advanced-math-trading/signal-processing-features

Denoising financial time-series applies wavelets, Kalman filtering, or empirical mode decomposition (EMD) to remove noise while preserving price movements. Select a technique matching your data characteristics—Kalman for linear trends, wavelets for multi-scale features, EMD for nonlinear patterns—then apply it through the Skill's modular interface to extract clean signals for modeling.

Wavelets, SSA (singular spectrum analysis), Kalman filtering, and spectral transforms each extract different signal properties: wavelets capture frequency-time localization, SSA decomposes trend and noise, Kalman tracks hidden state, spectral methods isolate periodic components. Combine detrending, scaling, and orthogonalization steps to engineer robust, stable features for prediction.

Yes. Apply Kalman filtering first to denoise and track hidden state in price series, then use wavelets to decompose the filtered signal into multi-scale components. This combined approach balances adaptive noise reduction with frequency-domain feature extraction, improving signal clarity and feature orthogonality for downstream models.

Financial signal processing requires scaling before transforms to prevent overflow, orthogonalization to reduce multicollinearity in engineered features, and reproducible random initialization. The Skill includes built-in numerical-stability checks across wavelets, EMD, Kalman, and SSA to ensure consistent results across runs and prevent precision loss in high-frequency or volatile data.

Combine spectral transforms or wavelets to isolate volatility components across frequencies, then scale and orthogonalize outputs to create independent features. Kalman filtering can track time-varying volatility directly. Use the Skill's detrending and feature engineering steps to normalize and prepare these volatility signals for machine learning models.

Use EMD for highly nonlinear price movements without assuming fixed frequency basis; use wavelets for multi-scale periodic patterns and faster computation. EMD adapts to data structure but requires more computation; wavelets offer speed and interpretability. Test both via the Skill's on-demand loading to match your signal characteristics and latency constraints.