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New York University (NYU)

Reinforcement Learning in Finance

New York University (NYU) via Coursera


This course aims at introducing the fundamental concepts of Reinforcement Learning (RL), and develop use cases for applications of RL for option valuation, trading, and asset management. By the end of this course, students will be able to - Use reinforcement learning to solve classical problems of Finance such as portfolio optimization, optimal trading, and option pricing and risk management. - Practice on valuable examples such as famous Q-learning using financial problems. - Apply their knowledge acquired in the course to a simple model for market dynamics that is obtained using reinforcement learning as the course project. Prerequisites are the courses "Guided Tour of Machine Learning in Finance" and "Fundamentals of Machine Learning in Finance". Students are expected to know the lognormal process and how it can be simulated. Knowledge of option pricing is not assumed but desirable.


  • MDP and Reinforcement Learning
  • MDP model for option pricing: Dynamic Programming Approach
  • MDP model for option pricing - Reinforcement Learning approach
  • RL and INVERSE RL for Portfolio Stock Trading

Taught by

Igor Halperin


1.0 rating, based on 2 Class Central reviews

3.5 rating at Coursera based on 126 ratings

Start your review of Reinforcement Learning in Finance

  • Profile image for Pablo Torre
    Pablo Torre
    The material covered in the course is mostly focused around the mathematics of the Bellman and Black Scholes equations. The professor takes several weeks attempting to relate between the two in order to provide a mathematical framework to price opti…
  • Albert Moon
    A very poor class: the material dive deep in math but with no explanation and moreover the material does not help to solve the homework or to reach the course goals.
    The forum is almost dead.
    The only acceptable points are the jupyter notebooks wich are interesting to solve (but you will need to find the knowledge outside of the course as I mentioned before).

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