advanced-math-trading/signal-processing-features

What problem does it solve?

This Skill provides a modular collection of signal processing techniques and math-driven feature engineering methods tailored for financial data, enabling robust preprocessing, denoising, and feature construction for modeling.

Core Features & Use Cases

• Techniques: Wavelets/EMD/Kalman/SSA, spectral transforms for noise reduction and feature extraction.
• Feature Engineering: Detrending, scaling, orthogonalization, and numerical stability tips.
• Use Case: Prepare a time-series of financial prices to create volatility-informed features for a predictive model.

Quick Start

Select a technique (e.g., Kalman filtering) and consult the navigation on demand in the knowledge base sections to apply it to your data.