QuantWave

High-performance, Polars-native technical analysis — in Python and Rust

221 Native Indicators · Full Ehlers DSP suite · Regime Detection · Backtest engine · Bit-identical streaming & batch

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

📖 Documentation • ⭐ GitHub •

221 indicators • Polars-native • Streaming & batch parity • MIT licensed

Why QuantWave?

Most quantitative libraries force an uncomfortable compromise.

Python-first libraries (pandas-ta, TA-Lib Python wrappers, etc.) are convenient but fall apart on large datasets, recursive indicators, or live streaming — often becoming 10-100x slower than native code.

Pure Rust libraries are fast, but they rarely integrate cleanly with modern Polars-based research pipelines and lack the breadth of advanced techniques (Ehlers DSP, regime detection, full Options India analytics).

QuantWave removes the tradeoff.

It delivers institutional-grade Rust performance through zero-copy Polars expressions, while offering a first-class, productive experience in both Python and Rust. Every indicator is built on a single mathematical source of truth — the Next<T> trait — guaranteeing that batch results (Polars) and real-time streaming results are bit-identical.

How We Compare

Approach	Speed on large data	Polars-native	Streaming parity	Breadth (Ehlers + Regimes + Options)
pandas-ta / TA-Lib (Python)	Poor–Average	Partial	Rare	Limited
Other Rust TA crates	Excellent	Poor	Rare	Limited
QuantWave	Excellent	Native	Guaranteed	Strong

What We’ve Built

QuantWave is no longer early-stage. It ships with production-ready depth across several domains:

221 Native Indicators** with gold-standard validation and extensive Ehlers DSP coverage
Full Regime Detection Suite (HMM, GMM, PELT, clustering, conditioned risk metrics)
Complete Options India Stack — Black-Scholes Greeks, IV solvers, chain analytics (Max Pain, PCR, GEX, OI Zones), and NSE utilities, all exposed as native Polars expressions
Streaming & Batch Parity — The same mathematical logic powers both high-speed Polars pipelines and low-latency streaming via the universal Next<T> trait
Gold-Standard Validation — Every indicator is tested against reference implementations for correctness

Core Strengths

Performance — Rust core with zero-copy Polars expressions
Correctness — Validated against gold-standard reference vectors
Parity — Bit-identical results between batch and streaming
Breadth — Classic indicators + advanced Ehlers DSP + regime detection + Options India
Developer Experience — Clean Python API (from quantwave import ta) and idiomatic Rust

Real-World Performance

Memory footprint on realistic multi-ticker data: 2–5× lower than Pandas (measured — see benchmarks)
Speed & latency: published only from the reproducible harness in benchmarks/; earlier unmeasured throughput figures have been removed

→ Full benchmarks & methodology

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()

More examples → Documentation

Get Started

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Made with ❤️ for the quant community.

quantwave-core 0.7.0