60,121 visible repositories in the index
1–23 of 23 · Most stars
Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.
TranslatedAn open-source quantitative trading platform development framework based on Python
FinceptTerminal is a modern finance application offering advanced market analytics, investment research, and economic data tools, designed for interactive exploration and data-driven decision-making in a user-friendly environment.
Translated[🔥updating ...] AI automated quantitative trading robot (fully local deployment) AI-powered Quantitative Investment Research Platform. 📃 online docs: https://ufund-me.github.io/Qbot ✨ :news: qbot-mini: https://github.com/Charmve/iQuant
The most complete open-source, self-hosted Agentic Trading OS—AI research, Python strategies, factor discovery, backtesting, optimization, paper/live multi-asset execution, risk controls, monitoring, MCP, and multi-tenant SaaS in one auditable stack.
TranslatedA-share data for AI agents: K-lines, ticks, reports, fund flow, news, financials, filings, options, macro, futures, events, convertibles | 15 layers·87 endpoints·34 sources. Full-stack A-share data toolkit: market quotes and K-lines · same-day tick-by-tick trades · research reports · signals · capital flow · news · financials · announcements · limit-up board trading (打板) · ETF options · public sentiment · macro and interest rates · futures and block trades (including Dalian Commodity Exchange daily K-lines) · event-driven · convertible bonds | No API key needed except for iwencai
ValueCell is a community-driven, multi-agent platform for financial applications.
A beautiful, private, local-first personal finance tracker. Investments, net worth, spending, and simulations.
🔎 📈 🐍 💰 Backtest trading strategies in Python.
Financial portfolio optimization in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
Portfolio Optimization in Python
Track and evaluate the performance of your investment portfolio across stocks, cryptocurrencies, and other assets.
DojoAgents: Full-Market AI Copilot for Personal Investment
An open-source, high-performance quantitative trading framework in C++/Python focused on strategy analysis and backtesting Trading model R&D · Ultra-fast engine · Efficient backtesting
Algorithmic Trading in Python with Machine Learning
TranslatedVibe-Research: A research workbench for China A-shares, US and HK stocks · watchlist and holdings, daily review, industry news, industry research, in-depth stock analysis, backtesting, research report library, 168 data endpoints. Supports Claude Code, Codex, WorkBuddy subscriptions or any model API.
A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.
Python library for portfolio optimization, factor model construction, and risk management built on scikit-learn
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.
A composable, real time, market data and trade execution toolkit. Built with Elixir, runs on the Erlang virtual machine
Self-hosted alternative to Claude and ChatGPT Personal Finance. An AI agent that manages your budget and investments, and can place trades.
Translated研迹 Trail (Yanji, "research trail"): a local-first AI investment research workbench. Connects market quotes, candlestick charts, and deep research, so that judgments are traceable and risks can be reviewed. Local-first AI investment research desktop with an evidence-grounded copilot.
Translated知策 (Zhice, "know strategy") | AI investment research and strategy experiment workbench: view markets and news, compare strategy judgments; organize investment documents into knowledge backed by source-text evidence, form rules through manual confirmation, screen candidates, and verify them in independent simulated accounts.