feature-engineeringlisted

# Feature Engineering for Trading ML Feature engineering is the single highest-leverage activity in building ML trading models. Model selection (XGBoost vs. neural net vs. logistic regression) matters far less than the quality and diversity of input features. A simple model on great features will outperform a complex model on raw prices every time. This skill covers constructing, validating, and selecting features from market data for use in classification (signal-classification) and regression models targeting crypto/Solana token trading. ## Why Features Beat Models Raw OHLCV data is non-stationary, noisy, and high-dimensional. Models trained directly on price series will overfit. Feature engineering transforms raw data into stationary, informative signals that capture distinct aspects of market behavior: - **Compression**: Reduce thousands of price bars to dozens of descriptive statistics - **Stationarity**: Convert non-stationary prices into stationary returns and ratios - **Domain knowledge**: Encode trader intuition (support/resistance, volume climax) as computable quantities - **Regime awareness**: Features that behave differently in trending vs. ranging markets help models adapt ## Feature Categories ### 1. Price Features Derived purely from OHLCV price columns. These capture trend, momentum, and volatility from the price series itself. | Feature | Formula | Lookback | |---------|---------|----------| | `log_return` | `ln(close_t / close_{t-1})` | 1 bar |