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Companion code repository for "Python for Algorithmic Trading" lectures.

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# EPAT · Python for Algorithmic Trading · Companion Code

This repository contains the companion code for the EPAT article and slide deck
on **Python for Algorithmic Trading: Efficient Markets, Backtesting, and Streaming**.
It provides the scripts and notebooks used to illustrate concepts such as
random walks and the Efficient Market Hypothesis (EMH), vectorized and
event-based backtesting, streaming tick data, and basic causality diagnostics.

---

## Copyright and Contact

- &copy; Dr. Yves J. Hilpisch  
- AI-Powered by GPT 5.1  
- The Python Quants GmbH · <https://tpq.io>  
- <https://hilpisch.com> · <https://linktr.ee/dyjh>

---

## Main Resources

- **Article (PDF)**  
  <https://hilpisch.com/epat_article.pdf>

- **Slide Deck (PDF)**  
  <https://hilpisch.com/epat_slides.pdf>

The code in this repository is designed to mirror the structure and examples
of these two documents.

---

## Repository Layout

- `code/`  
  Standalone Python scripts grouped by topic:
  - EMH benchmarks and random walks.
  - Vectorized lagged-returns OLS backtests.
  - Event-based momentum backtests.
  - ZeroMQ-based streaming examples.
  - Granger-causality simulations and tests.

- `notebooks/`  
  Jupyter notebooks that provide narrative, executable walk-throughs of the
  same topics, suitable for self-study and workshops.

- `figures/`  
  Placeholder directory for figures generated by the scripts and notebooks.
  Figures are not tracked here; regenerate them locally as needed.

Each script and notebook is documented with inline comments explaining the
purpose of key steps and how they relate back to the article and slides.

---

## Disclaimer

The material in this repository is provided **for educational and personal
research use only**. It is not investment advice and does not constitute an
offer to buy or sell any financial instrument. Past performance, whether
simulated or real, is not indicative of future results. Trading financial
instruments involves risk, including the risk of loss of capital.

You are solely responsible for any decisions you make based on this code or
the accompanying materials.

---

## License and Usage Restrictions

This code is licensed for **personal, non-commercial use only**. In
particular:

- You may:
  - Clone and run the code locally for your own learning and experimentation.
  - Adapt the examples for private study or internal prototypes that are not
    shared outside your organization.

- You may **not**:
  - Use the code (or direct derivatives) in commercial products or services.
  - Use the code in paid consulting projects, commercial training, or as part
    of publicly distributed course or workshop material.
  - Republish substantial portions of the code or notebooks in books, articles,
    blogs, or other media without prior written permission from
    **The Python Quants GmbH**.

If you are interested in commercial use, licensing, or custom training based
on this material, please contact **The Python Quants GmbH** via
<https://tpq.io> or <https://hilpisch.com>.

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