advanced-feature-engineering

What problem does it solve?

This Skill generates high-quality features for financial machine learning models by addressing data non-stationarity and preventing look-ahead bias during standardization.

Core Features & Use Cases

• Fractional Differentiation: Stabilizes time series data while preserving historical memory, crucial for financial modeling.
• Look-ahead Bias Free Rolling Normalization: Ensures that feature scaling at any point in time only uses past data, maintaining data integrity.
• Use Case: Prepare stock price data for a predictive model by applying fractional differentiation to achieve stationarity and then standardizing it using a rolling window to avoid look-ahead bias.

Quick Start

Use the advanced-feature-engineering skill to generate features for the provided financial time series data.