quantwave 0.6.0

High-performance Rust + Polars quantitative finance library with 150+ indicators and full Ehlers DSP suite

QuantWave

High-performance quantitative finance library
Built in Rust · Native Polars support · 150+ indicators · Full Ehlers DSP suite

Python pip install quantwave (or pip install "quantwave[polars]" for Polars layer)
Rust cargo add quantwave

📖 Full Documentation
⭐ GitHub

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Purpose of Our Work

Most quant libraries force you to choose between speed and ease of use.
We built QuantWave to give you both.

• 150+ technical indicators with perfect TA-Lib parity
• Complete Ehlers Digital Signal Processing suite (the most advanced open-source cycle tools)
• Zero-copy Polars expressions that run at Rust speed
• Seamless batch + streaming modes
• Future-proof architecture (Options Greeks, risk metrics, etc. coming soon)

One library. Research to production. No compromises.

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Quickstart (Python)

pip install "quantwave[polars]"
quantwave doctor

import polars as pl
import quantwave  # registers pl.col().ta and LazyFrame.bt

df = pl.read_parquet("ohlcv.parquet")

df = df.lazy().with_columns(
    pl.col("close").ta.rsi(timeperiod=14).alias("rsi"),
    pl.col("close").ta.ema(period=20).alias("ema"),
).collect()

Full examples → Documentation

Features

• Lightning fast – Rust core with Polars native expressions.
• Battle-tested – Every indicator validated against reference implementations.
• Modern – Works perfectly in Jupyter, scripts, and live trading systems.
• MIT licensed – Free for commercial and personal use.

Next Steps

• Python Guide
• Rust Guide
• Options Greeks & Pricing (roadmap)

CLI

quantwave list                  # all indicators by category
quantwave info rsi              # metadata for one indicator
quantwave doctor                # verify core, polars, plugins, backtest
quantwave version

Python 0.6.0 packaging

• Unified PyPI wheel — core + quantwave_plugins + quantwave._backtest in one install
• quantwave doctor — environment diagnostics for production pipelines
• pip install "quantwave[polars]" — adds Polars for batch .ta and .bt namespaces

Python 0.5.2 DX Improvements (quantwave-p3z9)

We have made major improvements to the Python developer experience:

New recommended APIs:

• quantwave.indicators() – discover available indicators
• quantwave.metadata("rsi") – rich per-indicator metadata (params, data inputs, warmup, category, etc.)
• quantwave.assert_parity(...) – verify batch vs streaming bit-identical behavior
• quantwave.streaming_class("rsi") + wrap_streaming(...) – better streaming ergonomics with readiness tracking

Reduced namespace pollution:

• Result dataclasses and Options India helpers are now available under quantwave.results and quantwave.options.
• Old top-level access still works but emits DeprecationWarning.

Other:

• quantwave.__version__ is now properly exposed.
• quantwave.categories() / category(name) for programmatic discovery.
• quantwave.boundary_info(name) for warmup and error semantics.
• quantwave.talib submodule with list_functions() for TA-Lib migration.
• Structured errors: QuantwaveError, ParityError, IndicatorNotFoundError, etc.
• Linux arm64 wheels are available.

Documentation: https://lavs9.github.io/quantwave/ — ML Features guide, Backtest quickstart, and API reference.

See the full list of changes in the main changelog.

Made with ❤️ for the quant community.