signal-processing

What problem does it solve?

Financial data is often contaminated with noise, making it hard to identify reliable signals. This skill provides a structured set of methods to denoise, decompose, and interpret financial time series, enabling robust indicators from price data.

Core Features & Use Cases

• Fourier transforms for cycle detection and spectral analysis to identify dominant frequencies in financial data.
• Wavelet and EMD based multi-scale decomposition to extract multi-timeframe signals (trend, swing cycles, and noise) with denoising options.
• Practical denoising and filtering techniques (HP filter, moving averages, Savitzky–Golay) with guidance on lag and phase considerations.
• Use Case: Decompose a daily price series to isolate the long-run trend, mid-term cycles, and high-frequency noise for strategy signals.

Quick Start

Instruct the agent to perform a multi-scale signal analysis on a price series to extract trend and cycles.