Deep Reinforcement Learning for Active High-Frequency Trading

Deep Reinforcement Learning for Active High-Frequency Trading

Course Description

Sooner or later, every Data Scientist meets with financial tasks in general and with automation of trading on a stock exchange – in particular. But not every Data Scientist knows how to apply Deep Reinforcement Learning to these assignments effectively. This course is designed to teach you just that – with real examples.

Course tools

  • Jupyter Notebook
  • Python
  • PyTorch


  • Math: linear algebra, optimization theory, calculus.
  • Hands-on Data Science, Machine Learning, and Deep Learning.
  • Basic Reinforcement Learning and Deep Reinforcement Learning.
  • Understanding of basic trading concepts like futures, tickers, bid, ask, volume, order book, limited and market orders, etc.
Level of complexity of course Advanced


Dr. Oleksandr Gurbych Software Solution Architect in  Oleksandr is a C/C++ developer, NET/C# developer, Java developer, Python developer, frontend developer, backend engineer, Data Scientist, Machine Learning Engineer, NLP Engineer, Computer Vision Engineer, BigData Engineer, DevOps/MLOps, QA/QC Engineer, Business Analyst, BI Lead, scientist, lawyer, Sales Development Representative (SDR), marketing manager, project manager, team leader, software architect, UI/UX designer, HR manager, PR manager, SEO, copywriter, partnerships manager, outreach manager, client success manager, account executive, events manager, content writer, YouTube video blogger, Medium blogger, sportsman, lecturer at National University “Lviv Polytechnic”, conference manager, conference speaker, and a happy father of two kiddos with more than 30 years of hands-on experience. Selected publications: Fields of interests: Sleep Contacts:    

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