🤖 ML Models
ML - Feature Engineering for Crypto
The process of transforming raw price/volume data into meaningful inputs for ML models. In finance, features are the "signals" the model uses to make predictions. Feature engineering is widely considered more important than model choice.
2
Minutes
3
Concepts
+15+30
Read+Quiz
1
Signal Forge's 27 Features (current)
Returns (4 features)
return_1h, return_4h, return_24h, return_7d What: Price change over different lookback windows Why: Captures momentum at multiple timescales
Volatility (2 features)
volatility_24h, volatility_7d What: Rolling standard deviation of returns Why: High volatility = uncertain, low volatility = calm (often precedes big moves)
Volume (2 features)
volume_change_24h, volume_sma_ratio What: Volume relative to recent history Why: Volume confirms price moves — big move + big volume = real, big move + low volume = suspect
Technical Indicators (5 features)
rsi_14 Relative Strength Index (overbought/oversold, 0-100) macd Moving Average Convergence Divergence (trend strength) macd_signal MACD signal line (crossover = trend change) bb_upper Bollinger Band upper (price at 2 std devs above mean) bb_lower Bollinger Band lower (price at 2 std devs below mean)
Crypto-Specific (3 features)
funding_rate_proxy Estimated from OHLC spread (basis between spot and implied future) volatility_regime Categorical: low/normal/high/crisis btc_correlation Rolling 30-day correlation with BTC (altcoins only)
Time Features (2 features)
hour_sin, hour_cos Cyclical encoding of hour-of-day Why: Crypto has intraday patterns (Asian/European/US session effects)