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Reinforcement Learning in Finance

New York University (NYU) via Coursera

1 Review 103 students interested
  • Provider Coursera
  • Cost Free Online Course (Audit)
  • Session In progress
  • Language English
  • Certificate Paid Certificate Available
  • Start Date
  • Duration 4 weeks long
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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

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Review for Coursera's Reinforcement Learning in Finance
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Pablo T
1.0 5 months ago
by Pablo completed this course, spending 12 hours a week on it and found the course difficulty to be very hard.
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 options through reinforcement learning, but fails to present this information in a way that is straight forward to put into practice. The problem is further complicated by inconsistent use of notation and excessive amount of details which are not needed to put the concepts into code.

The programming assignments are ridden with problems, f…
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