> Open access: all rights granted for use and re-use of any kind, by anyone, at no cost, under your choice of either the free MIT License or Creative Commons CC-BY International Public License.
Note: the one marked as `Live Trading` has reasonable live trading support for at least 1 broker. Otherwise, backtest
function only.
- [aat](https://github.com/AsyncAlgoTrading/aat) | `Python`, `C++`, `Live Trading`| - an asynchronous, event-driven framework for writing algorithmic trading strategies in python with optional acceleration in C++. It is designed to be modular and extensible, with support for a wide variety of instruments and strategies, live trading across (and between) multiple exchanges.
- [barter-rs](https://github.com/barter-rs/barter-rs) | `Rust` | - Open-source Rust framework for building event-driven live-trading & backtesting systems. Algorithmic trade with the peace of mind that comes from knowing your strategies have been backtested with a near-identical trading Engine.
- [backtesting.py](https://github.com/kernc/backtesting.py) | `Python` | - Backtesting.py is a Python framework for inferring viability of trading strategies on historical (past) data. Improved upon the vision of Backtrader, and by all means surpassingly comparable to other accessible alternatives, Backtesting.py is lightweight, fast, user-friendly, intuitive, interactive, intelligent and, hopefully, future-proof.
- [lumibot](https://github.com/Lumiwealth/lumibot/tree/8da88cadfe9ee35399dd69c94aa5ed3cf995f417) | `Python` | - A very simple yet useful backtesting and sample based live trading framework (a bit slow to run...)
- [sdoosa-algo-trade-python](https://github.com/sreenivasdoosa/sdoosa-algo-trade-python) | `Python` | - This project is mainly for newbies into algo trading who are interested in learning to code their own trading algo using python interpreter.
- [vnpy](https://github.com/vnpy/vnpy) | `Python`, `Stock`, `Futures`, `Crypto`, `Live Trading` | - Python-based open source quantitative trading system development framework, officially released in January 2015, has grown step by step into a full-featured quantitative trading platform
- [zipline](https://github.com/quantopian/zipline) | `Python` | - Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting.
- [pysystemtrade](https://github.com/robcarver17/pysystemtrade) | `Python`, `Live Trading` | - Systematic Trading in python from book <Systematic Trading> by Rob Carver
- [vectorbt](https://github.com/polakowo/vectorbt) | `Python`, `numba` | - vectorbt takes a novel approach to backtesting: it operates entirely on pandas and NumPy objects, and is accelerated by Numba to analyze any data at speed and scale. This allows for testing of many thousands of strategies in seconds.
- [crypto-crawler-rs](https://github.com/crypto-crawler/crypto-crawler-rs) | `Rust` | - Crawl orderbook and trade messages from crypto exchanges
- [cryptotrader-core](https://github.com/monomadic/cryptotrader-core) | `Rust` | - Simple to use Crypto Exchange REST API client in rust.
- [openlimits](https://github.com/nash-io/openlimits) | `Rust` | - A Rust high performance cryptocurrency trading API with support for multiple exchanges and language wrappers.
- [Freqtrade](https://github.com/freqtrade/freqtrade) | `Python` | - Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning.
- [Jesse](https://github.com/jesse-ai/jesse) | `Python` | - Jesse is an advanced crypto trading framework which aims to simplify researching and defining trading strategies.
- [OctoBot](https://github.com/Drakkar-Software/OctoBot) | `Python`, `Cython`, `Live Trading`| - Cryptocurrency trading bot for TA, arbitrage and social trading with an advanced web interface
- [Kelp](https://github.com/stellar/kelp) | `Go`, `Live Trading` | - Kelp is a free and open-source trading bot for the Stellar DEX and 100+ centralized exchanges
- [FinRL](https://github.com/AI4Finance-Foundation/FinRL) | `Python` | - FinRL is the first open-source framework to demonstrate the great potential of applying deep reinforcement learning in quantitative finance.
- [QLib (Microsoft)](https://github.com/microsoft/qlib) | `Python`, `Cython` | - Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib.
- [TradingGym](https://github.com/Yvictor/TradingGym) | `Python`, `Live Trading` | - Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.
- [Numpy](https://github.com/numpy/numpy) | `Python`, `C` | - The fundamental package for scientific computing with Python
- [Scipy](https://github.com/scipy/scipy) | `Python`, `C` | - Fundamental algorithms for scientific computing in Python
- [Pandas](https://github.com/pandas-dev/pandas) | `Python`, `Cython` | - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
- [Ray](https://github.com/ray-project/ray) | `Python`, `C++` | - An open source framework that provides a simple, universal API for building distributed applications.
- [Incremental (JaneStreet)](https://github.com/janestreet/incremental) | `Ocaml` | - Incremental is a library that gives you a way of building complex computations that can update efficiently in response to their inputs changing, inspired by the work of Umut Acar et. al. on self-adjusting computations. Incremental can be useful in a number of applications
- [Polars](https://github.com/pola-rs/polars) | `Rust`, `Python` | - Polars is a blazingly fast DataFrames library implemented in Rust using Apache Arrow Columnar Format as memory model.
- [Vaex](https://github.com/vaexio/vaex) | `Python`, `C++` | - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second
- [finta](https://github.com/peerchemist/finta) | `Python` | - Common financial technical indicators implemented in Pandas
- [pandas-ta](https://github.com/twopirllc/pandas-ta) | `Python` | - Pandas Technical Analysis (Pandas TA) is an easy to use library that leverages the Pandas package with more than 130 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns.
### Pricing
- [Quantlib](https://www.quantlib.org)
- [PyQL](https://github.com/enthought/pyql) | `Python`, `Cython` | - Python wrapper of the famous pricing library QuantLib
- [QuantLib.jl](https://github.com/pazzo83/QuantLib.jl) | `Julia` | - Quantlib implementation in pure Julia.
- [FinancePy](https://github.com/domokane/FinancePy) | `Python` | - A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.
- [tf-quant-finance](https://github.com/google/tf-quant-finance) - High-performance TensorFlow library for quantitative finance from Google
### Risk
- [pyfolio](https://github.com/quantopian/pyfolio) | `Python` | - Portfolio and risk analytics in Python
### Optimization
- [Deepdow](https://github.com/jankrepl/deepdow) | `Python` | - Python package connecting portfolio optimization and deep learning. Its goal is to facilitate research of networks that perform weight allocation in one forward pass.
- [empyrial](https://github.com/ssantoshp/Empyrial) | `Python` | - Empyrial is a Python-based open-source quantitative investment library dedicated to financial institutions and retail investors, officially released in March 2021.
- [spectre](https://github.com/Heerozh/spectre) | `Python` | - spectre is a GPU-accelerated Parallel quantitative trading library, focused on performance.
### TimeSeries Analysis
- [statsmodels](http://statsmodels.sourceforge.net) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests.
- [tsfresh](https://github.com/blue-yonder/tsfresh) - Automatic extraction of relevant features from time series.
- [Facebook Prophet](https://github.com/facebook/prophet) - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
- [pmdarima](https://github.com/alkaline-ml/pmdarima) - A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.
## Visualization
- [D-Tale (Man Group)](https://github.com/man-group/dtale) | `JavaScript`, `Python` | - D-Tale is the combination of a Flask back-end and a React front-end to bring you an easy way to view & analyze Pandas data structures.
- [mplfinance](https://github.com/matplotlib/mplfinance) | `Python` | - Financial Markets Data Visualization using Matplotlib
- [btplotting](https://github.com/happydasch/btplotting) | `Python`, `bokeh` | - btplotting provides plotting for backtests, optimization results and live data from backtrader.
- [Tectonicdb](https://github.com/0b01/tectonicdb) | `Rust` | - Tectonicdb is a fast, highly compressed standalone database and streaming protocol for order book ticks.
- [findatapy](https://github.com/cuemacro/findatapy) |`Python`| - findatapy creates an easy to use Python API to download market data from many sources including Quandl, Bloomberg, Yahoo, Google etc. using a unified high level interface.
- [yfinance](https://github.com/ranaroussi/yfinance) |`Python`| - yfinance offers a threaded and Pythonic way to download market data from Yahoo!Ⓡ finance.
- [pandas-datareader](https://github.com/pydata/pandas-datareader) |`Python`| - Up to date remote data access for pandas, works for multiple versions of pandas.
- [Wallstreet](https://github.com/mcdallas/wallstreet) |`Python`| - Wallstreet: Real time Stock and Option tools
- [TuShare](https://github.com/waditu/tushare) |`Python`| - TuShare is a utility for crawling historical data of China stocks
- [Investpy](https://github.com/alvarobartt/investpy) - Financial Data Extraction from Investing.com with Python
- [AkShare](https://github.com/akfamily/akshare) |`Python`| - AKShare is an elegant and simple financial data interface library for Python, built for human beings! 开源财经数据接口库
- [Fundamental Analysis Data](https://github.com/JerBouma/FundamentalAnalysis) | `Python` | - Fully-fledged Fundamental Analysis package capable of collecting 20 years of Company Profiles, Financial Statements, Ratios and Stock Data of 20.000+ companies.
- [ccxt](https://github.com/ccxt/ccxt) | `Python`, `JavaScript` | - A JavaScript / Python / PHP cryptocurrency trading API with support for more than 100 bitcoin/altcoin exchanges
- [Coinnect](https://github.com/hugues31/coinnect) | `Rust` | - Coinnect is a Rust library aiming to provide a complete access to main crypto currencies exchanges via REST API.
- [Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage (2021)](https://www.amazon.co.uk/Quantitative-Portfolio-Management-Statistical-Arbitrage/dp/1119821320/ref=asc_df_1119821320/?tag=googshopuk-21&linkCode=df0&hvadid=534858257189&hvpos=&hvnetw=g&hvrand=3040398248892159445&hvpone=&hvptwo=&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9044954&hvtargid=pla-919734400242&psc=1&th=1&psc=1)
- [Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python](https://github.com/stefan-jansen/machine-learning-for-trading)
- [Algorithmic Trading for Cryptocurrencies in Python](https://github.com/tudorelu/tudorials/tree/master/trading) - A simple yet practical experiment tutorial for cryto trading.
- [Hudson and Thames Quantitative Research](https://github.com/hudson-and-thames) - Our mission is to promote the scientific method within investment management by codifying frameworks, algorithms, and best practices.
