University Certificate

in Python for Algorithmic Trading

by The Python Quants

Master AI-Driven Algorithmic Trading

Level up your Python skills for Finance and Algorithmic Trading.

Online Training

150+ Hrs. Instruction

16 Week Study Plan

Comprehensive Online Training Program

This is an intense online training program about Python techniques for algorithmic trading. By signing up to this program you get access to 150+ hours of live/recorded instruction, 1,200+ pages PDF as well as 5,000+ lines of Python code and 50+ Jupyter Notebooks (read the 16 week study plan). Master AI-Driven Algorithmic Trading, get started today.

Program Brochure

Have a look at the Certificate Program Brochure which provides you with details about the program.

Build your own algorithmic trading business.

Enrollment

The next Certificate Program starts  in the week from 13. January 2020, is mostly self-paced and takes about 16 weeks to complete. Read the example 16 week study plan. Classes include Finance with Python, Python Tools & Skills, Python for Financial Data Science, Python for Algorithmic Trading, Artificial Intelligence in Finance, Python for Excel, Python for Databases and Natural Language Processing.

Enroll to the Certificate Program and benefit from the unique combination of materials, topics and codes that are covered in this comprehensive online training program. The regular fee is 2,695 EUR (net of VAT if applicable) per person.

Early bird pricing of 2,365 EUR (net of VAT if applicable) is valid until 23. December 2019. Do not miss this unique opportunity.

Get in touch if you think you qualify for a discount (e.g. students, academics, upgrades from other TPQ classes/courses).

You can also book the Platinum Package which includes additional elements from the Certificate Program in Computational Finance under https://home.tpq.io/certificates.

With the Platinum Package you get access to all our online training resources. Enrolling to this package signals that you are serious about improving your Python skills.

If you are signing up as an individual or in the name of a German corporation, use this button:

If you are booking in the name of a EU-based (excl. Germany) or international cooperation, do it here:

Cancellations or refunds are not possible since delegates get full access to the complete electronic program materials (videos, HTML pages, PDFs, Jupyter Notebooks, Python codes, etc.). The program resources are copyrighted and not allowed to be shared or distributed. The resources are instructional and illustrative only. They come with no warranties or representations, to the extent permitted by applicable law.

By signing up, you agree to our Privacy Policy and the Terms & Conditions.

Login credentials to the Quant Platform are sent immediately afterwards, so you can get started with the course materials right away. Detailed joining instructions for the live sessions will be sent in time before they start.

Award-Winning Program

We are proud to be named Top 10 Algorithmic Trading Solution Provider of 2019 by Capital Markets CIO Outlook.

Here you can read the full article.

Frequently Asked Questions

In the following, you find answers to the most frequently asked questions about the program.

The program covers a wide range of important topics in Python for Finance & Algorithmic Trading, such as vectorized & event-based backtesting, streaming data & socket programming, machine & deep learning as well as live trading on popular trading platforms. Among others, you get access to 150+ hours of recorded/live instruction, 1,200+ pages documentation, 5,000+ lines of Python code, 50+ Jupyter Notebooks. There are also tutorials with exercises and test projects. Towards the end, you take part in practice modules and prepare a final, graded project.

We expect you to have some programming experience (not necessarily Python) and some background in finance. Our Finance with Python course provides a gentle introduction to finance from fundamental principles and to Python from scratch.

We know that many of you have full-time jobs and other commitments. Therefore, we are fully flexible with regard to timings. If you prefer, you can do the program completely self-paced.

We do not limit access to the resources of the program. To the contrary, you benefit from certain updates that we will bring out in the future automatically.

Yes, there are live sessions in addition to the large number of recorded sessions available. We are used to having delegates basically from around the world. Therefore, all sessions are recorded and made available shortly afterwards since not everybody can follow every live session — be it due to time zone differences or due to other commitments.

Immediately after signing up, you get access to all the materials and video recordings. This allows you to orient yourself on the Quant Platform and to review the introductory lessons.

We offer a 15% discount for students and academics. We also offer special upgrade prices for those who have already booked a course or class with us. Just reach out to us via training@tpq.io.

To award the University Certificate, The Python Quants cooperate with the htw saar University of Applied Sciences.

Program Director

Dr. Yves J. Hilpisch is founder and CEO of The Python Quants as well as founder and CEO of The AI Machine. Yves is also Professor of Computational Finance at Miami Business School and lectures on computational finance, machine learning and algorithmic trading at the CQF Program. Yves is the author of four books: Artificial Intelligence in Finance (O’Reilly, current project), Python for Finance (2018, 2nd ed., O’Reilly), Listed Volatility and Variance Derivatives (2017, Wiley Finance) and Derivatives Analytics with Python (2015, Wiley Finance).

Reaching Us Is Easy

Get & Keep in Touch

Write us under training@tpq.io if you have further questions or comments. Sign up below to stay informed.

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The Experts in Data-Driven and AI-First Finance with Python. We focus on Python and Open Source Technologies for Financial Data Science, Artificial Intelligence, Algorithmic Trading and Computational Finance.

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