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LLM 驱动的多市场股票智能分析系统：多源行情、实时新闻、决策看板与自动推送，支持零成本定时运行。 LLM-powered multi-market stock analysis system with multi-source market data, real-time news, decision dashboard, automated notifications, and cost-free scheduled runs.

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.

An advanced crypto trading bot written in Python

Portfolio analytics for quants, written in Python

Build your autonomous hedge fund in minutes. AutoHedge harnesses the power of swarm intelligence and AI agents to automate market analysis, risk management, and trade execution.

Free, open source, a high frequency trading and market making backtesting and trading bot, which accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and order books(Level-2 and Level-3), with real-world crypto trading examples for Binance and Bybit

TSP自托管、零运维的 A 股「选股 + 监控 + 回测」量化工作台 | LLM能力驱使策略定制+个股分析+复盘 | 自由接入第三方数据源与个性化扩展数据 | 个人开源 ,非第三方官方项目

We are committed to the open-sourcing quantitative knowledge, aiming to bridge the information gap between the domestic and international quantitative finance industries. 我们致力于量化知识的开源与汉化，打破国内外量化金融行业信息差。

modular quant framework.

ArcticDB is a high performance, serverless DataFrame database built for the Python Data Science ecosystem.

:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

Framework for quantitative trading. Complete framework for development, backtesting, and deploying automated trading algorithms and trading bots.

Various Types of Stock Analysis in Excel, Matlab, Power BI, Python, R, and Tableau

Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy

A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.

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

Quantitative systematic trading strategy development and backtesting in Julia

Vanilla and exotic option pricing library to support quantitative R&D. Focus on pricing interesting/useful models and contracts (including and beyond Black-Scholes), as well as calibration of financial models to market data.

Kelly Criterion calculation

Quantitative factor research skills for AI coding assistants

Time series implementation for the Julia language focused on efficiency and flexibility

AI-powered SDK featuring algorithmic trading, backtesting, deployment on 100+ exchanges, and multiple optimization engines.

Python API for accessing Lake high frequency tick trades & order book data

A Python SDK for FinancialData.Net API - Real-time & Historical Stock Market Data

RL stock selection for China A-share — bundled polars-native factor library (105 Alpha101 + 191 GTJA Alpha191 = 296 factors), board-aware price limits, GPU train + ONNX CPU infer, MIT-licensed.

Python algorithmic trading bot framework for Kubernetes: backtesting, hyperparameter optimization, 150+ technical analysis indicators (RSI, MACD, Bollinger Bands, ADX), portfolio management, PostgreSQL integration, Helm deployment, CronJob scheduling. Minimal overhead, production-ready, Yahoo Finance data.

Julia Incremental Technical Analysis Indicators (inspired by talipp)