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Overview of Advanced Methods of Reinforcement Learning in Finance
  • Level Expert
  • Duration 13 hours
  • Course by New York University
  • Offered by

About

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.

Modules

Introduction

    • 3 Videos
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3 Videos

  • Welcome to Specialization
  • Specialization Prerequisites
  • Interview with Rossen Roussev

Lesson 1

    • 6 Videos
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6 Videos

  • Reinforcement Learning and Ptolemy's Epicycles
  • PDEs in Physics and Finance
  • Competitive Market Equilibrium Models in Finance
  • I Certainly Hope You Are Wrong, Herr Professor!
  • Risk as a Science of Fluctuation
  • Markets and the Heat Death of the Universe

Lesson 2

    • 4 Videos
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4 Videos

  • Option Trading and RL
  • Liquidity
  • Modeling Market Frictions
  • Modeling Feedback Frictions

Week 1 Assessment

    • 1 Assignment
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1 Assignment

  • Assignment 1

Lesson 1

    • 8 Videos
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8 Videos

  • From Portfolio Optimization to Market Model
  • Invisible Hand
  • GBM and Its Problems
  • The GBM Model: An Unbounded Growth Without Defaults
  • Dynamics with Saturation: The Verhulst Model
  • The Singularity is Near
  • What are Defaults?
  • Quantum Equilibrium-Disequilibrium

Week 2 Assessment

    • 1 Assignment
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1 Assignment

  • Assignment 2

Lesson 1

    • 8 Videos
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8 Videos

  • Welcome!!
  • Market Dynamics and IRL
  • Diffusion in a Potential: The Langevin Equation
  • Classical Dynamics
  • Potential Minima and Newton's Law
  • Classical Dynamics: the Lagrangian and the Hamiltonian
  • Langevin Equation and Fokker-Planck Equations
  • The Fokker-Planck Equation and Quantum Mechanics

Week 3 Assessment

    • 1 Assignment
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1 Assignment

  • Assignment 3

Lesson 1

    • 9 Videos
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9 Videos

  • Welcome!!
  • Electronic Markets and LOB
  • Trades, Quotes and Order Flow
  • Limit Order Book
  • LOB Modeling
  • LOB Statistical Modeling
  • LOB Modeling with ML and RL
  • Other Applications of RL
  • The Value of Universatility

Week 4 Assessment

    • 1 PeerReview
    • 1 Lab
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1 Peer Review

  • Final Project

1 Labs

  • Final Project: Exploration of non-linear market model dynamics

Auto Summary

Dive into the advanced methods of Reinforcement Learning in finance with this expert-level course by Coursera. Explore links between Reinforcement Learning, option pricing, and physics, and discover applications in high-frequency trading, cryptocurrencies, and peer-to-peer lending. Ideal for data science and AI enthusiasts, this 780-minute course offers deep insights into market modeling and dynamics. Available via Starter and Professional subscriptions.

Igor Halperin
Instructor

Igor Halperin