What Is Machine Learning Trying to Learn in Markets?
Machine learning (ML) estimates relationships between inputs—features such as returns, volume ratios, volatility, spreads, or text embeddings—and targets such as “next-bar direction,” “setup success within N bars,” or “relative outperformance.” Supervised learning dominates retail-facing tools: labels come from past outcomes. Unsupervised methods cluster regimes or discover anomalous days. Reinforcement approaches optimize sequential decisions but are harder to validate safely. Regardless of family, the model is a compressed history of correlations, not a crystal ball.
Write the prediction target in plain language before training; vague targets produce vague edges.
Which Features Matter Most for Trading Models?
Useful features are stable, available at decision time, and economically motivated. Relative volume, distance to VWAP or moving averages, intraday range position, and liquidity metrics often matter more than exotic indicators stacked for curve beauty. Leakage—using information that would not have been known at entry—creates fake accuracy. Session effects and corporate events must be handled carefully. Feature volume is not virtue; ten correlated RSI variants add little once one momentum measure exists.
Audit features for look-ahead bias the way you would audit a backtest’s entry fill assumptions.
How Should Machine Learning Results Be Evaluated?
Accuracy alone misleads when classes are imbalanced. Prefer expectancy, profit factor after costs, drawdown path, and stability across years and volatility regimes. Use walk-forward or purged cross-validation so adjacent bars do not leak into training. Separate development from a locked holdout. Compare against a simple baseline—buy relative strength, fade extremes—so complexity must earn its keep. If a model only beats chance on the training window, it is unfinished research.
Publish failure modes: when does the model systematically lose, and do you reduce size then?
How Do Traders Use ML Outputs Without Overfitting Process?
Typical workflows treat ML scores as ranking layers: trade only A-grade candidates that also pass chart structure and news checks. Some use models to size confidence within a risk budget—never to remove stops. Others run ML as a veto: skip signals the model flags as historically poor in similar conditions. Changing parameters after every losing week recreates curve-fitting in the human loop. Freeze rules for a defined sample period, then revise from aggregated journals.
One frozen scoring policy beats weekly retuning that invalidates all prior experience.
What Risks Are Specific to ML Trading Systems?
Regime shift breaks correlations overnight. Crowding appears when many models share similar features. Opaque deep networks resist diagnosis when performance collapses. Sparse big winners can dominate metrics while most days lose. Mitigation includes simpler models when possible, continuous monitoring of live versus research expectancy, hard risk caps, and diversification across uncorrelated signal families. Machine learning is a research workflow with production constraints—not a set-and-forget indicator pack.
If you cannot explain why a high score appears today, lower size until understanding catches up.