
How QuantMonitor Achieved DarwinIA GOLD on Darwinex Zero
Recently, one of our trading portfolios, SRBT, reached Darwinex Gold on Darwinex Zero—an overnight success that actually took us two years of hard work! 😊 Portfolio equity at the time …
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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:
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:
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.
Better Performance of Adaptive Blocks:
Lower Drawdown:
Stability:
Number of Trades:
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.
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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?
Thank you for your response. I understand the argument. The key point I would emphasize is that we live in a non-stationary market where edges appear and disappear. There can be periods when they work and periods when they don’t. This tool is specifically designed to help adapt to such situations.