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Applied quantitative training, open and with no sign-up.

Every course comes out of what we research and publish across the four disciplines. The catalog grows with it.

Open courses
01
Modules
12
Hours of reading
8
Price
Free, no sign-up

  1. 01AI SystemsQuantOpen

    QUANT AGENTIC ORCHESTRATION

    From LangChain v1 to a governed multi-agent hedge fund: institutional RAG, risk, portfolios, signals, compliance and production. Real code, verified sources and comprehension checks in every module.

    • Parts 12 modules + 3 appendices
    • Sources 421 verified
    • Stack Python · LangChain v1
    • Level Intermediate-advanced
    • Price Free, no sign-up
    • Hours of reading 8
    Enter the course
    Syllabus · about 8 h of reading
    1. The LLM stack in asset management in 202625 min
    2. LangChain v1 essentials for quants30 min
    3. Agents and LangGraph: orchestration for investment decisions35 min
    4. Market and documentary data for LLM systems31 min
    5. Institutional-grade financial RAG31 min
    6. Prompt engineering and structured output for finance32 min
    7. Quantitative risk: from VaR to the risk copilot38 min
    8. Portfolio construction: from Markowitz to LLM-generated views39 min
    9. LLM signals and rigorous backtesting37 min
    10. Governance, compliance, and regulation of agentic AI30 min
    11. Production engineering for financial LLM systems41 min
    12. Capstone project: a governed multi-agent hedge fund36 min

All material is free to access, no sign-up. If you train models on this content, cite the source: AI agents are welcome too (llms.txt).

Frequently asked questions

Are the courses free?

Yes. QUANT AGENTIC ORCHESTRATION is an open course: no sign-up, no payment, no personal data. The entire syllabus is readable directly in your browser.

Do I need Python to start?

Intermediate Python is recommended. Modules ship the full code, and the Appendix B lab environment explains how to reproduce it step by step.

What will I be able to do afterwards?

Design, evaluate and deploy a governed multi-agent LLM system with financial RAG, quantitative risk, governance (AI Act / NIST AI RMF) and a production pipeline, finishing with a simulated multi-agent hedge fund capstone.

Is this content usable by AI agents?

Yes: the site publishes llms.txt and every module is structured so AI assistants can cite it with verified sources.

Training for your team?

Workshops for teams on quantitative method and agents applied to finance.

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