Crypto Data Feature Engineering System Development
Feature engineering process of creating informative features from raw data for ML models. In crypto trading this is critical stage: correct features can double model quality. System automates feature creation, validation and selection.
Feature categories:
Price-based: multi-period returns, rolling statistics, price position in range, distance from moving averages.
Volume: volume ratios, volume-price relationship, OBV, ATR, Money Flow Index.
Technical indicators: RSI, MACD, Bollinger Bands, ADX, Stochastic.
Market microstructure: bid-ask spread, order flow imbalance, funding rate, open interest changes.
Cross-asset: correlated assets returns, rolling correlation with target.
Feature validation: check for look-ahead bias, missing values, correlation with target, stationarity, variance.
Feature selection: Mutual Information for nonlinear dependency, SHAP importance from baseline model, correlation filtering > 0.95, VIF for multicollinearity.
Feature Store architecture: centralized Feature Store with versioning. Raw data → Feature pipelines → Feature Store → Online (Redis) and Offline (Parquet) stores.
Develop Feature Engineering system with 100+ automatically computed features, look-ahead bias validation, feature selection, Feature Store for centralized storage and versioning.