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polars-anta

ANTA (Alpha & Numerical Technical Analysis): Ultra-fast, zero-C-dependency technical analysis and factor library powered natively by Polars Expressions.

What is polars-anta?

polars-anta is an ultra-fast, modern financial technical analysis and alpha factor library designed from the ground up for quantitative researchers, algorithmic traders, and financial engineers.

Unlike traditional libraries (TA-Lib, pandas-ta) that require external C compilers, introduce cumbersome binary dependencies, or execute through slow Python for loops, polars-anta implements all 106 indicators natively as Polars Expressions (pl.Expr).

Every calculation is executed directly inside Polars' multi-threaded Rust query engine with zero-copy Apache Arrow memory layouts.

Key Advantages

Calculations bypass the Python GIL and run across all CPU cores automatically. Compute indicators on 1,000,000 rows in milliseconds.

Both pl.col("close").anta.<indicator>() and pl.col("close").ta.<indicator>() are supported natively with zero overhead.

No MSVC, no GCC, no headers. pip install polars-anta installs instantly on Linux, macOS, and Windows.

Calculate indicators across entire universes (500+ tickers simultaneously) in parallel via .over("ticker") without slow .groupby().apply().

Integrates directly with Polars' LazyFrame query optimizer, enabling predicate pushdown and streaming out-of-core evaluation for datasets exceeding RAM.

Performance at a Glance

Indicator / Operation Scale Pandas Execution polars-anta Execution Speedup
Linear Regression Slope 1,000,000 rows 1.8018 s 0.0154 s 116.8x Faster
Multi-Asset RSI 500 tickers x 2k bars 0.4560 s 0.0610 s 7.5x Faster
Multi-Asset SMA 500 tickers x 2k bars 0.1229 s 0.0176 s 7.0x Faster
Z-Score Normalization 1,000,000 rows 0.0378 s 0.0182 s 2.1x Faster

3-Line Quickstart

import polars as pl
import polars_anta  # Registers both .anta and .ta namespaces

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

# Compute indicators cleanly within expressions
features = df.with_columns(
    pl.col("close").anta.sma(20).alias("sma_20"),
    pl.col("close").anta.rsi(14).alias("rsi_14"),
    pl.col("close").anta.bollinger_bands(20, 2.0).alias("bb"),
)