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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.
This article introduces 10 free open-source GitHub repos covering financial data, backtesting, trade execution, etc., enabling retail investors to access tools like those used by Wall Street hedge funds at a low cost.
Open-source A-share quantitative workstation, based on TickFlow data, implements three functions: stock selection, real-time monitoring, and backtesting. Built-in 20 Polars vectorized strategies, supports AI strategy writing and Feishu push.
A tweet highlights how a math genius built an AI system capable of trillions of operations per second for Jane Street, earning a huge salary, and promotes a lecture explaining the methodology.
Stone141319 released a local cryptocurrency quantitative backtesting tool called Hermes Backtest Lab, supporting spot, perpetual swap, and on-chain token backtesting without requiring an API key, and offers various presets and custom parameters.
"XQuant: Everyone Can Be a Quantitative Trader" is an open-source introductory manuscript for quantitative trading aimed at beginners, teaching readers to describe strategy ideas in natural language and have AI write the code, building a systematic quantitative trading system from scratch.
An intern in Guangzhou built a trading bot using Claude and netted $63,000 on Polymarket through high-frequency small-spread trades, demonstrating the application of AI-assisted quantitative trading in prediction markets.
Recommends 4 open-source quantitative trading tools/frameworks (VeighNa, AI-Trader, StockSharp, QuantDinger), emphasizing that they are suitable for ordinary users to conduct US stock quantitative trading, helping to free your hands and let the model handle trading.
A Chinese girl with no quant background used Claude Code over a weekend to create a trading bot covering five instruments, employing mean reversion, breakout, and trend strategies, achieving significant returns, demonstrating the potential of AI tools in automated trading.
A former Citadel quant trader, after being fired, rebuilt the entire trading algorithm using Claude Fable 5 in 48 hours, and through hedge trading on Polymarket, has profited $430,000. The story highlights a probability-based high-frequency trading strategy and the application of the law of large numbers.
Fincept Terminal is a free, open-source financial terminal built with C++20, integrating 37 AI agents (simulating investment masters like Buffett and Munger) and 100+ real-time data sources. It offers professional charts, quantitative backtesting, and risk analysis, aiming to be a free alternative to Bloomberg.
In the past two weeks, the AI Agent ecosystem has expanded from code writing to multiple vertical domains including job hunting, education, video production, security auditing, and quantitative trading. The community has contributed tools such as shushu-internship-tool, hermes-edu-skills, etc., marking the transformation of agents from code-assistance tools to all-around assistants.
Jane Street revealed inside views of its AI training center in Texas, housing 4,032 GPUs, 8,000 kilometers of fiber optics, and a full liquid cooling system, while recounting the 20-year evolution from a humble start with six Dell hosts to today's extreme trading system.