Awesome Quant

A Python 3 library making time series data mining tasks, utilizing matrix profile algorithms, accessible to everyone.

Robust and flexible Python implementation of the willow tree lattice for derivatives pricing.

Python framework for quantitative financial analysis and trading algorithms on decentralised exchanges

A fast, extensible, transparent python library for backtesting quantitative strategies.

Time series toolkit for Julia

A fixed income library for pricing bonds and bond futures, and derivatives such as interest rate swaps (IRS), cross-currency swaps (XCS) and FX swaps. Contains tools for full curveset construction with market standard optimisers and automatic differentiation (AD) and risk sensitivity calculations including delta and cross-gamma.

Technical analysis and other functions to construct technical trading rules with R

Fast and scalable construction of risk parity portfolios

Python client for interacting with the Tiingo Financial Data API (stock ticker and news data)

Basic options pricing in Python

No description.

Entropy Pooling views and stress testing combined with Conditional Value-at-Risk (CVaR) portfolio optimization in Python.

A backtester and spreadsheet library for stocks and ETFs

No description.

Vectorized backtester and trading engine for QuantRocket

Examples using pysystemtrade for my blog qoppac.blogspot.com

Open source math and physics engine that executes MATLAB-syntax code. Features a modern compiler, automatic GPU acceleration for large calculations, and support for math on geometry / FEA calculations.

pandas wrapper for Bloomberg Open API

Prediction-market trading engine — Wang Transform pricing on 291K+ contracts; paper-traded across Kalshi · Polymarket · Solana DFlow (Jito bundles) · 633 tests

Start developing and backtesting your own automated trading strategies

Forex algorithmic trading framework using OANDA REST API.

No description.

Financial market technical analysis & indicators in Julia

Extensible time series class that provides uniform handling of many R time series classes by extending zoo.

:chart: Python framework for real-time financial and backtesting trading strategies

The fastest way from backtest to live trading.

Quant Option Pricing - Exotic/Vanilla: Barrier, Asian, European, American, Parisian, Lookback, Cliquet, Variance Swap, Swing, Forward Starting, Step, Fader

Pipeline Extension for Live Trading

Fully functioning fast Limit Order Book written in Python

Feature Engineering and Feature Importance in Machine Learning for Financial Markets

A sentiment analyzer package for financial assets and securities utilizing GPT models.

A JavaScript library to allocate and optimize financial portfolios.

Repo for code examples in Quantitative Finance with Python (1st edition) by Chris Kelliher

Python Client for Interfacing with the Federal Reserve Bank of St. Louis' Economic Data API (FRED®)

Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning

R package interfacing the Bloomberg API from https://www.bloomberglabs.com/api/

Time-aware tibbles

Get market data from Yahoo Finance websocket in near-real time.

Self-hosted Python strategy research and Alpaca paper trading with broker reconciliation, evidence gates, and live execution disabled.

QuantComponents - Free Java components for Quantitative Finance and Algorithmic Trading

Detect trend in time series, drawdown, drawdown within a constant look-back window , maximum drawdown, time underwater.

Quantitative systematic trading strategy development and backtesting in Julia

High performance order matching engine

Time series market data

High level API for access to and analysis of financial data.

JQuantLib is a library for Quantitative Finance written in 100% Java

A python library for computing technical analysis indicators on streaming data.

Reimplementation of Autoencoder Asset Pricing Models (GKX, 2019)

portfolio construction and quantitative analysis

Implement, demonstrate, reproduce and extend the results of the Risk articles 'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the papers.