QuanTAlib/python/README.md

quantalib

447 technical analysis indicators compiled to native code via .NET NativeAOT, called from Python through ctypes. Same SIMD-accelerated engine as the QuanTAlib .NET package. Zero Python math reimplementation.

pip install quantalib

Quick Start

import numpy as np
import quantalib as qtl

close = np.random.default_rng(42).normal(100, 2, size=500)

sma = qtl.sma(close, period=20)
rsi = qtl.rsi(close, period=14)
upper, mid, lower = qtl.bbands(close, period=20, std=2.0)

Works with pandas, polars, and pyarrow — same-type-in, same-type-out:

# pandas — preserves index
import pandas as pd
s = pd.Series(close, name="close")
rsi = qtl.rsi(s, period=14)        # → pd.Series

# polars — zero-copy-friendly
import polars as pl
s = pl.Series("close", close)
rsi = qtl.rsi(s, period=14)        # → pl.Series
bb  = qtl.bbands(s, period=20)     # → pl.DataFrame (upper, mid, lower)

# pyarrow — for Arrow-native pipelines
import pyarrow as pa
a = pa.array(close, type=pa.float64())
rsi = qtl.rsi(a, period=14)        # → pa.Array

pandas-ta users: length= is accepted everywhere as an alias for period=.

Install optional backends:

pip install quantalib[pandas]       # pandas / pd.Series support
pip install quantalib[polars]       # polars / pl.Series support
pip install quantalib[pyarrow]      # pyarrow / pa.Array support
pip install quantalib[all]          # all three

Performance (500,000 bars)

Indicator	quantalib	wickra	pandas-ta
SMA	1,298 μs	12,548 μs	63,721 μs
EMA	1,067 μs	12,179 μs	4,280 μs
WMA	1,139 μs	12,547 μs	83,903 μs
HMA	2,647 μs	14,768 μs	152,733 μs

quantalib calls the same SIMD-optimized NativeAOT core as the .NET package through a single ctypes call (5-15 μs overhead). wickra (Rust/PyO3) returns stdlib array.array; pandas-ta stays in pure Python/numpy. For arrays above a few hundred bars, NativeAOT wins by an order of magnitude or more.

Categories

Category	Module	Examples
Channels	channels	bbands, kc, dc, aberr
Core	core	ha, midpoint, avgprice, typprice
Cycles	cycles	ht_dcperiod, ht_sine, cg, dsp
Dynamics	dynamics	adx, aroon, ichimoku, supertrend
Errors	errors	mse, rmse, mae, mape, huber
Filters	filters	kalman, sgf, hp, butter2, wavelet
Momentum	momentum	rsi, macd, roc, mom, tsi
Numerics	numerics	fft, normalize, sigmoid, slope
Oscillators	oscillators	stoch, cci, fisher, qqe, willr
Reversals	reversals	sar, pivot, fractals, swings
Statistics	statistics	zscore, correl, entropy, linreg
Trends FIR	trends_fir	sma, wma, hma, alma, trima
Trends IIR	trends_iir	ema, dema, tema, kama, jma
Volatility	volatility	atr, bbw, stddev, hv, tr
Volume	volume	obv, vwma, mfi, cmf, adl

Requirements

• Python 3.10+
• NumPy >= 1.24

Pre-built wheels — the NativeAOT core is compiled per platform in CI and ships inside the wheel, so there is no source build and no C++ toolchain:

Platform	Wheel tag
Windows x64	win_amd64
Linux x64	manylinux_2_17_x86_64
Linux arm64 (AWS Graviton)	manylinux_2_17_aarch64
macOS arm64 (Apple Silicon)	macosx_11_0_arm64
macOS x64 (Intel)	macosx_10_13_x86_64

Optional dependencies

Extra	Minimum version	Enables
pandas	≥ 1.5	pd.Series / pd.DataFrame round-trip
polars	≥ 0.20	pl.Series / pl.DataFrame round-trip
pyarrow	≥ 14.0	pa.Array / pa.ChunkedArray round-trip

License

Apache License 2.0