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Feature Engineering for Trading: 200+ Indicators That Actually Matter

April 1, 202614 min read
PythonML/AITradingFeature Engineeringpandasscikit-learn
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AlphaStream computes 200+ technical indicators for every security it analyzes. But most of them are noise. The hard part isn't computing indicators — it's selecting the ones that actually predict future price movement.

The Indicator Categories

I organize indicators into 6 groups:

Trend Indicators (40+): Moving averages (SMA, EMA, WMA, DEMA, TEMA), ADX, Aroon, Ichimoku, Parabolic SAR, SuperTrend. These tell you the direction.

Momentum Indicators (35+): RSI, MACD, Stochastic, Williams %R, CCI, ROC, MFI, Ultimate Oscillator. These tell you the strength.

Volatility Indicators (25+): Bollinger Bands, ATR, Keltner Channels, Donchian Channels, Standard Deviation, Historical Volatility. These tell you the risk.

Volume Indicators (20+): OBV, VWAP, A/D Line, CMF, Force Index, Volume Profile. These tell you the conviction.

Statistical Indicators (30+): Z-Score, Skewness, Kurtosis, Hurst Exponent, Autocorrelation, Cointegration scores. These tell you the regime.

Custom/Engineered (50+): Cross-timeframe features, lag features, rolling statistics, regime indicators. These are where the alpha lives.

The Feature Selection Problem

200+ features with daily data creates a classic p >> n problem. More features than useful data points means overfitting.

My approach:

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Jason Teixeira
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Jason Teixeira
Founder, Sage Ideas Studio
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livebuild 29be8ec2026-06-11 06:38Z
// solo studio// no analytics resold// every commit human-reviewed