Carlo factors (trades manipulation and retest methods) and Sequential optimization.
3.3 Another Automatic Retest with 1-minute data tick simulation, using 2 Monte Carlo factors and Sequential optimization.
3.4 Another Automatic Retest using the Walk-Forward Matrix.

Your current step-by-step process has both advantages and disadvantages to consider:

Advantages

Strategy diversity: Creating 20 strategies allows for greater trading diversity, increasing the chance of finding effective strategies in different market conditions.
Comprehensive testing: Backtesting with multiple currency pairs and different timeframes provides a comprehensive view of the strategy’s performance in various conditions, making the results more reliable.
Monte Carlo and Walk-Forward Testing: Increases confidence in the robustness of the strategy, as these tests simulate unexpected situations and market fluctuations.

Disadvantages

Time-consuming: Complex steps like backtesting and parameter adjustments for each strategy require significant processing time, which may hinder strategy development and improvement within limited time.
High resource consumption: Testing multiple iterations and multiple timeframes requires high computer resources, potentially causing delays on lower-performance machines.
Complexity of the process: The diverse and complex steps can make result analysis challenging and may increase the possibility of errors during processing.

Evaluation

Positive results if you can efficiently manage processing time and resources, and the results from comprehensive testing can be used to improve strategy effectiveness.
Negative aspects if the long processing time and high resource requirements prevent you from making timely improvements or developing, or causing the strategy to be delayed in responding to changing market conditions.

You can assess which steps take too long and consider reducing or simplifying those steps to improve overall efficiency.

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.

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

Setup/Parameters in SQX:

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

Ranking Criteria in SQX:

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

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

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

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

Why Rank with Full Data:

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

Focusing on Good OOS Performance:

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

Recommended Approach:

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

Conclusion:

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

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

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

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

2. Profit Factor (Key Profitability Measure)

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

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

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

4. Open DD% (Drawdown on Open Trades)

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

5. Stability (Smoothness of Equity Curve)

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

6. Win/Loss Ratio (Secondary to Profitability)

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

7. Fitness (Initial Strategy Validation)

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

8. Open Drawdown % (Open DD%)

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

9. Max Drawdown % (Total Drawdown)

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

Summary of Classification Ranges:

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

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

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

1. Prioritize Top-Performing Strategies:

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

2. Create High-Performance Portfolios:

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

3. Stress Test and Validation:

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

4. Evaluate Long-Term Performance:

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

5. Prepare for Live Trading:

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

6. Performance Monitoring:

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

Benefits of Strategies That Exceed Your Metrics:

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

Final Thoughts:

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

Profit factor (IS) > 1.4

Ret/DD ratio (full) > 4

Stability (is) > 0.6

open dd % (is) < 50%

cagr/max dd % (is) > 0.6

sharp ratio (is) > 1.5

max dd % (OOs) < 70%

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

Optimizing the Filters for Portfolio Selection:

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

Tips to Maximize Each Metric:

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

Suggested Ranking and Prioritization:

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

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

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

Steps for Using Filters to Create a Portfolio:

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

Benefits of Using These Filters for Portfolio Creation:

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

Final Tips:

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

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

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.

data precision whould be Selected timeframe only , 1 minute data tick or Real tick ? this is my step now running;

1. Autometic Retest H1/H4 M30/H4 M15/H4 M5/H4 with Selected TF only + Monte and Sequential Optimization Up30% Down30% 10 steps so apply to Strs too

2. do it again like 1. but using 1 minutes data tick simulation

3. No monte i do only Walk-Forward metrix with Real tick data

Your multi-step approach for testing and adapting your strategies across different timeframes and data precision is quite comprehensive. Here’s an evaluation of each step and recommendations:

Step-by-Step Review:

1. Automatic Retest on H1/H4, M30/H4, M15/H4, and M5/H4 with Selected TF Only + Monte Carlo and Sequential Optimization (+/- 30% with 10 Steps):
   • Purpose: This step provides a broad retest and optimization across multiple timeframes, which helps identify how strategies perform under different market conditions and timeframes.
   • Selected TF Only Data:
     • Pros: This approach is faster and sufficient for a high-level overview and preliminary evaluation.
     • Cons: It lacks the detail that comes with tick data, so some precision in backtesting results might be lost, especially for strategies sensitive to intrabar movements.
   • Monte Carlo Simulation: Including Monte Carlo helps test for robustness by simulating different scenarios and possible variations in data. Sequential optimization ensures the strategy is resilient with parameter shifts.

   Evaluation: This step is great for filtering strategies that can adapt well across different timeframes. It provides a solid foundation to identify which strategies are worth deeper analysis.

2. Retesting Using 1-Minute Data Tick Simulation:
   • Purpose: This is a more precise retest using minute data tick simulation, allowing for more accurate representation of price movements within bars.
   • Pros: Provides better data granularity and captures some intrabar price variations, leading to more accurate backtesting results.
   • Cons: It may still miss very fine price changes that could occur with real tick data but is a good balance between precision and speed.
   • Evaluation: This step improves precision and ensures that strategies behaving well in the broader retest (Step 1) are still reliable when checked with higher-resolution data.
3. Walk-Forward Matrix Using Real Tick Data (No Monte Carlo):
   • Purpose: The most detailed step, using real tick data to provide a highly accurate backtest. The Walk-Forward Matrix evaluates strategies in segments, testing out-of-sample performance and ensuring strategies can adapt in live trading conditions.
   • Pros: Real tick data provides the highest level of precision, simulating actual trading scenarios with exact price movements. The Walk-Forward Matrix ensures that strategies remain robust when exposed to new, unseen data.
   • Cons: This step is resource-intensive and takes more time to complete.
   • Evaluation: This is an essential step for final validation. Strategies that pass this phase with real tick data and Walk-Forward Matrix analysis can be considered highly reliable for live trading.

Recommendations and Thoughts:

• Balance Between Speed and Precision: Your approach moves from a broader, faster retest (Selected TF only) to higher-precision testing (1-minute data tick and real tick data). This progression is logical and balances speed with detail.
• Robustness Testing: The inclusion of Monte Carlo in Step 1 is excellent for robustness. Since you’re using the Walk-Forward Matrix in Step 3, you don’t need Monte Carlo there, as the matrix itself tests out-of-sample performance and strategy adaptability.
• Selected Timeframe Only vs. Tick Data:
  • Selected TF only data is suitable for a quick, broad analysis but may miss nuances of price movement.
  • 1-minute data tick is a good middle ground for more accurate backtesting without the long processing times of real tick data.
  • Real tick data is essential for the most accurate results, particularly for high-frequency or intraday strategies.

Final Thoughts:

Your planned approach is comprehensive and well-structured. The progression from broad retests and optimizations to detailed Walk-Forward testing with real tick data ensures that the strategies you finalize are robust, reliable, and suited for live trading.

If possible, after Step 3, run a final review to compare the performance consistency across all three steps to make sure the strategies chosen maintain their robustness under different testing conditions. This will help identify truly versatile strategies.

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.

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.