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Igor Halperin
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
Read more
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
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/c8y0cb/reinforcement
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What's inside
Syllabus
MDP and Reinforcement Learning
MDP model for option pricing: Dynamic Programming Approach
MDP model for option pricing - Reinforcement Learning approach
Read more
Syllabus
MDP and Reinforcement Learning
MDP model for option pricing: Dynamic Programming Approach
MDP model for option pricing - Reinforcement Learning approach
Read more
RL and INVERSE RL for Portfolio Stock Trading
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops technical skills for option valuation, trading, and asset management
Taught by Igor Halperin who has valuable knowledge and expertise in the field of Reinforcement Learning in finance
Provides case studies and examples for reinforcement learning in financial problems
Builds a solid foundation for understanding reinforcement learning and its application in finance
Requires knowledge of lognormal process and simulation
Does not cover option pricing in detail
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Reviews summary
Challenging but exciting rl course
Learners say this challenging course in reinforcement learning is still worthwhile. It requires strong finance and TensorFlow backgrounds. While some say it needs more practical examples, others praise its innovative and interesting content. The assignments may be difficult but also rewarding. Overall, if you can withstand the technicality and math involved, you can get a comprehensive overview of reinforcement learning in finance.
Mix of practical uses and theory.
"very diffcult , i cant find the csv file to view the data"
"Final assignment was too vague and doesn't seem that useful"
"Great course. Some financial math knowledge and good level of TensorFlow is needed to complete the final assignment"
Innovative and interesting material.
"The content discussed in the course is very interesting and innovative"
"Very interesting material"
"Great course. You require lot of patience to complete the course"
Prepare for a challenge.
"Challenging course as a non-finance person"
"The accent of the professor is completely a disaster"
"the simulation method may not work well in reality"
Assumes some finance and math knowledge.
"Do not take this course if you know nothing about finance"
"Very good course, somehow technical for those without a Finance background"
"There is too much focus on quantitative financial analysis, and not enough time on explaining RL"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Reinforcement Learning in Finance with these
activities:
Create a course notebook
Show steps
Helps you stay organized and keeps all your course materials in one place, making it easier to review and absorb the information.
Show steps
-
Gather your notes, assignments, quizzes, and exams in one place.
-
Organize the materials into logical sections or chapters.
Review basic financial concepts
Show steps
Refreshes your memory on terminology and basic facts, which will help you get better grades and more easily engage with the content of this course.
Browse courses on
Finance Fundamentals
Show steps
-
Re-read introductory textbooks or notes from previous courses on topics such as investment fundamentals, financial markets, and corporate finance.
-
Take practice questions or quizzes on these topics to test your understanding and identify areas where you need more review.
Read Reinforcement Learning: An Introduction
Show steps
Provides you with the fundamentals of Reinforcement Learning algorithms and techniques applicable in finance.
View
Reinforcement Learning, second edition: An...
on Amazon
Show steps
-
Review the chapters on Markov Decision Processes (MDPs) and dynamic programming.
-
Focus on understanding the concept of value functions and how they are used to make optimal decisions in MDPs.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve LeetCode problems on dynamic programming
Show steps
Refines your understanding of dynamic programming and the skills needed to apply it to financial problems by practicing on a set of challenging problems.
Browse courses on
Dynamic programming
Show steps
-
Select LeetCode problems tagged with 'dynamic programming' and start solving them.
-
For each problem, try to come up with a recursive or iterative solution and analyze its time and space complexity.
Join a study group or attend office hours
Show steps
Provides opportunities to interact with peers, discuss concepts, work on assignments together, and clear up any uncertainties, which can enhance your understanding and improve your grades.
Show steps
-
Join an online study group or forum for the course.
-
Attend office hours and actively participate in discussions.
Follow tutorials on reinforcement learning applications in finance
Show steps
Provides practical examples and guidance on how to apply reinforcement learning techniques to problems in financial domains.
Show steps
-
Search for online tutorials or courses that cover topics such as 'Introduction to Reinforcement Learning for Finance' or 'Deep Reinforcement Learning in Algorithmic Trading'.
-
Follow the tutorials and work through the exercises to gain hands-on experience.
Answer questions and help other students in online forums
Show steps
Reinforces your own knowledge and improves your problem-solving skills by helping others understand concepts and solve problems related to the course material.
Show steps
-
Join online forums or discussion boards related to the course.
-
Actively participate in discussions, answer questions, and provide assistance to other students.
Build a simple RL model for option pricing
Show steps
Allows you to apply and test your understanding of reinforcement learning in a practical financial context by developing a simple model to determine option prices.
Show steps
-
Choose an appropriate RL algorithm, such as Q-learning or SARSA.
-
Define the state space, action space, and reward function for your problem.
-
Implement the RL algorithm and train the model on historical option pricing data.
Create a course notebook
Show steps
Helps you stay organized and keeps all your course materials in one place, making it easier to review and absorb the information.
Show steps
Show steps
Show steps
- Gather your notes, assignments, quizzes, and exams in one place.
- Organize the materials into logical sections or chapters.
Review basic financial concepts
Show steps
Refreshes your memory on terminology and basic facts, which will help you get better grades and more easily engage with the content of this course.
Browse courses on
Finance Fundamentals
Show steps
Show steps
Show steps
- Re-read introductory textbooks or notes from previous courses on topics such as investment fundamentals, financial markets, and corporate finance.
- Take practice questions or quizzes on these topics to test your understanding and identify areas where you need more review.
Read Reinforcement Learning: An Introduction
Show steps
Provides you with the fundamentals of Reinforcement Learning algorithms and techniques applicable in finance.
View
Reinforcement Learning, second edition: An...
on Amazon
Show steps
Show steps
Show steps
- Review the chapters on Markov Decision Processes (MDPs) and dynamic programming.
- Focus on understanding the concept of value functions and how they are used to make optimal decisions in MDPs.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve LeetCode problems on dynamic programming
Show steps
Refines your understanding of dynamic programming and the skills needed to apply it to financial problems by practicing on a set of challenging problems.
Browse courses on
Dynamic programming
Show steps
Show steps
Show steps
- Select LeetCode problems tagged with 'dynamic programming' and start solving them.
- For each problem, try to come up with a recursive or iterative solution and analyze its time and space complexity.
Join a study group or attend office hours
Show steps
Provides opportunities to interact with peers, discuss concepts, work on assignments together, and clear up any uncertainties, which can enhance your understanding and improve your grades.
Show steps
Show steps
Show steps
- Join an online study group or forum for the course.
- Attend office hours and actively participate in discussions.
Follow tutorials on reinforcement learning applications in finance
Show steps
Provides practical examples and guidance on how to apply reinforcement learning techniques to problems in financial domains.
Show steps
Show steps
Show steps
- Search for online tutorials or courses that cover topics such as 'Introduction to Reinforcement Learning for Finance' or 'Deep Reinforcement Learning in Algorithmic Trading'.
- Follow the tutorials and work through the exercises to gain hands-on experience.
Answer questions and help other students in online forums
Show steps
Reinforces your own knowledge and improves your problem-solving skills by helping others understand concepts and solve problems related to the course material.
Show steps
Show steps
Show steps
- Join online forums or discussion boards related to the course.
- Actively participate in discussions, answer questions, and provide assistance to other students.
Build a simple RL model for option pricing
Show steps
Allows you to apply and test your understanding of reinforcement learning in a practical financial context by developing a simple model to determine option prices.
Show steps
Show steps
Show steps
- Choose an appropriate RL algorithm, such as Q-learning or SARSA.
- Define the state space, action space, and reward function for your problem.
- Implement the RL algorithm and train the model on historical option pricing data.
Career center
Learners who complete Reinforcement Learning in Finance will develop knowledge and skills
that may be useful to these careers:
Quantitative Researcher
Quantitative Researcher
Quantitative Researchers develop and apply mathematical and statistical models to financial data. This course can be very useful as it provides advanced RL techniques specifically tailored to financial problems, an area of high demand in quantitative research.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and implement machine learning models. This course can be useful as it provides a foundation in RL, a specialized and in-demand area of machine learning with applications in finance.
Portfolio Manager
Portfolio Manager
Portfolio Managers oversee investment portfolios, making decisions on asset allocation, risk management, and investment strategy. This course can be useful by providing hands-on RL examples specifically tailored to portfolio optimization.
Financial Engineer
Financial Engineer
Financial Engineers develop and implement mathematical models for financial problems. This course can be useful as it provides a foundation in RL, a cutting-edge technique for solving complex financial problems.
Data Scientist
Data Scientist
Data Scientists analyze and interpret data to uncover patterns and insights. This course may be helpful as it provides a foundation in RL, a powerful technique for solving complex data-driven problems, including those in finance.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use advanced mathematical and statistical models to solve complex financial problems, improving investment strategies and risk management. This course can be useful as it provides a foundation in Reinforcement Learning (RL), a powerful technique for solving sequential decision-making problems in finance such as optimal trading, portfolio optimization, and option pricing.
Actuary
Actuary
Actuaries assess and manage financial risks. This course may be helpful as it introduces RL techniques for risk management, providing valuable insights for actuarial work.
Investment Banker
Investment Banker
Investment Bankers advise companies on financial matters, including mergers and acquisitions, capital raising, and restructuring. This course may be useful for understanding RL applications in portfolio optimization and risk management, relevant to investment banking activities.
Investment Analyst
Investment Analyst
Investment Analysts research and evaluate investment opportunities. This course may be helpful by providing RL techniques for option pricing and risk management, key areas for investment analysis.
Trading Strategist
Trading Strategist
Trading Strategists develop and implement strategies for trading financial instruments. This course may be useful as it covers RL applications for optimal trading, providing valuable insights and techniques for this role.
Risk Manager
Risk Manager
Risk Managers identify, assess, and mitigate financial risks. This course may be helpful as it introduces RL techniques which can improve risk management through more accurate and dynamic decision-making.
Financial Analyst
Financial Analyst
Financial Analysts research and evaluate financial data to make investment recommendations and provide guidance to clients. This course may be helpful, especially with its focus on RL applications for option valuation and risk management, key areas for financial analysis.
Insurance Analyst
Insurance Analyst
Insurance Analysts evaluate and manage insurance risks. This course may be helpful as it provides RL techniques for risk management, applicable to the insurance industry.
Financial Consultant
Financial Consultant
Financial Consultants provide financial advice and guidance to individuals and organizations. This course may be helpful as it covers RL applications for portfolio optimization and risk management, important areas for financial consulting.
Venture Capitalist
Venture Capitalist
Venture Capitalists invest in and support early-stage companies with high growth potential. The course's focus on RL applications for portfolio optimization and risk management may be helpful for evaluating investment opportunities.
For more career information including salaries, visit:
OpenCourser.com/course/c8y0cb/reinforcement
Reading list
We've selected 25 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Reinforcement Learning in Finance.
Save
A comprehensive and in-depth reference book for applications of Mathematics and Statistics to finance, with chapters on stochastic calculus, a key mathematical tool in finance.
Provides a modern and practical guide to modeling, analysis and solution of MDPs, a foundational concept in RL.
This comprehensive textbook provides a thorough introduction to the fundamental concepts and algorithms of reinforcement learning and is commonly used as a reference for practitioners and researchers in the field. It provides a good background and foundation for the topics covered in the course.
Save
The standard reference for Deep Learning. Deep Learning has been used to model market dynamics, therefore having a sound knowledge of DL will help understand this application of RL.
An excellent reference book for financial professionals working with derivatives, with a comprehensive section on option pricing models, an area where RL is applied.
Offers an in-depth exploration of Markov decision processes (MDPs), a type of stochastic dynamic programming model that is fundamental to reinforcement learning. It provides a solid theoretical foundation for the MDP-based models discussed in the course.
One of the most comprehensive references for professional traders and finance students on financial derivatives, It contains excellent sections on options pricing.
Provides a good introduction to Machine Learning, a precursor to Reinforcement Learning. Its Python-focus makes it a good resource for implementing and testing RL algorithms.
Save
Provides a good overview of the different applications of Machine Learning algorithms to finance, RL being one of them.
Provides a comprehensive overview of Reinforcement Learning , a cornerstone of ML and AI.
This practical guide provides hands-on experience in applying deep reinforcement learning algorithms to real-world problems. It complements the theoretical concepts covered in the course by showcasing practical implementations of RL in various domains.
This textbook offers a comprehensive overview of financial engineering concepts and numerical methods. It provides a solid foundation in the financial concepts and techniques relevant to the course, particularly in the context of option pricing and portfolio optimization.
Save
Provides a comprehensive view on algorithmic trading, from its foundations, to the different kinds of trading strategies one can develop.
Provides a complete introduction to statistics with a focus on finance. A good grasp of statistics is fundamental to working in finance, including in roles where RL is applied.
This foundational text introduces the mathematical underpinnings of stochastic calculus, which is essential for understanding the dynamics of financial markets. It provides a rigorous treatment of the binomial asset pricing model, which serves as a building block for more advanced RL-based option pricing approaches.
This widely-used textbook provides a comprehensive treatment of options, futures, and other derivatives. It offers a thorough grounding in the concepts and techniques of option pricing, which is essential for understanding the RL-based approaches to option valuation explored in the course.
This practical guide focuses on applying machine learning techniques to asset management. It provides insights into the use of RL for portfolio construction and risk management, complementing the course's coverage of RL in finance.
Provides a good introduction to Reinforcement Learning from the ground up, includes a very solid introduction to Probability Theory.
This accessible guide introduces deep learning concepts with a focus on practical applications. It provides a gentle introduction to deep learning libraries like Fastai and PyTorch, which are commonly used for implementing RL algorithms.
This practical guide introduces Python programming for data analysis and manipulation. It provides a solid foundation for implementing and experimenting with RL algorithms in Python, the primary language used in the course.
This practical guide focuses on implementing RL algorithms using Tensorflow, a popular deep learning library. It provides a valuable resource for students who want to gain hands-on experience in building and training RL models.
This textbook provides a rigorous foundation in financial econometrics, covering topics such as time series analysis and forecasting. It offers a valuable reference for understanding the statistical concepts and methods used in RL-based financial applications.
This introductory guide offers a high-level overview of AI applications in finance. It provides a helpful introduction to the field for students who are new to the intersection of AI and finance.
This practical guide focuses on the application of AI techniques to asset management. It offers insights into the use of RL for portfolio optimization and risk management in a real-world context, complementing the course's theoretical coverage.
This textbook introduces deep learning concepts and techniques in the context of finance. It provides a valuable resource for students who want to explore the intersection of deep learning and RL in financial applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/c8y0cb/reinforcement
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Level
Advanced
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Institution
New York University
Instructor
Igor Halperin
Language
English
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آذربایجان دیلی
This course belongs to a
collection
called
Machine Learning and Reinforcement Learning in Finance. It's comprised of four courses:
- Guided Tour of Machine Learning in Finance
- Fundamentals of Machine Learning in Finance
- Reinforcement Learning in Finance (viewing)
- Overview of Advanced Methods of Reinforcement Learning in Finance
Good to know
Develops technical skills for option valuation, trading, and asset management
Taught by Igor Halperin who has valuable knowledge and expertise in the field of Reinforcement Learning in finance
Provides case studies and examples for reinforcement learning in financial problems
Builds a solid foundation for understanding reinforcement learning and its application in finance
Requires knowledge of lognormal process and simulation
Does not cover option pricing in detail
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