Wavelet Transforms for High-Frequency Trading
Wavelet analytics capture microstructure shifts and regime changes without the lag of traditional filters, enabling faster decisions in HFT execution pipelines.
Latency-First Signal Extraction
Streaming wavelet transforms isolate meaningful movement in nanoseconds, keeping feature pipelines aligned with execution windows.
Multi-Scale Market Structure
Separate noise, liquidity sweeps, and momentum shifts across multiple time horizons without re-sampling.
Robust Regime Detection
Detect regime breaks earlier with frequency-aware features that respond to volatility compression and expansion.
What Matters in HFT Signal Processing
HFT systems require consistent latency, deterministic memory behavior, and streaming analytics that do not depend on batch windows. Wavelet transforms provide time-frequency localization, enabling transforms to update incrementally as each tick arrives.
Traditional FFT pipelines introduce windowing artifacts and latency that conflict with nanosecond execution. Wavelets preserve locality, giving you cleaner short-horizon signals without sacrificing mid-horizon context.
The result is a signal stack that scales from microstructure detection to higher-level regime classification using a single unified transform family.
Deploy with MorphIQ Libraries
Related Guides
FAQ
Do wavelet transforms add latency?
Streaming wavelet transforms update incrementally and can operate at nanosecond scale with SIMD acceleration.
Which library should I choose for HFT?
VectorWave suits JVM-based AI and execution stacks, while IronWave targets Rust-native low-latency pipelines.
How are benchmarks measured?
Latency uses JMH or Criterion microbenchmarks with pinned cores and steady-state sampling, documented in the methodology page.
Validate with Benchmarks
Review latency and throughput benchmarks to confirm fit for your execution stack.