quantwave 0.1.18

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
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

import polars as pl
from quantwave import ta

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

df = df.with_columns(
    ta.rsi("close", 14).alias("rsi"),
    ta.mama("close").alias("mama"),
)

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

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

Made with ❤️ for the quant community.