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Reinforcement Learning in Finance
  • Level Expert
  • Duration 17 hours
  • Course by New York University
  • Offered by

About

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.

Modules

Introduction

    • 3 Videos
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3 Videos

  • Introduction to the Specialization
  • Prerequisites
  • Welcome to the Course

Markov Decision Processes (MDP)

    • 5 Videos
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5 Videos

  • Introduction to Markov Decision Processes and Reinforcement Learning in Finance
  • MDP and RL: Decision Policies
  • MDP & RL: Value Function and Bellman Equation
  • MDP & RL: Value Iteration and Policy Iteration
  • MDP & RL: Action Value Function

Discrete-Time Black-Scholes Model

    • 6 Videos
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6 Videos

  • Options and Option pricing
  • Black-Scholes-Merton (BSM) Model
  • BSM Model and Risk
  • Discrete Time BSM Model
  • Discrete Time BSM Hedging and Pricing
  • Discrete Time BSM BS Limit

Module 1 Assessment

    • 2 Readings
    • 1 Lab
    • 1 Programming
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1 Programming

  • Discrete-time Black Scholes model

1 Labs

  • Discrete-time Black Scholes model

2 Readings

  • Jupyter Notebook FAQ
  • Hedged Monte Carlo: low variance derivative pricing with objective probabilities

MDP for Discrete-Time BS Model

    • 4 Videos
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4 Videos

  • MDP Formulation
  • Action-Value Function
  • Optimal Action From Q Function
  • Backward Recursion for Q Star

Monte-Carlo Solution

    • 3 Videos
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3 Videos

  • Basis Functions
  • Optimal Hedge With Monte-Carlo
  • Optimal Q Function With Monte-Carlo

Module 2 Assessment

    • 2 Readings
    • 1 Lab
    • 1 Programming
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1 Programming

  • QLBS Model Implementation

1 Labs

  • QLBS Model Implementation

2 Readings

  • Jupyter Notebook FAQ
  • QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds

MDP: RL Approach

    • 8 Videos
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8 Videos

  • Week Introduction
  • Batch Reinforcement Learning
  • Stochastic Approximations
  • Q-Learning
  • Fitted Q-Iteration
  • Fitted Q-Iteration: the Ψ-basis
  • Fitted Q-Iteration at Work
  • RL Solution: Discussion and Examples

Module 3 Assessment

    • 3 Readings
    • 1 Lab
    • 1 Programming
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1 Programming

  • Fitted Q-Iteration

1 Labs

  • Fitted Q-Iteration

3 Readings

  • Jupyter Notebook FAQ
  • QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds and The QLBS Learner Goes NuQLear
  • Course Project Reading: Global Portfolio Optimization

RL for Stock Trading

    • 10 Videos
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10 Videos

  • Week Welcome Video
  • Introduction to RL for Trading
  • Portfolio Model
  • One Period Rewards
  • Forward and Inverse Optimisation
  • Reinforcement Learning for Portfolios
  • Entropy Regularized RL
  • RL Equations
  • RL and Inverse Reinforcement Learning Solutions
  • Course Summary

Module 4 Assessment

    • 2 Readings
    • 1 PeerReview
    • 1 Lab
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1 Peer Review

  • IRL Market Model Calibration

1 Labs

  • IRL Market Model Calibration

2 Readings

  • Jupyter Notebook FAQ
  • Multi-period trading via Convex Optimization

Auto Summary

"Reinforcement Learning in Finance" focuses on applying Reinforcement Learning (RL) to financial problems like portfolio optimization, trading, and option pricing. Designed for experts in Data Science & AI, the course is led by Coursera and spans 1020 minutes. It includes practical examples, such as Q-learning in finance, and a market dynamics model project. Prerequisites include foundational courses in Machine Learning in Finance. Subscription options are available, making it ideal for advanced learners seeking to enhance their finance skills using RL.

Igor Halperin
Instructor

Igor Halperin