QuanTAlib/README.md

QuanTAlib - Quantitative Technical Indicators Without Compromises

TA libraries face a fundamental choice: accept approximations for simplicity OR enforce math rigor. QuanTAlib chooses rigor.

Quantitative TA library (QuanTAlib) is a C# library built on the premise that you shouldn't have to choose. Modern CPUs process 4-8 FLOPS per cycle via SIMD. Modern .NET exposes memory layouts making hardware acceleration trivial. QuanTAlib exploits both. Result: mathematically rigorous indicators at speeds making real-time multi-symbol analysis practical on ordinary hardware.

Key Features

• Zero Allocation: Hot paths are allocation-free. No GC pauses during trading.
• SIMD Accelerated: Uses AVX2/AVX-512 for 8x throughput on modern CPUs.
• O(1) Streaming: Constant time updates regardless of lookback period.
• Platform Agnostic: Runs on .NET 8/9/10, compatible with Quantower, NinjaTrader, QuantConnect.
• Mathematically Rigorous: Validated against original research papers and established libraries.

Indicators

Category	What It Measures	Representative Indicators
Trends	Direction and strength of price movement through smoothing and filtering	SMA, EMA, WMA, HMA, JMA, KAMA, ALMA, DEMA, TEMA, T3
Volatility	Size and variability of price movements	ATR, StdDev, Bollinger Bands, Keltner Channels, Historical Volatility
Momentum	Speed and magnitude of price changes	RSI, Stochastic, CCI, Williams %R, MACD, Momentum, ROC
Volume	Trading activity and price-volume relationships	OBV, VWAP, Volume ROC, A/D, MFI
Channels	Price boundaries and range definitions	Donchian Channels, Keltner Channels, Price Channels
Statistics	Mathematical relationships between price series	Correlation, Covariance, Beta, Z-Score, Linear Regression
Numerics	Mathematical transformations and signal processing	Convolution, Filters, Integration, Differentiation, Smoothing
Errors	Measurement accuracy and model fit quality	MAE, RMSE, Residuals, R-Squared
Forecasts	Future price prediction and projection	Linear Regression Forecast, Moving Average Projection
Cycles	Periodic patterns and dominant frequencies	Hilbert Transform, Dominant Cycle, Instantaneous Phase, Sine Wave

Quick Start

Install from NuGet:

dotnet add package QuanTAlib

Calculate an SMA in real-time:

using QuanTAlib;

var sma = new Sma(period: 14);
double price = 100.0;

// Update with new price
var result = sma.Update(new TValue(DateTime.UtcNow, price));

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

Performance Snapshot

QuanTAlib is designed for speed. Here is how it compares calculating a 500,000 bar SMA against other libraries:

Library	Mean Time	Allocations	Relative Speed
QuanTAlib (Span)	318.3 μs	0 B	1.00x (baseline)
TA-Lib	356.4 μs	34 B	1.12x slower
Tulip Indicators	359.3 μs	0 B	1.13x slower
Skender Indicators	71,277 μs	50.8 MB	224x slower

See Benchmarks for full details and methodology.

Documentation

• Architecture: Learn about SoA layout, SIMD, and design philosophy.
• Indicators: Full catalog of available indicators and their mathematical families.
• Benchmarks: Detailed performance evidence and test methodology.
• Usage Guides: Detailed patterns for Span, Streaming, Batch, and Eventing modes.
• Integration: Setup guides for Quantower, NinjaTrader, and QuantConnect.
• Glossary: Definitions of core QuanTAlib concepts, types, and terminology.