Analysis of the Effectiveness of Adaptive Blocks in the GBPUSD, EURJPY, and NQ Markets

In recent months, we have introduced new comparison blocks, Crosses Above/Below Adaptive and IsGreater/IsLower Adaptive, on our codebase server. These advanced comparative blocks in trading strategies evaluate the historical performance of specific signals before confirming their validity. Unlike traditional blocks that analyze only the current market conditions, adaptive blocks examine past data to verify the statistical reliability of a signal.

The objective of this research is to verify whether generating strategies using adaptive blocks yields better results compared to traditional, non-adaptive blocks. As part of the experiment, we designed a method that allows us to compare thousands of strategies and perform statistical analysis on their performance.

Experiment Methodology:

  • We created 20 custom symmetrical rules for each type of comparative block. (80 in total ) 
  • Four build tasks were prepared, each generating strategies based on different conditions:
    • Crosses Above/Below Base
    • Crosses Above/Below Adaptive
    • IsGreater/IsLower Base
    • IsGreater/IsLower Adaptive

Strategies will be generated au hasard without using genetic optimization, with only one  filter requiring a minimum of two trades per month.

Hypothesis: Strategies using adaptive blocks should demonstrate better performance, with higher average values in the database (net profit, profit factor, drawdown, stability, and the number of trades) compared to strategies using non-adaptive blocks.

Tested Markets and Parameters:

  • Markets: GBPUSD, EURJPY, NQ
  • Platform: MT4
  • Testing Range: January 1, 2010 – December 31, 2024
  • System Types: Intraday (closing positions at the end of the US session) and Intraweekly (closing positions no later than Friday).

The results of this experiment will provide a clearer understanding of the effectiveness of adaptive blocks and their contribution to the automated generation of trading strategies.

You can download the Custom Blocks/Conditions ici.

 

In the image above, you can see the results of the analysis of adaptive blocks compared to normal blocks on 120 thousand trades.

Interpretation of Results:

Better Performance of Adaptive Blocks:

  • Adaptive blocks generally show higher average profit (smaller losses).
  • The profit factor is consistently higher with adaptive blocks, indicating better strategy efficiency.

Lower Drawdown:

  • Adaptive blocks have a lower average drawdown, suggesting better risk management.

Stability:

  • Adaptive blocks demonstrate higher stability (less negative stability values).

Number of Trades:

  • Adaptive blocks generate fewer trades (Avg. NoT), indicating more cautious and likely better-filtered signals.

 

 

Résumé :

  • Adaptive blocks consistently deliver better performance across nearly all tested parameters and markets.
  • Significant improvements in lower drawdown, higher profit factor, and improved stability.
  • Adaptive blocks generate fewer trades, indicating stricter signal filtering and reduced market exposure.

 

Conclusion :

Adaptive blocks prove to be more effective in risk management and achieving better results compared to traditional blocks.Therefore, we will continue to improve such adaptive blocks in the coming months as we aim to demonstrate whether their performance is better compared to traditional blocks.

clonex / Ivan Hudec

Ivan Hudec, known as “Clonex” on the forum, is an experienced algorithmic trader, consultant, and researcher who has been trading for 15 years and using StrategyQuant X (SQX) since 2014. He contributes to the SQX blog and enhances the software by adding new indicators, snippets, and incorporating Python programming for advanced data analysis, machine learning algorithms, and quantitative modeling. Ivan offers his expertise to help others accelerate their trading projects and approach them in innovative ways.

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Tanay Roy
19. 1. 2025 6:41 am

What are the chances that it produce better result by selecting less sample size? Like I have fixed loss $200 and sample size is 400 if I lose every trade, it will be $80000 loss and If I reduce the sample size 200 it will be $40000 loss. So method is not doing anything rather reducing my sample. I just want to know whether the adaptive method can create true edge or not?

Dernière modification le 29 jours il y a par Tanay Roy

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