Overview of Advanced Methods of Reinforcement Learning in Finance

This course is part of Machine Learning and Reinforcement Learning in Finance Specialization

Instructor: Igor Halperin

Skills you'll gain

  •   Artificial Intelligence and Machine Learning (AI/ML)
  •   Reinforcement Learning
  •   Finance
  •   Derivatives
  •   Securities (Finance)
  •   Machine Learning
  •   Market Liquidity
  •   Physics
  •   Market Dynamics
  •   Financial Trading
  •   Financial Market
  •   Credit Risk
  •   Financial Modeling
  • There are 4 modules in this course

    In particular, we will talk about links between Reinforcement Learning, option pricing and physics, implications of Inverse Reinforcement Learning for modeling market impact and price dynamics, and perception-action cycles in Reinforcement Learning. Finally, we will overview trending and potential applications of Reinforcement Learning for high-frequency trading, cryptocurrencies, peer-to-peer lending, and more. After taking this course, students will be able to - explain fundamental concepts of finance such as market equilibrium, no arbitrage, predictability, - discuss market modeling, - Apply the methods of Reinforcement Learning to high-frequency trading, credit risk peer-to-peer lending, and cryptocurrencies trading.

    Reinforcement Learning for Optimal Trading and Market Modeling

    Perception - Beyond Reinforcement Learning

    Other Applications of Reinforcement Learning: P-2-P Lending, Cryptocurrency, etc.

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