How AI "Learns" a Trading Pattern
Machine learning models don't just see a line on a chart; they see a mathematical sequence. The process generally follows this pipeline:
Feature Engineering: Raw price data is converted into "features" like RSI (Relative Strength Index) Moving Average Crossovers, or even "Sentiment Scores" derived from news headlines.
Pattern Recognition:
Supervised Learning: The model is shown thousands of past "Bullish Flag" patterns and told, "This led to a 2% gain." It learns to map these specific inputs to that outcome.
Unsupervised Learning: The model looks at raw data without labels to find "clusters" of behavior that humans haven't named yet, identifying hidden regimes in market volatility.
Backtesting & Optimization: The AI "trades" on historical data it hasn't seen before to prove the pattern is statistically significant and not just a fluke (overfitting).