The question whether algorithmic trading systems (ATS) can improve human trading in terms of effectiveness is eliciting an increasingly relevant debate among traders and investors, as well as quantitative studies that address this issue through numerical testing [[9]]. In recent years, the discussion regarding whether algorithmic trading systems (ATS) can surpass human traders in terms of efficiency, consistency, and adaptability has gained significant traction in both academic and professional circles. Empirical evidence indicates that algorithmic strategies tend to exhibit superior performance in volatile or declining markets, whereas human-managed funds may retain a relative advantage during upward market trends due to behavioral and intuitive factors [[2]]. Moreover, large-scale behavioral studies reveal that algorithms largely eliminate well-known cognitive biases such as the disposition effect that continue to affect human traders [[23]]. Complementary research has also emphasized the growing integration of artificial intelligence and machine learning methods in modern ATS, which enhances predictive accuracy and execution speed [[7]]. Nonetheless, experimental findings suggest that algorithmic trading may still be constrained by design limitations, challenging the notion of its absolute superiority over human decision-making [[16]]. These findings collectively indicate that algorithmic and human trading approaches might be best viewed as complementary, each offering unique strengths under different market conditions.

New breakthroughs in AI make the headlines everyday. Far from the buzz of customer-facing businesses, the wide adoption and powerful applications of Machine Learning in Finance are less well known. In fact, there are few domains with as much historical, clean and structured data as the financial industry -- making it one of those predestined use cases where'learning machines' made an early mark with tremendous success that still continues. About three years ago, I got involved in developing Machine Learning (ML) models for price predictions and algorithmic trading in Energy markets, specifically for the European market of Carbon emission certificates. In this article, I want to share some of the learnings, approaches and insights which I have found relevant in all my ML projects since.

As you may know, the Foreign Exchange (Forex, or FX) market is used for trading between currency pairs. But you might not be aware that it's the most liquid market in the world. A few years ago, driven by my curiosity, I took my first steps into the world of Forex algorithmic trading by creating a demo account and playing out simulations (with fake money) on the Meta Trader 4 trading platform. After a week of'trading', I'd almost doubled my money. Spurred on by my own successful algorithmic trading, I dug deeper and eventually signed up for a number of FX forums.

A few years ago, driven by my curiosity, I took my first steps into the world of Forex trading algorithms by creating a demo account and playing out simulations (with fake money) on the Meta Trader 4 trading platform. After a week of'trading', I'd almost doubled my money. Spurred on by my own success, I dug deeper and eventually signed up for a number of forums. Soon, I was spending hours reading about algorithmic trading systems (rule sets that determine whether you should buy or sell), custom indicators, market moods, and more. Around this time, coincidentally, I heard that someone was trying to find a software developer to automate a simple trading system.