quantalib 0.8.6

High-performance technical analysis wrappers over QuanTAlib NativeAOT

quantalib

393 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, AVX-512)

Indicator	quantalib	pandas-ta	Ratio
SMA	328 μs	~50 ms	~150×
EMA	421 μs	~45 ms	~107×
WMA	302 μs	~60 ms	~199×
RSI	517 μs	~80 ms	~155×

The ctypes call adds 5-15 μs overhead. For arrays above a few hundred bars, NativeAOT wins by two orders of magnitude.

Categories

Category	Module	Examples
Channels	channels	bbands, kchannel, dchannel, 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	psar, pivot, fractals, swings
Statistics	statistics	zscore, correlation, 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: win-x64, linux-x64, osx-x64, osx-arm64

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