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Purpose of Our Work

QuantWave was built to solve a real problem in quantitative finance:

Most existing libraries force you to choose between performance and ease of use.
Python-only libraries are slow on large datasets and live streams.
Pure Rust crates are fast but painful to integrate into modern Polars-based research workflows.

We decided to build something better.

Our Mission

To create the fastest, most complete, and most Polars-native quantitative toolkit in existence — combining:

  • 150+ technical indicators with perfect TA-Lib parity
  • Full Ehlers Digital Signal Processing suite (the most advanced cycle and trend tools available in open source)
  • Options India Suite: Native Black-Scholes, IV solvers, and Greeks optimized for the Indian market.

  • Zero-copy, vectorized Polars expressions that run at Rust speed

  • Seamless batch + streaming modes for both research and live trading

  • Future-proof architecture ready for options pricing, Greeks, risk engines, and beyond

Why This Matters

Whether you are: - A quantitative researcher backtesting thousands of instruments - A systematic trader running live strategies - A hedge fund building institutional-grade tooling - A student or hobbyist who refuses to compromise on speed

…QuantWave gives you one library that works everywhere — from Jupyter notebooks to production trading systems — without ever leaving the Polars ecosystem.

Our Promise

  • Performance first — Rust under the hood, zero compromises
  • Correctness first — every indicator validated against gold-standard references
  • Developer experience first — beautiful Python API + clean Rust API
  • Community driven — MIT licensed, fully open, built for you

We are just getting started.
The Polars-native backtest engine is documented in the Backtest Capability Matrix. Advanced risk metrics and portfolio tools remain on the roadmap.

Welcome to the next generation of open-source quant tooling.

Made with ❤️ for the quant community
— The QuantWave Team