What machine learning adds
Machine learning can help traders model complex relationships between features such as momentum, volatility, liquidity and broader market conditions. It is especially useful when a trader wants to test many variables and interactions systematically.
The goal is not always to predict the next price move perfectly. In many cases, the model is used to rank opportunities or improve the quality of a trading signal.
The challenge of overfitting
A major risk in machine learning trading is fitting a model too closely to historical noise. The result can look impressive in sample but fail dramatically when the market regime changes.
This is why robust backtesting, out-of-sample checks and sensible feature selection remain essential parts of the process.
Why structure matters
Strong ML-driven strategies still need a disciplined framework: clear objectives, risk constraints, a rational feature set and a process for improving models without over-optimizing.
Machine learning is powerful, but it works best when it sits inside a broader systematic trading design. For the distinction between a strategy, its model component, and related trading concepts, see Trading Strategies: A Beginner’s Guide.