Topics / #quantitative-finance

Quantitative-finance projects on GitHub

Quant methods for markets: backtesting, factor models, and trading systems.

As of , the most-starred Quantitative-finance projects on GitHub are TradingAgents (111.6K stars), OpenBB (74.3K stars) and daily_stock_analysis (66.5K stars). GitHub.Locker tracks 51 Quantitative-finance repositories with 1M stars combined. The fastest-growing on October 16 was TradingAgents, with +155 stars in a day.

51Projects
1MStars in total
+1.2KStars in a day

Hot right now

Most stars gained in a day · Oct 16

The all-time top

51 projects · by stars

  1. #1 · TauricResearch/TradingAgents

    TradingAgents

    TradingAgents is a multi-agent LLM framework that simulates a trading firm: specialized agents analyze fundamentals, sentiment, news, and technicals, bull/bear researchers debate the findings, and a trader plus risk/portfolio manager team decide whether to execute the trade.

    111.6K
  2. #2 · openbq-org/OpenBB

    OpenBB

    Open Data Platform (ODP) by OpenBB is an open-source toolset that lets you integrate proprietary, licensed, and public financial data sources once and then consume them everywhere: in Python, Excel, OpenBB Workspace, AI agents via MCP, or REST APIs.

    74.3K
  3. #3 · ZhuLinsen/daily_stock_analysis

    daily_stock_analysis

    An AI-driven stock analysis system that pulls market data and news for A-share, Hong Kong, US, Japanese, Korean and Taiwanese stocks, then generates a daily AI decision report and pushes it to WeChat Work, Feishu, Telegram, Discord, Slack or email.

    66.5K
  1. 4

    ai-hedge-fund

    64.1Kvirattt/ai-hedge-fund · Python

    An AI-powered hedge fund proof of concept that uses AI (LLM-driven 'alpha models') to make simulated trading decisions. It's explicitly for educational and research purposes, not real trading or investment.

  2. 5

    qlib

    49.4Kmicrosoft/qlib · Python

    Qlib is Microsoft's open-source, AI-oriented quantitative investment platform that covers the full quant research pipeline — data processing, model training, backtesting, and order execution — using supervised learning, market-dynamics modeling, and reinforcement learning.

  3. 6

    Summer2027-Internships

    48KSimplifyJobs/Summer2027-Internships · Python

    A community-maintained list of Summer 2027 tech internships across software engineering, data science/AI, quant, product management, and hardware, updated daily by Pitt CSC and Simplify.

  4. 7

    Kronos

    40.9Kshiyu-coder/Kronos · Python

    Kronos is an open-source foundation model for financial candlestick (K-line) data, pretrained on data from over 45 global exchanges to produce price/volume forecasts.

  5. 8

    Vibe-Trading

    35.7KHKUDS/Vibe-Trading · Python

    Vibe-Trading is a personal trading agent toolkit that gives an AI agent full trading capabilities in one command — market data, backtesting, a quant research library, and order execution across many brokers and markets.

  6. 9

    FinceptTerminal

    32.6KFincept-Corporation/FinceptTerminal · C++

    Fincept Terminal is a free, open-source desktop finance terminal in the spirit of a Bloomberg terminal: institutional-grade market analytics, AI research agents and trading tools combined in one application.

  7. 10

    TradingAgents-CN

    32.3Khsliuping/TradingAgents-CN · Python

    TradingAgents-CN is a Chinese-enhanced learning and research platform built on a multi-agent LLM trading framework, helping Chinese-speaking users systematically study stock analysis using multiple AI agents for A-share, Hong Kong, and US markets. It explicitly does not issue real trading orders and is positioned for education and research only.

  8. 11

    nautilus_trader

    30.2Knautechsystems/nautilus_trader · Rust

    A production-grade algorithmic trading engine: a Rust-native event-driven core driven from Python. Backtesting and live trading share the same architecture and execution semantics, so strategies go from research to production without a rewrite.

  9. 12

    awesome-quant

    30.2Kwilsonfreitas/awesome-quant · HTML

    A curated list of libraries, packages and resources for quants - people doing quantitative finance: pricing, trading, backtesting, risk and market data.

  10. 13

    backtrader

    23.5Kmementum/backtrader · Python

    backtrader is a Python library for backtesting trading strategies: you write a strategy as code, run it against historical market data and measure how it would have performed before risking real money.

  11. 14

    akshare

    23Kakfamily/akshare · Python

    AKShare is a free, open-source Python library that wraps a large set of financial data interfaces behind simple function calls - one line of code is enough to fetch a dataset.

  12. 15

    Lean

    22KQuantConnect/Lean · C#

    LEAN is QuantConnect's open-source, event-driven algorithmic trading engine, written in C# with Python support. You use it to build, backtest and live-trade strategies, with alternative data support out of the box.

  13. 16

    FinGPT

    21.4KAI4Finance-Foundation/FinGPT · Jupyter Notebook

    FinGPT is AI4Finance Foundation's open-source project for financial large language models: trained models are released on HuggingFace, with code for RAG, fine-tuning pipelines and benchmarks built around them.

  14. 17

    machine-learning-for-trading

    21.4Kstefan-jansen/machine-learning-for-trading · Jupyter Notebook

    The code repository for Machine Learning for Trading, 3rd Edition by Stefan Jansen: an end-to-end workflow for building, testing, and deploying ML-driven trading strategies, from data sourcing to live execution.

  15. 18

    hummingbot

    20.5Khummingbot/hummingbot · Python

    An open-source Python framework for building and deploying automated crypto trading bots across centralized and decentralized exchanges. Its users generated over $34 billion in volume across 140+ venues in the past year, and it is free under the Apache 2.0 license.

  16. 19

    zipline

    20.2Kquantopian/zipline · Python

    Zipline is a Pythonic algorithmic trading library — an event-driven system for backtesting trading strategies written in Python. It is used in production as the backtesting and live-trading engine behind Quantopian.

  17. 20

    abu

    18.9Kbbfamily/abu · Python

    This is 'abu' (阿布量化), an open-source Python framework for building, backtesting and running quantitative trading strategies across US/A-share/HK stocks, futures, options and cryptocurrencies like Bitcoin, using machine learning to optimize the strategies.

  18. 21

    Qbot

    18.7KUFund-Me/Qbot · Jupyter Notebook

    Qbot is a free, AI-oriented quantitative investment research platform that covers the whole loop: data acquisition, strategy development, backtesting, simulated trading, and live trading.

  19. 22

    New-Grad-Positions

    18.2KSimplifyJobs/New-Grad-Positions

    A curated list of full-time job openings for new college graduates, covering software engineering, quant, and product management roles at companies that are hiring.

  20. 23

    questdb

    17.4Kquestdb/questdb · Java

    QuestDB is an open-source, low-latency time-series database on open formats: it ingests millions of events per second, computes on live data in milliseconds, and keeps years of history queryable through one SQL engine.

  21. 24

    FinRL

    16.7KAI4Finance-Foundation/FinRL · Jupyter Notebook

    FinRL is the original open-source framework for financial reinforcement learning, letting you train deep RL agents (A2C, DDPG, PPO, TD3, SAC) to trade stocks and other assets. It's built around a train-test-trade pipeline split into market environments, DRL agents, and financial applications.

  22. 25

    tushare

    15.5Kwaditu/tushare · Python

    TuShare is a Python utility for crawling, cleaning and storing historical and realtime data for China's stock and futures markets, built for quant analysts and anyone learning data analysis.

  23. 26

    RD-Agent

    15Kmicrosoft/RD-Agent · Python

    R&D-Agent is a Microsoft multi-agent framework that automates data-driven R&D: one agent proposes new research ideas and another implements them, applied to quant trading, data mining, Kaggle competitions, paper reproduction, and LLM fine-tuning.

  24. 27

    stock

    14.8Kmyhhub/stock · Python

    It is a Python-based A-share stock quant system that scrapes daily stock and ETF data, computes technical indicators and chip (cost) distribution, recognizes 61 K-line patterns, runs multi-factor stock screening and strategies, backtests them, and can place automated trades, all viewable from a web UI on PC or mobile.

  25. 28

    awesome-systematic-trading

    14.8Kpaperswithbacktest/awesome-systematic-trading · Python

    A curated list of resources for systematic (quantitative) trading: papers, libraries, packages, books, articles, videos, blogs and courses for finding, developing and running trading strategies.

  26. 29

    python-training

    14.3Kjpmorganchase/python-training · Jupyter Notebook

    This is a hands-on Python training course built by J.P. Morgan for business analysts, traders, and select institutional clients. It teaches numerical computing and data visualization in Python through a series of Jupyter notebooks rather than being a full computer-science curriculum.

  27. 30

    gs-quant

    13.2Kgoldmansachs/gs-quant · Python

    GS Quant is a Python toolkit for quantitative finance built by Goldman Sachs quants on top of their risk transfer platform, used for derivative structuring, trading strategy development, and risk management.

  28. 31

    QuantDinger

    12.9KOpenByteInc/QuantDinger · Python

    QuantDinger is an open-source, self-hosted AI Trading OS: it lets you research trading ideas with AI, write Python strategies on a Strategy API, backtest them server-side, and run paper or live trading across crypto exchanges and traditional brokers, with web, mobile, API, and MCP access.

  29. 32

    ta-lib-python

    12.3KTA-Lib/ta-lib-python · Cython

    A Python wrapper around the TA-Lib C library for technical analysis of financial market data: 150+ indicators such as RSI, MACD and Bollinger Bands, plus candlestick pattern recognition.

  30. 33

    QUANTAXIS

    11.3Kyutiansut/QUANTAXIS · Python

    QUANTAXIS is a local-first quantitative trading framework for China's stock, futures and options markets, covering data fetching, backtesting, paper trading, live trading, visualization and multi-account management, with support for distributed task scheduling.

  31. 34

    quant-trading

    11Kje-suis-tm/quant-trading · Python

    This is a Python repository of quantitative trading strategies — a collection of standalone scripts implementing technical indicators, pattern recognition, options strategies and statistical arbitrage for backtesting on historical market data.

  32. 35

    a-stock-data

    10.9Ksimonlin1212/a-stock-data · Python

    It is a self-contained Skill file that bundles raw A-share (Chinese stock market) data from more than two dozen sources into a ready-to-use tool set for AI coding assistants, covering K-lines, research reports, fund flow, news, financials, filings, limit-up boards, ETF options and macro data.

  33. 36

    StockSharp

    10.9KStockSharp/StockSharp · C#

    StockSharp (S#) is a free, open-source platform for trading on any market worldwide - crypto exchanges, stocks, futures, options, forex, and Bitcoin - supporting both manual and algorithmic trading, including high-frequency strategies.

  34. 37

    UZI-Skill

    7.3Kwbh604/UZI-Skill · Python

    A free skill that turns your AI coding agent into a personal stock analyst: give it a ticker and it pulls 22 dimensions of data, runs 22 institutional methods (DCF, comps, LBO), lets 66 investor personas vote, and outputs a ~600KB Bloomberg-style HTML report in 5-8 minutes. Covers A-shares, Hong Kong and US stocks.

  35. 38

    prediction-market-analysis

    3.9KJon-Becker/prediction-market-analysis · Python

    It's a framework for collecting and analyzing prediction market data from Polymarket and Kalshi, bundled with the largest publicly available dataset of their market and trade history.

  36. 39

    investing-for-beginners

    3.8Kxuchonglang/investing-for-beginners

    A free Chinese-language guide that builds a knowledge framework for beginners on US stocks, options, and cryptocurrency, written by one author (徐冲浪) rather than compiled from external sources.

  37. 40

    skills

    3.7KAlphaGBM/skills · Python

    AlphaGBM Skills is a collection of 29 open-source Skills that bring real-time market research workflows for stocks, options, commodities and digital assets into AI tools like Claude Code and Cursor.

  38. 41

    easy-stock

    1.5Kjundizhou/easy-stock · Go

    easy-stock is an AI-native desktop research workbench for China's A-share market: it aggregates real-time quotes, sector/limit-up data and influencer commentary, then uses AI agents to produce stock analysis, portfolio inspections and after-market reviews for individual investors.

  39. 42

    ALTA

    1.1Kkyky2347/ALTA · Python

    ALTA is a research-only multi-agent LLM trading platform that turns weak market signals into evidence-backed opportunities, debates them with independent agent roles, runs them through a separate risk audit, and only ever executes simulated or explicitly authorized paper trades.

  40. 43

    anti-gambling-trader-tw

    1.1Kmars-tw/anti-gambling-trader-tw · Python

    A free, open-source Python tool that analyzes trading records (Taiwan stocks, US stocks, crypto, forex) to statistically test whether your profits are a repeatable edge or just luck plus survivorship bias, scans for scam language and fake performance claims, and can scaffold backtestable or paper-trading automation projects.

  41. 44

    bytex

    886BYTEX-TRADE/bytex · C#

    BYTEX is an open-source, event-driven algorithmic trading engine for .NET, written in C#. You write a trading strategy once and run the same code unchanged in backtesting, sandbox simulation, and live trading against real exchange accounts.

  42. 45

    fund-guy-skill

    597wbh604/fund-guy-skill · HTML

    A fund-manager behavior audit skill for AI agents: give it a Chinese mutual fund code and it pulls every buy/sell the manager made over 8 years, checks each trade against the real 12-month-later price action, and outputs a single 700KB interactive HTML report with candlestick battle replays, buy/sell autopsies, independence scoring and 'star manager' red-flag detection.

  43. 46

    CNEquity

    571rootSunc/CNEquity · Python

    之后每天运行一次 cne run daily 保持更新。想要更长的历史,或了解 init 具体做了什么,见初始化指南。

  44. 47

    trade-journal

    500LuxAlgo/trade-journal · TypeScript

    Trade Journal is an open-source, self-hosted trading journal that turns broker syncs, statement imports, or manual entries into round-trip trades, a P&L calendar, deep analytics, and an AI-assisted daily journal.

  45. 48

    ict-trading-model

    314carreypro/ict-trading-model · Python

    ICT Trading Model is a Codex skill that combines multi-timeframe bias, liquidity mapping, premium/discount location, session context, displacement, market structure, and PD arrays into a strict ICT/SMC (Inner Circle Trader / Smart Money Concepts) setup-validation framework.

  46. 49

    deepseek-harness-quant

    248yuanwang589-dev/deepseek-harness-quant · Python

    A natural-language-driven, low-frequency quantitative trading decision system for China's A-share market. It combines a subjective Pitch decision chain, a hardcoded factor engine, and strict data-based arbitration, with the AI forbidden from predicting individual stock moves.

  47. 50

    openchart

    238longsurf-ai/openchart · TypeScript

    OpenChart is an open-source trading workspace that brings AI agents into charting, technical analysis, market alerts, and research, letting you use your own Claude, Codex, or Gemini models to analyze markets.

  48. 51

    bytex

    212BYTEX-TRADE/bytex · C#

    BYTEX is an open-source, event-driven algorithmic trading engine for .NET written in C#. You write a trading strategy once and run the exact same code unchanged against historical data (backtest), live data with simulated fills (sandbox/paper), or real exchange accounts (live), with fixed-point money and adapters for major crypto exchanges.

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