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The aim of this course is to introduce the fundamental concepts of Reinforcement Learning (RL) and to develop use cases for RL applications in option valuation, trading, and asset management.
Students will be able to: -
- Use reinforcement learning to solve classic finance problems such as portfolio optimization, optimal trading, option pricing, and risk management by the end of this course.
- Experiment with valuable examples, such as the well-known Q-learning using financial problems.
- As the course project, apply their knowledge gained in the course to a simple model for market dynamics obtained through reinforcement learning.
The courses "Guided Tour of Machine Learning in Finance" and "Fundamentals of Machine Learning in Finance" are required. Students must understand the lognormal process and how it can be simulated. Option pricing knowledge is not required, but it is preferred.
Syllabus :
1. 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
2. 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
3. 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
4. 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
