Q-agent

An AI-compatible quantitative research workspace — pipelines, notebooks, LEAN strategies, and agent workflows in one reproducible repo.

Docs License: MIT Python 3.11+ Last Commit Stars
Start Here → View on GitHub
📈

QuantConnect & LEAN

Cloud and local backtesting with the LEAN engine. Atomic project structure, shared signals library, and tested workflow patterns for strategy development.

🗄️

Data Pipelines

Ready-made pipelines for crypto, Polymarket, WRDS / CRSP, SEC EDGAR, and yfinance — some LEAN-native, others research-ready for notebooks or custom readers.

📓

Research Notebooks

Interactive Marimo notebooks for signal research, factor analysis, and backtest diagnostics — runnable against real datasets with no cloud dependency.

🤖

AI-Assisted Workflows

Claude Code integration with agent guidelines, tested prompt patterns, and memory-backed workflows for safe, consistent AI-assisted development.

Where should I start?

🎓 I'm a student New to quant finance 📊 I want to build a strategy LEAN backtests and signal research 🔌 I want to add a pipeline Integrate a new data source 🤖 I want to use agents AI-assisted development 🤝 I want to contribute Open source collaboration

Zero to notebook in 3 commands

Clone the repo, install notebook dependencies, and run the included Election & Industry Returns example — no QuantConnect account required.

Full Setup Guide →
Terminal
git clone https://github.com/WolfpackOfOne/Q-agent.git
cd Q-agent
python -m venv infrastructure/marimo/venv
source infrastructure/marimo/venv/bin/activate
pip install -r infrastructure/marimo/requirements.txt
marimo run infrastructure/marimo/notebooks/election_industry_returns.py --port 2719

Used in graduate quantitative finance and trading technology coursework.