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.

English | 中文

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. Analysis output (ratings, reasons) is in Chinese; the tool targets Chinese-speaking investors researching US stocks.

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 seconds

No entry point? python -m quant_agent analyze AAPL works the same.

Related MCP server: mcp-financex

What you get

Real output from quant-ai analyze AAPL. The five ratings range from 强烈看多 (strong buy) through 中性 (neutral) to 强烈看空 (strong sell). Add --output-dir to export Markdown + JSON, or --chart for a price/MA/RSI PNG.

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_server

Then 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 AAPL returns rating, returns, RSI, volatility, MA positions, support/stop levels, and human-readable reasons.

• 🧩 Personalized watchlist — quant-ai init builds 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.

Common commands

How it works

1. Data — daily OHLCV from Yahoo Finance (yfinance) by default, or local CSV/Parquet. Cached and validated.

2. Signals — cross-sectional, point-in-time-lagged factors, z-scored per day.

3. Portfolio & risk — deterministic target weights under position/turnover/liquidity limits.

4. Evaluation — train / validation / test split, walk-forward windows, and benchmark-relative metrics (Sharpe, Sortino, Calmar, max drawdown, alpha/beta).

5. 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.py

Disclaimer

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.

License

MIT