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> 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.
- [AI Hedge Fund](https://github.com/virattt/ai-hedge-fund)   | `Python` | - An AI Hedge Fund Team
- [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.
- [FinGPT](https://github.com/AI4Finance-Foundation/FinGPT)   - FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
- [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.
- [Qbot](https://github.com/UFund-Me/Qbot)   | `Python` | - AI 自动量化交易机器人 AI-powered Quantitative Investment Research Platform.
- [VARRD](https://github.com/augiemazza/varrd)   | `Python` | - AI-powered trading research platform that validates any trading idea with event studies, statistical tests, and real market data across 15,000+ instruments. CLI, Python SDK, and MCP server.
- [the0](https://github.com/alexanderwanyoike/the0)   | Python, TypeScript, Rust, C++, C#, Scala, Haskell, Live Trading | - Self-hosted execution engine for algorithmic trading bots. Each bot runs in an isolated container. No framework imposed — your bot is just normal code.
- [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.
- [* bt](https://github.com/pmorissette/bt)   | Python | - Flexible backtesting for Python based on Algo and Strategy Tree
- [Better Quant](https://github.com/byrnexu/betterquant)   | C++, Live Trading | - Better quant today, best quant tomorrow. 💪
- [Botvana](https://github.com/featherenvy/botvana)   | Rust | - high-performance and event-driven trading system built using Rust
- [backtrader](https://github.com/mementum/backtrader)   | Python, Live Trading | - Event driven Python Backtesting library for trading strategies
- [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.
- [FlashFunk](https://github.com/HFQR/FlashFunk)   | Rust | - High Performance Runtime in Rust
- [QuantFabric](https://github.com/QuantFabric/QuantFabric)   | C++ | - QuantFabric是基于Linux/C++开发的中高频量化交易系统，支持中金所、郑商所、大商所、上期所、上海国际能源中心的期货业务品种交易，支持上交所、深交所的股票、债券品种交易。
- [gobacktest](https://github.com/gobacktest/gobacktest)   | Go | - A Go implementation of event-driven backtesting framework
- [lumibot](https://github.com/Lumiwealth/lumibot)   | Python | - A very simple yet useful backtesting and sample based live trading framework (a bit slow to run...)
- [quanttrader](https://github.com/letianzj/quanttrader)   | Python | - Backtest and live trading in Python. Event based. Similar to backtesting.py.
- [qf-lib](https://github.com/quarkfin/qf-lib)   | Python | - Modular Python library that provides an advanced event driven backtester and a set of high quality tools for quantitative finance. Integrated with various data vendors and brokers, supports Crypto, Stocks and Futures.
- [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.
- [PandoraTrader](https://github.com/pegasusTrader/PandoraTrader)   | C++ | - CTP 高频量化交易平台 C++ Trade Platform for quant developer
- [hftbacktest](https://github.com/nkaz001/hftbacktest)   | Python, numba | - A high-frequency trading and market-making backtesting tool accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and order books.
- [Cipher](https://github.com/nanvel/cipher-bt)   | Python | - Backtesting library with focus on position adjustment that allows testing complicated setups. Pythonic, extensible, well-structured, documented.
- [Tradingview Screener API](https://github.com/jmargieh/tradingview-screener)   | TypeScript | - Query TradingView's financial data with a type-safe, fluent TypeScript API. Screen stocks, crypto, forex, bonds, and futures using 13,000+ data fields. The library includes a Model Context Protocol (MCP) server that lets you query financial markets using natural language in Claude Desktop. This is a unique feature that enables conversational market analysis.
- [QTradeX](https://github.com/squidKid-deluxe/QTradeX-Algo-Trading-SDK)   | Python, Live Trading | - A powerful and flexible Python framework for designing, backtesting, optimizing, and deploying algotrading bots
- [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.
- [fastquant](https://github.com/enzoampil/fastquant)   | Python | - Backtest and optimize your ML trading strategies with only 3 lines of code
- [basana](https://github.com/gbeced/basana)   | Python | - A Python async and event driven framework for algorithmic trading, with a focus on crypto currencies.
- [triangular-arbitrage2](https://github.com/zlq4863947/triangular-arbitrage2)   | TypeScript | - a server side application for perform triangular arbitrage.
- [bTrader](https://github.com/gabriel-milan/btrader)   | Rust | - Triangle arbitrage trading bot for Binance
- [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.
- [* Hummingbot](https://github.com/CoinAlpha/hummingbot)   | Python, Cython, Live Trading | - A client for crypto market making
- [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
- [exc](https://github.com/Nouzan/exc)   | Rust | - The abstraction layer of exchanges.
- [MyCryptoBot](https://github.com/diogomatoschaves/MyCryptoBot)   | Python, Js | - Automated, open source crypto trading and backtesting platform
- [TradingGym](https://github.com/Yvictor/TradingGym)   | Python, Live Trading | - Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.
- [Stock Trading Bot using Deep Q-Learning](https://github.com/pskrunner14/trading-bot)   | Python | - Stock Trading Bot using Deep Q-Learning
- [Finance](https://github.com/shashankvemuri/Finance) | `Python` | - 150+ quantitative finance Python programs to help you gather, manipulate, and analyze stock market data
- [volest](https://github.com/jasonstrimpel/volatility-trading) | `Python` | - A complete set of volatility estimators based on Euan Sinclair's Volatility Trading
- [torchquantum](https://github.com/nymath/torchquantum) | `Cython`, `C`, `Python` | - TorchQuantum is a backtesting framework that integrates the structure of PyTorch and WorldQuant's Operator for efficient quantitative financial analysis.
- [OpenAlpha](https://github.com/caoruicn/openalpha) | `C++` | - An open source equity statistical arbitrage backtest simulator, use the same API as WorldQuant's WebSim
- [Genetic-Alpha](https://github.com/Morgansy/Genetic-Alpha) - A genetic programming algorithm used for generating alpha factors in the multi-factor investment strategy
- [Cvxpy](https://github.com/cvxpy/cvxpy)   | Python, C++ | - A Python-embedded modeling language for convex optimization problems.
- [jax](https://github.com/google/jax)   | `Python` | - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
- [Numpy](https://github.com/numpy/numpy)   | Python, C | - The fundamental package for scientific computing with Python
- [trade-frame](https://github.com/rburkholder/trade-frame)   | C++ | - C++ 17 based library (with sample applications) for testing equities, futures, currencies, etfs & options based automated trading ideas using DTN IQFeed real time data feed and Interactive Brokers (IB TWS API) for trade execution. Some support for Alpaca & Phemex. Notifications via Telegram
- [Scipy](https://github.com/scipy/scipy)   | Python, C | - Fundamental algorithms for scientific computing in Python
- [statsmodels](https://github.com/statsmodels/statsmodels/)   - Python module that allows users to explore data, estimate statistical models, and perform statistical tests.
- [PyMC](https://github.com/pymc-devs/pymc)   | Python | - Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara
- [DEAP](https://github.com/DEAP/deap)   |Python| - Distributed Evolutionary Algorithms 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
- [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.
- [FireDucks](https://fireducks-dev.github.io/)  - Compiler Accelerated DataFrame Library for Python with fully-compatible pandas API
- [Hugging Face](https://github.com/huggingface/)  - The AI community building the future.
- [LangChain](https://github.com/langchain-ai/langchain)  - Building applications with LLMs through composability
- [JAX](https://github.com/google/jax)   - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
- [Pytorch](https://github.com/pytorch/pytorch)   | Python | - Tensors and Dynamic neural networks in Python with strong GPU acceleration
- [Keras](https://github.com/keras-team/keras)   | Python | - The most user friendly Deep Learning for humans in Python
- [TensorFlow](https://github.com/tensorflow/tensorflow)   | Python, C++ | - More low level Deep Learning framework
- [Ray](https://github.com/ray-project/ray)   | Python, C++ | - An open source framework that provides a simple, universal API for building distributed applications.
- [csp (Point72)](https://github.com/Point72/csp/tree/main)   | Python, C++ | - csp is a high performance reactive stream processing library, written in C++ and Python
- [Dask](https://github.com/dask/dask)   | Python | - Parallel computing with task scheduling in Python with a Pandas like API
- [Spark](https://github.com/apache/spark)   | Scala | - Apache Spark - A unified analytics engine for large-scale data processing
- [Hamilton](https://github.com/dagworks-inc/hamilton)  | Python | - A scalable general purpose micro-framework for defining dataflows. You can use it to build dataframes, numpy matrices, python objects, ML models, etc. Embed Hamilton anywhere python runs, e.g. spark, airflow, jupyter, fastapi, python scripts, etc.
- [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
- [Joblib](https://github.com/joblib/joblib)   | Python | - running Python functions as pipeline jobs
- [Tributary](https://github.com/timkpaine/tributary)  | Python | - Streaming reactive and dataflow graphs in Python
- [GraphKit(No activity)](https://github.com/yahoo/graphkit)  | Python | - A lightweight Python module for creating and running ordered graphs of computations.
- [Man MDF (No activity)](https://github.com/man-group/mdf)  | Python | - Data-flow programming toolkit for Python
- [Anchors - C++(No activity)](https://github.com/oluwatimilehin/anchors)  | C++ | - C++ library for incremental computing
- [Loman (No activity)](https://github.com/janushendersonassetallocation/loman)  | Python | - Loman is a Python library designed to allow quantitative researchers to control complex live updating calculation processes
- [cython](https://github.com/cython/cython)   - Cython is a Python compiler that makes writing C extensions for Python as easy as Python itself. Cython is based on Pyrex, but supports more cutting edge functionality and optimizations.
- [numba](https://github.com/numba/numba)   - NumPy aware dynamic Python compiler using LLVM
- [pybind11](https://github.com/pybind/pybind11)   - Seamless operability between C++11 and Python
- [pyo3](https://github.com/PyO3/pyo3)   - Rust bindings for the Python interpreter
- [CuPy](https://github.com/cupy/cupy/)   | Python, C++, Cython, Cuda | - CuPy is an open-source array library for GPU-accelerated computing with Python. 100x Boost for some operations
- [codon](https://github.com/exaloop/codon)   | C++ | - A high-performance, zero-overhead, extensible Python compiler using LLVM
- [Bottleneck](https://github.com/pydata/bottleneck)   | Python, C | - Fast NumPy array functions written in C
- [NumExpr](https://github.com/pydata/numexpr)   | Python, C++ | - Fast numerical array expression evaluator for Python, NumPy, PyTables, pandas, bcolz and more
- [pandarallel](https://github.com/nalepae/pandarallel)   | Python | - A simple and efficient tool to parallelize Pandas operations on all available CPUs
- [py-spy](https://github.com/benfred/py-spy)   - Sampling profiler for Python programs
- [pyinstrument](https://github.com/joerick/pyinstrument)   - Call stack profiler for Python. Shows you why your code is slow!
- [Memray](https://github.com/bloomberg/memray)   - Memray is a memory profiler for Python
- [ndarray](https://github.com/rust-ndarray/ndarray)   | Rust | - ndarray: an N-dimensional array with array views, multidimensional slicing, and efficient operations
- [faer](https://github.com/sarah-ek/faer-rs)   | Rust | - Linear algebra foundation for the Rust programming language
- [DataFrame](https://github.com/hosseinmoein/DataFrame)   | C++ | - C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage
- [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
- [Modin](https://github.com/modin-project/modin)   | Python | - Modin: Speed up your Pandas workflows by changing a single line of code
- [Koalas](https://github.com/databricks/koalas)   | Python | - Koalas: pandas API on Apache Spark
- [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.
- [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
- [vollib](https://github.com/vollib/vollib) | `Python` | - Fundamentally a swig/python wrapper around Peter Jaeckel's lets_be_rational. lets_be_rational focuses exclusively on Black76, while Vollib extends this to add support for Black-Scholes and Black-Scholes-Merton.
- [System R](https://agents.systemr.ai) | `Python` | - Risk intelligence API for trading agents. Pre-trade gate with position sizing (G-formula/Kelly), drawdown analysis, Monte Carlo simulation, regime detection. REST API and MCP server.
- [cvxportfolio](https://github.com/cvxgrp/cvxportfolio)   | Python | - Portfolio optimization and back-testing.
- [skfolio](https://github.com/skfolio/skfolio)   | Python | - Python library for portfolio optimization built on top of scikit-learn
- [Riskfolio-Lib](https://github.com/dcajasn/Riskfolio-Lib)   | C++, Python | - Portfolio Optimization and Quantitative Strategic Asset Allocation in Python
- [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.
- [PyPortfolioOpt](https://github.com/robertmartin8/PyPortfolioOpt)   | Python | - Financial portfolio optimizations in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
- [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.
- [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.
- [Matplotlib](https://github.com/matplotlib/matplotlib)   | Python | - matplotlib: plotting with Python
- [Seaborn](https://github.com/mwaskom/seaborn)   | Python | - Statistical data visualization in Python
- [Dash](https://github.com/plotly/dash)   | Python | - Data Apps & Dashboards for Python. No JavaScript Required.
- [Perspective](https://github.com/finos/perspective)   | C++, Python | - A data visualization and analytics component, especially well-suited for large and/or streaming datasets.
- [Streamlit](https://github.com/streamlit/streamlit)   | Python | - Streamlit — A faster way to build and share data apps.
- [gradio](https://github.com/gradio-app/gradio/)   | Python | - Build and share delightful machine learning apps, all in Python.
- [pylatex](https://github.com/JelteF/PyLaTeX/)   | Python | - A Python library for creating LaTeX files
- [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.
- [Kafka](https://github.com/apache/kafka)   | Java | - Mirror of Apache Kafka
- [RedPanda](https://github.com/redpanda-data/redpanda/)   | C++ | - Redpanda is a streaming data platform for developers. Kafka API compatible. 10x faster. No ZooKeeper. No JVM!
- [BlazingMQ](https://github.com/bloomberg/blazingmq)   | C++ | - A modern high-performance open source message queuing system
- [ArcticDB (Man Group)](https://github.com/man-group/ArcticDB) | `C++`, `Python` | - ArcticDB is a high performance, serverless DataFrame database built for the Python Data Science ecosystem.
- [DuckDB](https://github.com/duckdb/duckdb)  | `C++`, `Python` | - ArcticDB is a high performance, serverless DataFrame database built for the Python Data Science ecosystem.
- [pylance](https://github.com/lancedb/lance)  | `Rust` | - Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow
- [Arctic (Man Group)](https://github.com/man-group/arctic)  | `Python` | - High performance datastore for time series and tick data
- [Tectonicdb](https://github.com/0b01/tectonicdb) | `Rust` | - Tectonicdb is a fast, highly compressed standalone database and streaming protocol for order book ticks.
- [FinanceDatabase](https://github.com/JerBouma/FinanceDatabase)  - This is a database of 300.000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets.
- [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
- [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
- [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.
- [StockAInsights](https://stockainsights.com) - Institutional-grade financial statements API with AI extraction from SEC filings — not XBRL. Covers domestic and foreign filers (20-F, 6-K, 40-F), normalized quarterly and annual data.
- [goMacro.ai](https://gomacro.ai) - AI-powered economic calendar with institutional-grade macro insights and scenario planning for NFP, CPI, PPI and other data releases.
- [edgartools](https://github.com/dgunning/edgartools) |`Python`| - SEC EDGAR data for quant strategies — fundamentals, institutional holdings (13F), insider transactions, and corporate events (8-K). Includes MCP server for AI workflows.
- [Cryptofeed](https://github.com/bmoscon/cryptofeed) |`Python`| - Cryptocurrency Exchange Websocket Data Feed Handler with Asyncio
- [Agent Gateway](https://agent-gateway-kappa.vercel.app) | `REST API` | - Free unified REST API providing real-time prices for 500+ crypto tokens via Hyperliquid. No API key required for price data. OpenAPI spec at `/openapi.json`.
- [Parsec](https://github.com/parsecular/parsec-mcp) | `Rust`, `TypeScript`, `Python` | - Prediction market data, execution, and live streams across all major exchanges. [Website](https://parsecapi.com)
- [PENDAX](https://github.com/CompendiumFi/PENDAX-SDK) | `JavaScript` | - A free Javascript library allowing simplified interaction with trading and data commands on a growing list of cryptocurrency exchanges like FTX, OKX, ByBit, & more.
- [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.
- [pmxt](https://github.com/pmxt-dev/pmxt) | `Python`, `JavaScript` | - A JavaScript / Python prediction markets trading API with support for major exchanges. (The ccxt for prediction markets)
- [PolyClawster](https://github.com/al1enjesus/polyclawster) - AI agent skill for trading on Polymarket prediction markets. Non-custodial, whale signal detection, public leaderboard. Works as OpenClaw skill or Telegram Mini App.
- [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.
