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Learn-Quant

Master quantitative finance, algorithmic trading and professional Python — one runnable lesson at a time.

121 self-contained modules · 135 Python files · 7 JavaScript modules · MIT licensed

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Why Learn-Quant?

  • Every folder runs

    No frameworks to install, no notebooks to wire up. Each module is a single folder you can run from the command line and read top to bottom.

  • Theory and code

    The maths is explained, then implemented from first principles — you see the formula and the line of code that makes it real.

  • A real curriculum

    Seven levels take you from print("hello") to GARCH volatility models, Black-Litterman allocation and reinforcement-learning agents.

  • Tested & linted

    Modules ship with unit tests and pass ruff in CI, so the code you learn from is the code you can trust.

Explore by topic

  • Python Fundamentals

    Core Python for financial analysis — start here if you are new to code.

    10 modules

  • Data Structures

    The right container for the job: arrays, lists, dicts, sets on market data.

    4 modules

  • Algorithms

    Classic computer-science algorithms applied to price and order data.

    8 modules

  • Advanced Python

    Production engineering: async, OOP, concurrency, resilient error handling.

    6 modules

  • Quantitative Methods

    The mathematics underpinning modern finance, implemented from first principles.

    22 modules

  • Options, Derivatives & Finance

    Pricing, Greeks, fixed income and valuation of financial instruments.

    27 modules

  • Risk & Performance

    Measure what can go wrong and how well a strategy actually performed.

    8 modules

  • Portfolio Management

    Construct, optimise and rebalance multi-asset portfolios.

    6 modules

  • Strategies

    End-to-end trading strategies with signals, backtests and execution.

    8 modules

  • AI & Machine Learning

    Data-driven models: random forests, deep learning, RL and NLP for markets.

    10 modules

  • Market Microstructure

    Order books, spreads and the low-latency mechanics of how trades happen.

    2 modules

  • Utilities & Tools

    The plumbing: data ingestion, logging, FX, calendars and helpers.

    10 modules

A guided path

flowchart LR
    A[Python Fundamentals] --> B[Data Structures & Algorithms]
    B --> C[Advanced Python]
    C --> D[Quantitative Methods]
    D --> E[Options & Finance]
    E --> F[Risk & Portfolio]
    F --> G[Strategies]
    G --> H[AI / ML & Microstructure]

New here? Follow the recommended learning paths — curated sequences for beginners, options traders, quant researchers and ML engineers.

Quick start

git clone https://github.com/MeridianAlgo/Learn-Quant
cd Learn-Quant
pip install -r requirements.txt

# run any module, e.g.
cd "Quantitative Methods - GARCH"
python garch.py

See the Getting Started guide for the full setup, or jump straight to the complete module index.