This course is part of the Machine Learning and Reinforcement Learning in Finance Specialization

Igor Halperin

Offered By

Machine Learning and Reinforcement Learning in Finance SpecializationNew York University

About this Course

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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.

Shareable Certificate

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Course 3 of 4 in the

Machine Learning and Reinforcement Learning in Finance Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

Approx. 17 hours to complete

English

Subtitles: English

Skills you will gain

option pricing and risk managementsimple model for market dynamicsQ-learning using financial problemsoptimal tradingPortfolio Optimization

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 3 of 4 in the

Machine Learning and Reinforcement Learning in Finance Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

Approx. 17 hours to complete

English

Subtitles: English

Instructor

Instructor rating

3.6/5 (5 Ratings)

Igor Halperin

34,451 Learners

4 Courses

Offered by

New York University

New York University is a leading global institution for scholarship, teaching, and research. Based in New York City with campuses and sites in 14 additional major cities across the world, NYU embraces diversity among faculty, staff and students to ensure the highest caliber, most inclusive educational experience.

Syllabus - What you will learn from this course

Week

Week 1

4 hours to complete

MDP and Reinforcement Learning

4 hours to complete

14 videos (Total 107 min), 2 readings, 1 quiz

14 videos

Introduction to the Specialization4m

Prerequisites7m

Welcome to the Course5m

Introduction to Markov Decision Processes and Reinforcement Learning in Finance9m

MDP and RL: Decision Policies9m

MDP & RL: Value Function and Bellman Equation7m

MDP & RL: Value Iteration and Policy Iteration4m

MDP & RL: Action Value Function9m

Options and Option pricing7m

Black-Scholes-Merton (BSM) Model8m

BSM Model and Risk9m

Discrete Time BSM Model7m

Discrete Time BSM Hedging and Pricing8m

Discrete Time BSM BS Limit6m

2 readings

Jupyter Notebook FAQ10m

Hedged Monte Carlo: low variance derivative pricing with objective probabilities10m

Week

Week 2

4 hours to complete

MDP model for option pricing: Dynamic Programming Approach

4 hours to complete

7 videos (Total 59 min), 2 readings, 1 quiz

7 videos

MDP Formulation10m

Action-Value Function5m

Optimal Action From Q Function6m

Backward Recursion for Q Star8m

Basis Functions8m

Optimal Hedge With Monte-Carlo8m

Optimal Q Function With Monte-Carlo10m

2 readings

Jupyter Notebook FAQ10m

QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds10m

Week

Week 3

4 hours to complete

MDP model for option pricing - Reinforcement Learning approach

4 hours to complete

8 videos (Total 71 min), 3 readings, 1 quiz

8 videos

Week Introduction2m

Batch Reinforcement Learning9m

Stochastic Approximations8m

Q-Learning8m

Fitted Q-Iteration10m

Fitted Q-Iteration: the Ψ-basis9m

Fitted Q-Iteration at Work11m

RL Solution: Discussion and Examples11m

3 readings

Jupyter Notebook FAQ10m

QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds and The QLBS Learner Goes NuQLear10m

Course Project Reading: Global Portfolio Optimization10m

Week

Week 4

5 hours to complete

RL and INVERSE RL for Portfolio Stock Trading

5 hours to complete

10 videos (Total 82 min), 2 readings, 1 quiz

10 videos

Week Welcome Video2m

Introduction to RL for Trading12m

Portfolio Model8m

One Period Rewards6m

Forward and Inverse Optimisation10m

Reinforcement Learning for Portfolios9m

Entropy Regularized RL8m

RL Equations10m

RL and Inverse Reinforcement Learning Solutions10m

Course Summary3m

2 readings

Jupyter Notebook FAQ10m

Multi-period trading via Convex Optimization10m

About the Machine Learning and Reinforcement Learning in Finance Specialization

The main goal of this specialization is to provide the knowledge and practical skills necessary to develop a strong foundation on core paradigms and algorithms of machine learning (ML), with a particular focus on applications of ML to various practical problems in Finance.

The specialization aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) mapping the problem on a general landscape of available ML methods, (2) choosing particular ML approach(es) that would be most appropriate for resolving the problem, and (3) successfully implementing a solution, and assessing its performance. The specialization is designed for three categories of students: · Practitioners working at financial institutions such as banks, asset management firms or hedge funds · Individuals interested in applications of ML for personal day trading · Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance. The modules can also be taken individually to improve relevant skills in a particular area of applications of ML to finance.

Machine Learning and Reinforcement Learning in Finance

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Reinforcement Learning in Finance