Feature Engineering for Time-Series Data

Feature engineering is often the most impactful step in a machine learning pipeline, especially for financial time-series data. Good features encode domain knowledge in a form that models can use effectively.

Why Feature Engineering Matters

Raw price data is rarely informative on its own. Feature engineering transforms raw data into representations that capture meaningful patterns — momentum, volatility, mean reversion, and other phenomena that have been studied in financial research.

Common Feature Categories

Price-Based Features - Returns over various lookback periods - Moving averages and crossovers - Bollinger Bands and volatility measures - Support and resistance levels

Volume Features - Volume-weighted average price (VWAP) - On-balance volume - Volume relative to moving average

Statistical Features - Rolling standard deviation - Skewness and kurtosis of return distributions - Autocorrelation measures - Hurst exponent estimates

Technical Indicators - RSI, MACD, Stochastic oscillators - Average True Range (ATR) - Various momentum indicators

Best Practices

1. Ensure features are causal — no lookahead bias
2. Normalize features appropriately for the model being used
3. Test feature importance and remove redundant features
4. Use domain knowledge to guide feature creation
5. Be aware of the curse of dimensionality