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quant.ai
Quant research for US stocks, built to live inside your AI assistant. Plug it into Claude or Codex via MCP and ask about any ticker — get explainable ratings, key levels, and the reasoning, then discuss it in the same chat. Also works as a one-command CLI.
quant.ai is a command-line quant-research toolkit for US equities, built for everyday investors. Type one command and get an explainable rating, key support/stop levels, and the reasoning behind them — all derived from historical prices, fully offline-friendly, and with no brokerage attached.
⚠️ Research only — not investment advice. It never places or suggests live orders. Output is English by default; add--lang zh(or pick atquant-ai init) for Chinese (中文).
Quick start
pip install -e . # or: pip install -r requirements.txt
quant-ai doctor # environment self-check (deps + data connectivity)
quant-ai analyze AAPL # rate one stock in secondsNo entry point? python -m quant_agent analyze AAPL works the same.
What you get
<img src="assets/analyze-example.svg" alt="Real output of quant-ai analyze AAPL" width="820">
Real output from quant-ai analyze AAPL — English by default, --lang zh for Chinese. Ratings range from Strong Buy through Neutral to Strong Sell. Add --output-dir to export Markdown + JSON, or --chart for a PNG chart.
Use it inside Claude or Codex
The headline feature — expose quant.ai as an MCP server and let your AI assistant call it:
claude mcp add quant-research -- python -m quant_agent.mcp_serverThen just ask, in the same chat where you work:
"What's the read on NVDA?" · "Any long-term buy candidates today?" · "Compare AAPL and MSFT."
Claude (or Codex) calls into real project data, shows you the quant analysis, and you discuss it inline — no separate website, no copy-pasting. See README_CN.md for Claude Desktop / Codex config.
Highlights
- 🤖 Lives inside your AI assistant — the headline feature. Plug the built-in MCP server into Claude or Codex and ask "what's the read on NVDA?" It pulls real quant analysis from this project, so you see the analysis and discuss it in one conversation. Most stock-analysis tools are standalone websites — this one is embedded in the AI you already chat with.
- 🎯 Zero-config single-stock analysis —
analyze AAPLreturns rating, returns, RSI, volatility, MA positions, support/stop levels, and human-readable reasons. - 🧩 Personalized watchlist —
quant-ai initbuilds a universe that is 2/3 your own picks (companies + sectors you care about) and 1/3 discovered by the engine from the wider market. - 🔬 Research backtests — cross-sectional signals (12-1 momentum, 20/50 trend, 1-month reversal, low-vol), signal-weight search, walk-forward stability analysis, plus SPY and equal-weight baselines to separate alpha from beta.
- 📊 Local dashboard & daily market report — a no-key market-intelligence brief and an interactive Markets dashboard, served locally.
- 🛡️ Safe by design — deterministic signals + risk layer, friendly degradation on network/data errors, paper trading only — never submits real orders.
Daily market report & dashboard
quant-ai market-report builds a daily US-equity research brief — market overview, watch / high-risk lists, quant picks by holding horizon, and free news headlines — in an Anthropic-style design:
<img src="assets/market-report-example.png" alt="Daily market report" width="820">
quant-ai serve-dashboard (or write-dashboard) renders backtest diagnostics — headline metrics, alerts, period breakdown, risk checks, positions and trades — with a built-in EN / 中文 toggle:
<img src="assets/dashboard-example.png" alt="Local dashboard" width="820">
Common commands
| Command | What it does |
|---|---|
quant-ai analyze AAPL MSFT NVDA | Rate one or more stocks |
quant-ai analyze --file watchlist.txt | Rate symbols from a file |
quant-ai init | Build a personalized watchlist (interactive) |
quant-ai analyze --watchlist | Rate your personalized watchlist |
quant-ai run-backtest --config configs/default.yaml | Run the research backtest |
quant-ai market-report | Generate the daily market-intel report |
quant-ai serve-dashboard | Start the local dashboard service |
quant-ai doctor | Environment self-check |
How it works
- Data — daily OHLCV from Yahoo Finance (
yfinance) by default, or local CSV/Parquet. Cached and validated. - Signals — cross-sectional, point-in-time-lagged factors, z-scored per day.
- Portfolio & risk — deterministic target weights under position/turnover/liquidity limits.
- Evaluation — train / validation / test split, walk-forward windows, and benchmark-relative metrics (Sharpe, Sortino, Calmar, max drawdown, alpha/beta).
- AI (optional) — an LLM only reviews and narrates research; it never generates orders. Falls back to an offline template when no API key is set.
Documentation
- Full manual (Chinese): README_CN.md — detailed config, backtest/walk-forward, dashboard API, MCP integration, output files.
- Changelog: CHANGELOG.md · Contributing: CONTRIBUTING.md · Roadmap: roadmap.md
Tests
python -m pytest # 47 tests, network-free
python -m ruff check quant_agent tests conftest.pyDisclaimer
This project is for quantitative research and education only. It analyzes historical prices and produces research signals — not investment advice, and not authorization to trade. Markets carry risk; you are responsible for your own decisions.