DNYL . SPACE

ah yes i added these filters already in all step;

Profit factor (IS) > 1.4

Ret/DD ratio (full) > 4

Stability (is) > 0.6

open dd % (is) < 50%

cagr/max dd % (is) > 0.6

sharp ratio (is) > 1.5

max dd % (OOs) < 70%

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

Review of Applied Filters:

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

Next Steps for Final Review:

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

Summary:

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

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.