Wavelet vs FFT for Market Microstructure
FFTs are powerful for stationary signals, but market microstructure demands time-localized transforms. Wavelets preserve locality and reduce latency, making them a better fit for modern trading systems.
FFT Strengths
- • Efficient for stationary frequency analysis.
- • Strong global frequency resolution.
- • Widely supported in legacy toolchains.
Wavelet Strengths
- • Time-frequency localization for regime shifts.
- • Incremental updates for streaming data.
- • Multi-resolution insight across horizons.
Why Microstructure Favors Wavelets
Microstructure signals are transient: liquidity sweeps, spoofing, and regime breaks happen on short time windows. FFTs require fixed windows that can smear these events and introduce latency.
Wavelets update incrementally and retain time-localized detail, enabling you to capture short bursts of activity without losing mid-horizon trend context.
For HFT systems, this means cleaner features with less lag and better alignment to execution windows.
Deploy Wavelets with MorphIQ
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FAQ
Is FFT still useful in trading systems?
Yes. FFTs are effective for stationary signals and batch analysis, but they can introduce latency and windowing artifacts in streaming microstructure data.
Why do wavelets handle microstructure better?
Wavelets preserve time localization and update incrementally, making them more responsive to regime shifts and short-lived liquidity events.
Which MorphIQ library should I use?
VectorWave fits JVM-based pipelines, while IronWave targets Rust-native low-latency execution.