Discussing the article: "Self Optimizing Expert Advisors in MQL5 (Part 15): Linear System Identification"
You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
Check out the new article: Self Optimizing Expert Advisors in MQL5 (Part 15): Linear System Identification.
Trading strategies may be challenging to improve because we often don’t fully understand what the strategy is doing wrong. In this discussion, we introduce linear system identification, a branch of control theory. Linear feedback systems can learn from data to identify a system’s errors and guide its behavior toward intended outcomes. While these methods may not provide fully interpretable explanations, they are far more valuable than having no control system at all. Let’s explore linear system identification and observe how it may help us as algorithmic traders to maintain control over our trading applications.
Simple strategies may appear reliable during calm market conditions, yet both simple and complex systems often fail when volatility increases. Despite this, the fields of control theory and signal processing appear underutilized in addressing these challenges. Control theory, devoted to maintaining stability in dynamic and uncertain systems, aligns closely with the problems our community of algorithmic traders face daily.
Classical control theory assumes a first-principles understanding of the system — explicit formulas describing the relationship between inputs and outputs. Modern financial markets, however, defy such clear mathematical structure. This has led to growing interest in integrating control theory with machine learning, which can approximate these relationships directly from data rather than relying on explicit equations.
This concept is powerful: even without knowing the precise control equations, practitioners can still learn to regulate system behavior from data. Control theory and algorithmic trading share the same goal — managing uncertainty while maintaining stability. A feedback controller does not predict prices; it regulates system responses, suppressing overreactions to noise and ensuring steady performance.
Feedback controllers also improve capital efficiency by learning when capital is being deployed effectively and reducing unnecessary trades. When combined with machine learning, these systems gain the ability to adapt autonomously, enhancing precision, control, and reliability. Despite the clear overlap, a significant research gap remains between control theory and algorithmic trading — a gap rich with potential.
Author: Gamuchirai Zororo Ndawana