Hack Session: Automated Portfolio Management using Reinforcement Learning

Nov 14, 2019


Auditorium 3

60 minutes

Reinforcement Learning

  • Introduction to deep RL, how to define a RL problem?
  • Introduction to the problem statement and definition of the network architecture
    • the types of networks used
    • the scoring functions
    • how to optimise for costs and other nuances
  • The demo on the notebook
    • exploratory data analysis
    • demonstration of the RL framework + other comparative frameworks
  • Demonstration of results in comparison with other comparative frameworks
  • Resources/Papers to find more about deep RL for portfolio optimization

Key Takeaways for the Audience

  1. How to create a framework for portfolio management in python?
  2. Details of the nuances of the portfolio management and backtesting problem: data framework, problem structure, transaction cost management, etc.
  3. How to apply reinforcement learning for the portfolio management problem?
Key Takeaways f
  • Sonam Srivastava

    Independent Investment Advisor

    Wright Research

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