Continuous Wavelet Transform — time-frequency analysis of QQQ

Continuous Wavelet Transform — time-frequency analysis of QQQ

Applies CWT to QQQ to analyze frequency-domain dynamics over time. Unlike FFT (fixed window), wavelets capture how dominant frequencies shift across regimes. Reveals the market's changing 'heartbeat'.

• CWT captures how frequencies shift over time • Superior to FFT for non-stationary data • Reveals regime-dependent dominant cycles

Python Continuous Wavelet Transform, Scalogram, Time-Frequency Analysis

Non-stationary signal processing on $QQQ. 🧠 Price action only tells half the story. To understand the market’s heartbeat, you have to look at the frequency domain. ​This is a Continuous Wavelet Transform (CWT) applied to QQQ. Unlike a standard Fourier Transform, this lets us see when specific market cycles are active over time. Want the source code to build this? 👇 Comment "wavelet" below and I’ll DM you the repo link. ​The Alpha: Watch the "Dark Zones." You’ll notice a distinct pattern: When QQQ grinds down to its lows, the heatmap turns dark. This represents a drop in spectral energy—a moment of "silence" in the frequency spectrum where momentum dissipates. This lack of energy is often a clearer signal of an oversold bottom than price alone. ​Stop looking at just candles. Start look…