{"repo":"OnePunchMonk/AgentQuant","free":true,"listed":false,"github":"https://github.com/OnePunchMonk/AgentQuant","clone":"git clone https://github.com/OnePunchMonk/AgentQuant.git","description":"Autonomous quantitative trading research platform that transforms stock lists into fully backtested strategies using AI agents, real market data, and mathematical formulations, all without requiring any coding.","language":"Python","stars":172,"topics":["langchain","langgraph","portfolio-optimization","autonomous-agent","agentic-ai","fintech","quantitative-finance","vectorbt","finance-ai","quantitative-research"],"license":null,"category":"trading","readme_excerpt":"AgentQuant: Autonomous Quantitative Research Agent A fully autonomous AI agent that researches, generates, validates, and remembers trading strategies. --- What This Is AgentQuant is a regime-adaptive research platform that runs a real ReAct agent loop — not a prompt template. Each run: 1. Analyzes the current market regime using VIX percentile (relative, not absolute thresholds), multi-horizon momentum, and SMA trend signals. 2. Hypothesizes strategy parameters via a LLM → Grid Search → Random fallback chain, constrained to a canonical ParameterGrid so comparisons are scientific. 3. Backtests all proposals in a tournament, computing Sharpe, Calmar, Sortino, max drawdown, and bootstrapped Sharpe (p5). 4. Reflects on results and retries if Sharpe is below the configured threshold (up to max iterations times). 5. Stores the best result to SQLite memory so future runs can recall what worked in similar regimes. Every completed run now emits a screenshot-friendly regime card and a transparent candidate table with pass/watch/reject verdicts, Sharpe, Calmar, Sortino, max drawdown, and bootstrapped Sharpe p5. --- Platform Preview Live Data Selection Choose a date range, select preset stocks/ETFs, or type any yfinance ticker. AgentQuant fetches data on demand and only uses the local cache when it covers the requested range. Research Workspace The dashboard tracks experiment runs, baselines, robustness scores, validation checks, and report-ready research notes in one place. Alpha + NLA","default_branch":null,"files":null,"tree":[],"storefront":"/r/OnePunchMonk","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/OnePunchMonk/AgentQuant/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}