Discussing the article: "Overcoming The Limitation of Machine Learning (Part 2): Lack of Reproducibility"
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Check out the new article: Overcoming The Limitation of Machine Learning (Part 2): Lack of Reproducibility.
The article explores why trading results can differ significantly between brokers, even when using the same strategy and financial symbol, due to decentralized pricing and data discrepancies. The piece helps MQL5 developers understand why their products may receive mixed reviews on the MQL5 Marketplace, and urges developers to tailor their approaches to specific brokers to ensure transparent and reproducible outcomes. This could grow to become an important domain-bound best practice that will serve our community well if the practice were to be widely adopted.
For this discussion, I randomly selected two brokers I, personally, use for independent trading. In line with our community guidelines, which prohibit broker promotion, their names have been redacted and replaced with “Broker A” and “Broker B.”
Using the MetaTrader 5 Python library, I requested four years of daily historical EURUSD data from both brokers. Upon review, I noticed that the timestamps didn’t align: one broker’s data extended back to September 2019, while the other’s only reached August 2020. Nevertheless, both returned exactly 1,460 rows of daily data, correctly fulfilling our request.
Given the decentralized nature of brokers, it’s expected that their operating time zones may differ. Less obvious, however, are the effects of daylight saving time, recognized public holidays, and other subtle discrepancies, all of which can further skew timestamp alignment.
We then calculated the 10-Day EURUSD return on both brokers and found that the numerical properties of the EURUSD symbol were inconsistent with each other. The average 10-Day EURUSD return with Broker A was 0.000267 while with Broker B, the average 10-Day return was -0.000352. This represents a difference of about 232% in the expected return of the same underlying asset.
To make matters worse, it appears that the expected returns from Broker A carry 21% more risk than the expected returns from Broker B. This was suggested to us by the fact that the variance in returns between Brokers grew by the same amount, 21%.
Author: Gamuchirai Zororo Ndawana