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pyalloq-features

pyalloq-features provides feature engineering transformers, TA-Lib indicator integration, scaling utilities, and feature pipeline orchestration for PyAlloq.

Key Modules

  • BaseFeatureTransformer: Abstract base class for all feature transformation modules operating on MarketData.
  • TechnicalIndicator: Applies TA-Lib technical indicators (e.g., SMA, RSI, MACD, ATR, BBANDS) across asset columns of a 2D DataFrame and appends resulting features to MarketData.features.
  • FeaturePipeline: Sequential feature pipeline builder for chaining feature transformers.
  • scalers: Cross-sectional and time-series feature normalization and standardization utilities.

Quick Example

from pyalloq_features.transformers.technical import TechnicalIndicator
from pyalloq_features.core.pipeline import FeaturePipeline

# Define feature engineering pipeline
pipeline = FeaturePipeline()
pipeline.add(TechnicalIndicator(indicator="RSI", timeperiod=14))
pipeline.add(TechnicalIndicator(indicator="SMA", timeperiod=50))

# Apply transformations directly to MarketData
# updated_data = pipeline.run(market_data)

Metadata

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