NYU - Reinforcement Learning in Finance
5.0 /5- Offered byCoursera
Reinforcement Learning in Finance at Coursera Overview
Duration | 17 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
Reinforcement Learning in Finance at Coursera Highlights
- This Course Plus the Full Specialization.
- Shareable Certificates.
- Graded Programming Assignments.
Reinforcement Learning in Finance at Coursera Course details
- 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.
Reinforcement Learning in Finance at Coursera Curriculum
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 ?-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
Reinforcement Learning in Finance at Coursera Admission Process
Important Dates
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