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Thomas Starke – Deep Reinforcement Learning in Trading



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Deep Reinforcement Learning in Trading requires a grounding understanding of financial markets such as buying and selling of securities. To carry out the strategies covered, the basic knowledge of “Keras” and “matplotlib”, “pandas dataframe” is required. The required skills are covered in the free course, ‘Python for Trading: Basic’, ‘Introduction to Machine Learning for Trading’ on Quantra. To get a deep understanding of Neural Networks, you can register in the ‘Neural Networks in Trading’ course which is recommended but optional.

Who is Thomas Starke?

Dr. Thomas Starke is the chief executive officer of the financial advisory firm AAAQuants. He has worked on the development of high-frequency, stat-arb strategies for index futures and an Al-based sentiment strategy during his illustrious career with Boronia Capital, Vivienne Court Trading, and Rolls-Royce. He was a senior research fellow and lecturer at Oxford University in his academic career. As a tech enthusiast, he is fascinated by emerging technologies such as artificial intelligence, quantum computing, and blockchain. He received his doctorate in Physics from Nottingham University (UK).

Deep Reinforcement Learning in Trading with Thomas Starke

Learn applications and effectiveness of using RL models in trading. The Deep Reinforcement has used 100+ research papers, articles to generate the RL model which went through hundreds of iterations on known synthetic patterns in order to finalize experience replay, hyperparameters, Q-learning and feedforward network. You will learn to create a full RL framework from scratch and practice in the capstone project. At the end of the course, you will learn to implement the model in live trading.

  • List and explain the need for reinforcement learning to hold on the delayed gratification test
  • Depict states, actions, double Q-learning, policy, experience replay and rewards.
  • Explain exploitation vs exploration tradeoff
  • Create and backtest a reinforcement learning model
  • Investigate returns and risk using different performance measures
  • Practice the concepts on real market data through a capstone project
  • Explain the challenges faced in live trading and list the solutions for them
  • Deploy the RL model for live trading and paper

Refund is acceptable:

  • Firstly, item is not as explained
  • Secondly, Deep Reinforcement Learning in Trading do not work the way it should.
  • Thirdly, and most importantly, support extension can not be used.

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