Awesome Quant

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

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

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

Agent-driven alpha factory — LLM autonomously designs, backtests, and submits factors to WorldQuant BRAIN

Fast and scalable construction of risk parity portfolios

Basic options pricing in Python

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

No description.

A backtester and spreadsheet library for stocks and ETFs

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

No description.

Examples using pysystemtrade for my blog qoppac.blogspot.com

Vectorized backtester and trading engine for QuantRocket

Open Source Algorithmic Trading Engine

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

Forex algorithmic trading framework using OANDA REST API.

No description.

Start developing and backtesting your own automated trading strategies

Financial market technical analysis & indicators in Julia

Open-source runtime for math. Write MATLAB syntax, run on CPU + GPU across platforms (Mac/Win/Linux/Web).

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

Pipeline Extension for Live Trading

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

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.

The fastest way from backtest to live trading.

A JavaScript library to allocate and optimize financial portfolios.

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

Time-aware tibbles

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

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

QuantComponents - Free Java components for Quantitative Finance and Algorithmic Trading

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

Quantitative systematic trading strategy development and backtesting in Julia

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

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.

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.

Reimplementation of Autoencoder Asset Pricing Models (GKX, 2019)

High performance order matching engine

Quantlib implementation in pure Julia

Python client for tardis.dev - historical tick-level cryptocurrency market data replay API.