30/09/2026
👨💻 Machine learning does not necessarily have to predict prices. In practice, it is often more effective at solving narrower tasks, such as:
🔹estimating the probability of price movements
🔹filtering out weak signals
🔹ranking trading opportunities
🔹identifying the current market regime
📊 Common models include Logistic Regression, Random Forest, Gradient Boosting, and XGBoost. Their strengths include robustness, interpretability, and the ability to work effectively with relatively small datasets.
🧩 In practice, however, feature quality often matters more than model complexity. Common examples include returns, volatility, trading volumes, momentum, spreads, deviations from moving averages, and macroeconomic factors.
❗ It is essential to test a model using only the information available at the time each trading decision was made. Otherwise, look-ahead bias can occur.
🔄 Markets evolve, so models require regular retraining and ongoing monitoring for performance degradation.
🧠 The real value of machine learning lies not in the “magic” of prediction, but in improving trading logic: filtering out weaker entry signals, adapting to changing volatility, and managing risk more effectively.
Trading involves risks. This material is for informational purposes only and does not constitute investment advice.
