QuanTAlib

QuanTAlib

447 technical indicators. One library. Brutal architectural trade-offs for absolute speed.

⭐ Documentation pages →

QuanTAlib exists because I got tired of validating other people’s indicators. Every implementation is cross-checked against TA-Lib, Tulip, Skender, and Pandas-TA. Where they disagree, we went to the original papers. Where the papers disagree, we picked the math that doesn’t lie.

Same indicators, same results: C#, Python, and PineScript.

How Fast?

C# native AOT-compiled code spits out half a million bars of SMA in 288 microseconds. That is faster (per value) than a single L1 cache miss on any fancy new CPU. Achieved by trading object allocation for contiguous memory spans, slapping Fused Multiply-Add (FMA) on everything, and forcing SIMD vectorized paths. You want speed? We dictate the heap.

Library	SMA (500K bars)	Allocations	Relative time
QuanTAlib	288 μs	0 B	1×
TA-Lib (C++)	365 μs	32 B	1.3× slower
Wickra (Rust)	1,562 μs	4 MB	5.4× slower
Skender (C#)	68,378 μs	42 MB	238× slower
Ooples (C#)	346,187 μs	151 MB	1,202× slower

Measured with BenchmarkDotNet v0.15.3 on .NET 10 x64 (AVX-512), 500K bars, period 220. Benchmark project →

Skender and Ooples are measured on their list-based APIs; QuanTAlib on its zero-allocation span API. Same input, same output — different memory shape.

Full benchmarks →

Install

Platform	Install	Guide
.NET	dotnet add package QuanTAlib	Architecture . API Reference
Python	pip install quantalib	Python Guide
PineScript v6	Copy-paste to TradingView	PineScript Guide

pip install quantalib ships a compiled native wheel — no source build, 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

Show Me the Code

C# Streaming (Real-time incoming data, value by value)

using QuanTAlib;

var sma = new Sma(period: 14);
var result = sma.Update(110.4);

if (result.IsHot)
    Console.WriteLine($"SMA: {result.Value}");

State lives inside the indicator. No list of historic bars. No LINQ chains allocating their way to thermal throttling. Call .Update(), get answer.

C# — batch (500K bars in microseconds)

double[] prices = LoadHistoricalData();
double[] results = new double[prices.Length];

Sma.Batch(prices.AsSpan(), results.AsSpan(), period: 14);

Contiguous memory. AVX-512 vectorization. The Garbage Collector sleeps through the whole thing and nobody wakes it.

Python

import quantalib as qtl
import numpy as np

prices = np.random.default_rng(42).normal(100, 2, size=500_000)
sma = qtl.sma(prices, period=14)       # 447 indicators, similar syntax

Works with NumPy, pandas, polars, and PyArrow. NativeAOT compiled, ships as a binary. No CLR runtime dragged along for the ride.
Full Python guide →

PineScript

Every indicator ships as a standalone .pine file. Open it. Copy it. Paste it into TradingView. No magic, no dependencies, just math that matches the C# and Python versions to the 10th decimal.
Full PineScript guide →

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447 Indicators

Category	Count	What It Measures	Examples
Core	8	Price transforms, building blocks	AVGPRICE, MEDPRICE, TYPPRICE, HA
Trends (FIR)	33	Finite impulse response averages	SMA, WMA, HMA, ALMA, TRIMA, LSMA
Trends (IIR)	36	Infinite impulse response averages	EMA, DEMA, TEMA, T3, JMA, KAMA, VIDYA
Filters	39	Signal processing, noise reduction	Kalman, Butterworth, Gaussian, Savitzky-Golay
Oscillators	61	Bounded/centered oscillators	RSI, MACD, Stochastic, CCI, Fisher, Williams %R
Dynamics	27	Trend strength and direction	ADX, Aroon, SuperTrend, Ichimoku, Vortex
Momentum	20	Speed of price changes	ROC, Momentum, Velocity, TSI, Qstick
Volatility	26	Price variability	ATR, Bollinger Width, Historical Vol, True Range
Volume	28	Trading activity	OBV, VWAP, MFI, CMF, ADL, Force Index
Statistics	37	Statistical measures	Correlation, Variance, Skewness, Z-Score
Channels	24	Price boundaries	Bollinger Bands, Keltner, Donchian
Cycles	18	Cycle analysis	Hilbert Transform, Homodyne, Ehlers Sine Wave
Reversals	15	Pattern detection	Pivot Points, Fractals, Swings
Forecasts	1	Predictive indicators	Time Series Forecast
Errors	26	Error metrics, loss functions	RMSE, MAE, MAPE, SMAPE, R²
Numerics	36	Mathematical transforms	Log, Exp, Sigmoid, Normalize, FFT
Signals	12	Strategy primitives: predicates, triggers, guards	ABOVE, BELOW, ISRISING, ISFALLING

Browse all 447 indicators →

Architecture (the short version)

Streaming mode: O(1) per update. Fixed memory. State maintained internally. Feed it ticks, get answers. No history buffer, no lookback window allocation, no please pass me the last 200 bars so I can warm-up nonsense.

Batch mode: Structure-of-Arrays memory layout. SIMD vectorized. FMA everywhere the hardware allows. Processes contiguous Span<double> with zero heap allocation. Your profiler will be confused by the absence of GC pressure.

Dual-state management: Bars can be corrected mid-stream (because real-time feeds are liars). The indicator tracks both confirmed and pending state so corrections don’t require a full recalculation.

Full architecture docs →

Validation

Every indicator is cross-validated against reference implementations using Geometric Brownian Motion (GBM) generated test data. Not cherry-picked sine waves. Geometric Brownian Motion with realistic drift and volatility, because indicators that only work on textbook inputs are not indicators: they are demos.

Validation matrices →
Error metrics →
Trend comparison →

Documentation

Architecture & API: Architecture · API Reference · Usage Patterns · Integration (Quantower, NinjaTrader, QuantConnect)

Analysis: Benchmarks · Validation · MA Qualities · Glossary

Code Quality Assurance: NDepend · Codacy · SonarCloud · CodeFactor

Stability

In development since 2022. Four years, 700+ commits, 447 indicators — every one cross-validated against at least three independent implementations on realistic GBM data. The public API is frozen: breaking changes fail the build, so your code doesn’t wake up one morning to a surprise. Versioning is SemVer with a one-minor-version [Obsolete] deprecation window — deprecate today, remove next release, no guillotine.

Bugs get triaged fast. Open an issue — every report is read, and real bugs jump the queue ahead of the Dependabot noise. Questions, feature ideas, and “why does this number look wrong” all land in the same place. If something is broken and you say nothing, it stays broken. If you say something, it gets fixed.

Where to ask: GitHub Issues — bugs, questions, and ideas all live there. Versioning policy →

License

Apache 2.0. Not MIT. Not BSD. Deliberately →