reinforcement learning in finance

The Reinforcement Learning in Finance training 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 attending Reinforcement Learning in Finance workshop, Participants will:

  • 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.

"Machine Learning in Finance" and "Fundamentals of Machine Learning in Finance". Participants are expected to know the lognormal process and how it can be simulated. Knowledge of option pricing is not assumed but desirable.

COURSE AGENDA

  • 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
  • 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
  • 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
  • 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

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