Using Simplified Wavelet Filters to Smooth Prices and Identify Trends

Summary

This article introduces wavelet transforms as localized, multi-scale filters for financial price series. It explains how wavelet basis functions and their coefficients can separate low-frequency movement from faster fluctuations, then describes a simplified approach that applies coefficient-weighted convolution to smooth prices. Comparing consecutive smoothed closing values provides a basic directional signal: rising values imply a long bias and falling values a short bias. The article also outlines an example workflow using hourly Bitcoin futures data and shows how the resulting signal could drive position changes.

The presentation is educational and oriented toward rapid prototyping. It omits full multi-level decomposition, threshold denoising, inverse reconstruction, and careful boundary handling, so its convolution should not be treated as a rigorous implementation of complete wavelet analysis. It offers no reported performance results or evidence that the example is profitable. The article’s claims about simplified processing and trading usefulness are not supported by a systematic backtest, and practical use would require validation, realistic costs, and attention to signal lag and risk.

Key ideas

• Wavelet filters use localized basis functions to represent price behavior at different time scales.
• The article proposes smoothing prices with coefficient-weighted convolution to retain slower movements.
• Comparing adjacent smoothed prices creates a simple long-or-short trend signal.
• The example deliberately omits standard wavelet steps, including thresholding and boundary treatment.
• The document provides no measured strategy results, so the approach requires independent testing.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.