金融业的强化学习

Reinforcement Learning in Finance

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纽约大学坦登工程学院
Coursera
  • 完成时间大约为 20 个小时
  • 高级
  • 英语
注:本课程由Coursera和Linkshare共同提供,因开课平台的各种因素变化,以上开课日期仅供参考

课程概况

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.

课程大纲

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

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