Crypto Price Prediction ML Model Training
Predicting crypto prices using machine learning is one of the most complex tasks in quant finance. Market is adaptive: pattern that worked yesterday may be arbitraged away tomorrow. However, properly built models give statistical edge on few-hour horizon.
Regression vs classification: predicting exact price (regression) is much harder than predicting direction (classification). For trading usually classification used: "will price rise >0.5% in next 4h?"
Target engineering: proper target is critical:
- Forward return: (price[t+n] - price[t]) / price[t]
- Binary direction: sign(forward_return)
- Tercile classification: buy (top 33%), hold, sell (bottom 33%)
Feature Engineering:
Price-based features: returns for different periods, technical indicators (RSI, MACD, Bollinger Bands), volume features, volatility measures.
On-chain features (for BTC/ETH): exchange inflow/outflow, active addresses, hash rate, NVT ratio, SOPR, NUPL.
Market microstructure: bid-ask spread, order book imbalance, funding rate, open interest changes.
Critical aspects:
- Look-ahead bias: features must be calculated from information available at time t only
- Walk-forward validation: mandatory for time series
- Purging and embargoing: clean train set from overlapping labels
Feature selection: SHAP values for interpretability, correlation filtering, VIF for multicollinearity.
Develop full pipeline: feature engineering, train multiple models, walk-forward validation, SHAP interpretation, production API for realtime predictions and MLflow tracking.