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TradingAgents is an open-source multi-agent framework for AI-driven financial trading, supporting multiple AI models and data sources, with checkpointing functionality.
Mathematician Yilin Wang's work on Loewner energy quantifies how far curves deviate from randomness, aiding Citadel in distinguishing true market signals from noise with $500,000 rewards for precise computation.
The author built an autonomous swing trading pipeline using Hermes AI that screens stocks, tracks performance, and self-validates strategy changes every 48 hours, achieving a 60% win rate over two months.
This GitHub repository named finance-quant-skills provides 13 installable Agent Skills specifically designed for A-share quantitative trading, covering functions such as data acquisition, strategy backtesting, and document query.
Tonghuashun officially open-sources its A-share data service, providing comprehensive market data through a single API Key, with native support for MCP and Agent Skill, facilitating use by AI Agents and quantitative development.
NautilusTrader is a high-performance, open-source trading engine with a Rust core and Python strategy support, enabling seamless transition from backtesting to live trading across multiple assets and exchanges.
This article introduces eight GitHub repositories covering AI side hustles, remote work, quantitative trading, and more, helping users leverage technical resources to increase income.
This article provides a detailed introduction to the InStock stock system, an open-source quantitative investment tool that supports data fetching, technical indicator calculation, K-line pattern recognition, and strategy backtesting, among other functions.
The user is open-sourcing their profitable quantitative trading code and introducing the QTrader platform, which offers strategy management, backtesting, and simulated trading features.
Jane Street, a quantitative trading firm, suffered a $15 billion financial loss following a meltdown incident involving its Situational Awareness system.
This article introduces a GitHub repository called awesome-systematic-trading, which categorizes free open-source quantitative trading libraries and resources by programming language, helping developers quickly find suitable tools.
AQuA is a research system with two independent language-model-driven agents that recursively self-improve in quantitative trading research, achieving strong information coefficients on crypto and US equities while using sealed sandboxes to prevent data leakage.
Introduces a full-stack hands-on Python quantitative trading video tutorial, covering data scraping, machine learning prediction, portfolio optimization, and strategy backtesting, and mentions the open-source quantitative trading execution system Kungfu.
This tweet recommends four high-Star open-source financial analysis tools that can replace Bloomberg Terminal, covering daily briefings, professional terminals, algorithmic trading, and in-depth research, all deployable for free on GitHub.
Vibe-Trading is a personal AI trading agent from the HKUDS team at HKU, supporting natural language generation of quantitative strategies, one-click backtesting, and multi-agent collaboration. It comes with an embedded library of 456 alpha factors and is open-sourced.
Proposes EVOQUANT, a self-evolving framework that uses LLMs and a verifier pipeline to automate quantitative trading strategy optimization, achieving significant Sharpe ratio improvements across A-share and crypto markets.
Alpha-Dojo/DojoAgents is a full-market personal investment AI copilot framework that supports automated analysis and strategy backtesting for stocks, forex, and cryptocurrencies. It is fully open-source and aims to give retail investors access to institutional-grade quantitative tools.
This article details how to build a personal quantitative trading system using free AI open-source tools (such as OpenBB, Qlib, TradingAgents, etc.), covering five major modules: data, research, backtesting, risk control, and execution, and points out common pitfalls and discipline.
A curated list of open-source quantitative trading projects on GitHub, including AI-powered platforms like Qlib and FinGPT, multi-agent frameworks, and backtesting tools, all with tutorials and ready-to-use strategies.
This article details the learning path for an ordinary person to become a quantitative trader, covering five stages: probability, statistics, linear algebra, calculus, and stochastic calculus. It also explains the industry's compensation structure, interview requirements, and the rapid growth of AI/ML positions.