Signal Analytics Infrastructure for HFT & AI Pipelines
Build nanosecond-grade signal processing into execution systems and machine learning pipelines with wavelet analytics engineered for institutional scale.
Real-Time Denoising
Extract clean signals from noisy market data without lag, enabling sharper execution and faster model convergence.
Multi-Scale Insight
Decompose price action into distinct timescales to separate microstructure noise from durable trends.
Enterprise-Ready
Pure Java and Rust implementations integrate cleanly into Spark, Flink, and low-latency execution stacks.
Technical Guides
Core Signal Analytics Libraries
Enterprise Use Cases
HFT Execution
Stream sub-100ns transforms for real-time signal filtering and microstructure detection.
AI Feature Engineering
Build stable, shift-invariant features with MODWT for time-series forecasting at scale.
System Monitoring
Detect anomalies and regime shifts in streaming telemetry with multi-resolution analysis.
Performance You Can Validate
Benchmarks show nanosecond latency and hundreds of millions of samples per second with SIMD acceleration.
See benchmark methodology and results →FAQ
Why use wavelets for HFT signal analytics?
Wavelets preserve time localization and update incrementally, reducing windowing lag and capturing microstructure shifts faster than batch FFT pipelines.
How do VectorWave and IronWave fit enterprise stacks?
VectorWave runs on Java 21+ with SIMD support for AI pipelines, while IronWave delivers Rust-native streaming transforms for low-latency execution systems.
Where can I validate performance claims?
Review the performance benchmarks and methodology pages, or request an evaluation pack to reproduce results in your environment.
Build Your Signal Stack
Talk with our team about evaluation access, custom integrations, and enterprise licensing.
Request Access