Discussing the article: "Forex arbitrage trading: Analyzing synthetic currencies movements and their mean reversion"
Large outliers are obtained on weekends, maybe it makes sense to exclude them from the overall statistics
It's like the text was written by an AI: so much water.
Good night! I apologise for the water, it's just that this time there were very few concrete conclusions for this article, but this is an introductory one so to speak - in the next part I will analyse different formulas for obtaining synthetics, and publish a robot for working on forks, which works perfectly on netting accounts with limit orders.
After the calculate_synthetic_rate function, I am somewhat perplexed - close prices are Bid, and when searching for arbitrage opportunities, one should take into account the direction of trades and use ask/bid in different combinations.
Yes, it is true, but the Python script was needed only for general evaluation, close was taken only for the sake of code execution speed - and the main bots in MQL5 work naturally on ticks, and ticks are combined into batches, so that anomalies do not affect the work.
thank you very much for the articles. Interesting trading approach on statistics - I am studying the topic and content.....
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Check out the new article: Forex arbitrage trading: Analyzing synthetic currencies movements and their mean reversion.
What happens when math meets market reality? Anomalies arise – tiny, almost invisible, but incredibly valuable to those who know where to look. Such anomalies are rarely noticed by major players, whose algorithms are tuned to large-scale movements and macroeconomic events. They are too busy hunting big game to notice the gold nuggets right under their feet.
In the world of high-frequency trading, it is not the fastest who survives, but the most attentive. Someone who sees patterns where others see only noise. In recent years, technology has made markets more efficient, but paradoxically, this has created new niches for smart scalping. When milliseconds matter, even giant algorithmic systems leave traces of their activity — microscopic imbalances that a skilled trader can turn into systematic profits.
Our research began with a simple question: do cross rates really always match their calculated values? Theory says "yes". Practice whispers "not quite". And within this "not quite" lies a whole world of opportunity for those armed with the right tools and methodology.
Author: Yevgeniy Koshtenko