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An introduction to the awesome-systematic-trading open-source project on GitHub (5.3K Stars), which curates the market data, backtesting, live trading, AI quant, and risk management tools needed for quantitative trading into a comprehensive navigation index, covering markets such as stocks, futures, options, and cryptocurrencies.
The author explains how to build a 24/7 AI financial trading agent using a combination of Minara and Jev, claiming to have turned 100U of capital into a 174% return in 7 days, and shares a complete workflow covering data acquisition, decision-making, backtesting, risk control, and execution.
This post highlights an MIT quant paper showing how AI speeds up trading research but also makes fake alpha easier to believe, arguing AI should serve as a strategy rejection machine rather than a trading oracle.
作者用 AI 代理回测了一个策略：每月买入上月表现最好的股票，自 1998 年以来 1000 美元可增长到 880 万美元，远超标普 500 的 220 万美元（但收益主要来自苹果、英伟达等少数离群股）。整个实验是用 Agents API 构建的。
HKU's lab open-sourced Vibe-Trading, an MIT-licensed financial research platform with 34K+ GitHub stars that lets users run full quant research chains—from data sourcing and factor mining to multi-agent backtests—entirely via natural language.
This study evaluates whether test-time reasoning in large language models (like DeepSeek, GPT, and Gemini) improves net portfolio returns in trading, finding that additional reasoning does not reliably enhance economic outcomes across various conditions.
The author built an AI trading agent but found that using LLM for direct trading decisions is unstable, leading to a deterministic system where LLM translates strategies into explicit rules for execution.
This article recommends Minara AI, an AI agent designed to help ordinary people trade US stocks by providing features like a powerful database, market dashboard, built-in strategies, backtesting, and a Wall Street research report analysis team.
GPT-6 Astra optimized a Rust backtesting engine using a 2014 math paper, solving a problem other AI models couldn't, and the author argues this indicates imminent AGI.
Indicator Go is a Go library that provides 80+ technical analysis indicators, a backtesting framework, and integrates with AI tools via MCP for algorithmic trading.
Horizon is an AI-powered tool that allows traders to describe trading strategies in plain language, automatically generating backtests with detailed metrics for iterative testing and improvement.
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.
The paper explains how removing market beta and factor exposure reveals true trading signals, emphasizing rigorous stress testing to avoid overfitting and false confidence in backtests.
The article argues that AI trading agents should include a skeptic agent to check for flaws like data leakage and impossible assumptions, rather than just generating more strategies.
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.
The author built PortfolioLab, an AI agent pipeline for trading that stages models through backtesting, paper trading, and read-only API execution to prevent premature exposure to real money. They are seeking feedback on trust patterns in AI agent architectures for high-stakes applications.
The user is open-sourcing their profitable quantitative trading code and introducing the QTrader platform, which offers strategy management, backtesting, and simulated trading features.
AI-Trader is an open-source Python framework that simplifies backtesting and optimizing algorithmic trading strategies across multiple markets using config-driven YAML files and AI assistant integration.
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.
Presents Backtrader-Bench, a benchmark for evaluating LLM coding agents in algorithmic trading using self-generated multiple-choice questions and a generator-solver filtering pipeline, showing tool-augmented agents achieve 90% accuracy.