Wavelet transform is a powerful signal processing technique used in quantitative finance to filter market noise and extract clear trading trends from raw price data. Unlike traditional Fourier transforms that assume market periodicity, wavelets excel at localization, identifying trends within specific time windows. This article introduces a practical, trading-focused approach, simplifying complex theory into a direct convolution method for rapid strategy development.

It details seven common wavelet types—from the simple Haar wavelet to the turning-point-focused Mexican Hat—demonstrating how each applies different weights to historical prices. The analysis shows that more coefficients result in stronger smoothing but introduce significant lag. By adjusting the 'smoothing level,' traders can match the wavelet's responsiveness to their specific holding period, whether for intraday swings or long-term trend following.

The core trading signal is derived from the slope of the wavelet-smoothed price curve, offering a more reliable basis for entry and exit decisions than reacting to raw candlestick volatility.