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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In the last course of our specialization, Overview of Advanced Methods of Reinforcement Learning in Finance, we will take a deeper look into topics discussed in our third course, Reinforcement Learning in Finance.
In particular, we will talk about links between Reinforcement Learning, option pricing and physics, implications of Inverse Reinforcement Learning for modeling market impact and price dynamics, and perception-action cycles in Reinforcement Learning. Finally, we will overview trending and potential applications of Reinforcement Learning for high-frequency trading, cryptocurrencies, peer-to-peer lending, and more.

After taking this course, students will be able to 
- explain fundamental concepts of finance such as market equilibrium, no arbitrage, predictability,
- discuss market modeling,
- Apply the methods of Reinforcement Learning to high-frequency trading, credit risk peer-to-peer lending, and cryptocurrencies trading.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 4 of 4 in the

Machine Learning and Reinforcement Learning in Finance Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

Approx. 13 hours to complete

English

Subtitles: English

Instructor

Igor Halperin

33,457 Learners

4 Courses

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 4 of 4 in the

Machine Learning and Reinforcement Learning in Finance Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

Approx. 13 hours to complete

English

Subtitles: English

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

Black-Scholes-Merton model, Physics and Reinforcement Learning

4 hours to complete

13 videos (Total 103 min)

13 videos

Welcome to Specialization4m

Specialization Prerequisites7m

Interview with Rossen Roussev14m

Reinforcement Learning and Ptolemy's Epicycles5m

PDEs in Physics and Finance5m

Competitive Market Equilibrium Models in Finance5m

I Certainly Hope You Are Wrong, Herr Professor!7m

Risk as a Science of Fluctuation3m

Markets and the Heat Death of the Universe3m

Option Trading and RL14m

Liquidity9m

Modeling Market Frictions9m

Modeling Feedback Frictions10m

1 practice exercise

Assignment 12h

Week

Week 2

3 hours to complete

Reinforcement Learning for Optimal Trading and Market Modeling

3 hours to complete

8 videos (Total 73 min)

8 videos

From Portfolio Optimization to Market Model9m

Invisible Hand5m

GBM and Its Problems9m

The GBM Model: An Unbounded Growth Without Defaults9m

Dynamics with Saturation: The Verhulst Model7m

The Singularity is Near9m

What are Defaults?11m

Quantum Equilibrium-Disequilibrium11m

1 practice exercise

Assignment 22h

Week

Week 3

3 hours to complete

Perception - Beyond Reinforcement Learning

3 hours to complete

8 videos (Total 60 min)

8 videos

Welcome!!4m

Market Dynamics and IRL5m

Diffusion in a Potential: The Langevin Equation8m

Classical Dynamics7m

Potential Minima and Newton's Law4m

Classical Dynamics: the Lagrangian and the Hamiltonian7m

Langevin Equation and Fokker-Planck Equations9m

The Fokker-Planck Equation and Quantum Mechanics12m

1 practice exercise

Assignment 32h

Week

Week 4

3 hours to complete

Other Applications of Reinforcement Learning: P-2-P Lending, Cryptocurrency, etc.

3 hours to complete

9 videos (Total 79 min)

9 videos

Welcome!!1m

Electronic Markets and LOB9m

Trades, Quotes and Order Flow7m

Limit Order Book8m

LOB Modeling8m

LOB Statistical Modeling10m

LOB Modeling with ML and RL9m

Other Applications of RL7m

The Value of Universatility15m

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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Overview of Advanced Methods of Reinforcement Learning in Finance