Course Summary
This course explores how reinforcement learning can be applied to finance. Topics include optimal trading strategies, portfolio management, and risk management.Key Learning Points
- Learn about the basics of reinforcement learning and how it can be used in finance
- Understand how to apply reinforcement learning to trading strategies, portfolio management, and risk management
- Explore recent research and developments in the field
Related Topics for further study
Learning Outcomes
- Ability to apply reinforcement learning to finance
- Understanding of optimal trading strategies and portfolio management
- Knowledge of recent research and developments in the field
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics and programming
- Familiarity with finance and trading concepts
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Machine Learning for Trading
- Financial Markets
- Options Trading Strategies
Related Education Paths
Notable People in This Field
- Andrew Ng
- Nassim Taleb
Related Books
Description
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.
Outline
- MDP and Reinforcement Learning
- Introduction to the Specialization
- Prerequisites
- Welcome to the Course
- 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
- 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
- Jupyter Notebook FAQ
- Hedged Monte Carlo: low variance derivative pricing with objective probabilities
- MDP model for option pricing: Dynamic Programming Approach
- MDP Formulation
- Action-Value Function
- Optimal Action From Q Function
- Backward Recursion for Q Star
- Basis Functions
- Optimal Hedge With Monte-Carlo
- Optimal Q Function With Monte-Carlo
- Jupyter Notebook FAQ
- QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds
- MDP model for option pricing - Reinforcement Learning approach
- 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
- 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 and INVERSE RL for Portfolio Stock Trading
- 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
- Jupyter Notebook FAQ
- Multi-period trading via Convex Optimization
Summary of User Reviews
Find out what users are saying about Reinforcement Learning in Finance course on Coursera. This online course has garnered positive reviews for its comprehensive coverage of the subject matter, with users praising the practical applications of the concepts taught.Key Aspect Users Liked About This Course
Users appreciate the practical applications of the concepts taught in the course.Pros from User Reviews
- Comprehensive coverage of the subject matter
- Practical applications of the concepts taught
- Well-structured and easy to follow content
- Engaging and knowledgeable instructors
- Great resource for anyone looking to learn about reinforcement learning in finance
Cons from User Reviews
- May be too technical for beginners
- Some sections of the course could be more detailed
- Lack of hands-on exercises
- Limited interaction with instructors
- Course material may be outdated
Igor Halperin
3.6 Raiting
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