金融业的强化学习高级方法综述

Overview of Advanced Methods of 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 14 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

In the last course of our specialization, Overview of Advanced Methods of Reinforcement Learning in Finance, we will take a deeper look into topics discussed in our third course, Reinforcement Learning in Finance.

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.

课程大纲

Black-Scholes-Merton model, Physics and Reinforcement Learning

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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