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Jack Farmer
In the final course from the Machine Learning for Trading specialization, you will be introduced to reinforcement learning (RL) and the benefits of using reinforcement learning in trading strategies. You will learn how RL has been integrated with neural networks and review LSTMs and how they can be applied to time series data. By the end of the course, you will be able to build trading strategies using reinforcement learning, differentiate between actor-based policies and value-based policies, and incorporate RL into a momentum trading strategy.
Read more
In the final course from the Machine Learning for Trading specialization, you will be introduced to reinforcement learning (RL) and the benefits of using reinforcement learning in trading strategies. You will learn how RL has been integrated with neural networks and review LSTMs and how they can be applied to time series data. By the end of the course, you will be able to build trading strategies using reinforcement learning, differentiate between actor-based policies and value-based policies, and incorporate RL into a momentum trading strategy.
Read more
In the final course from the Machine Learning for Trading specialization, you will be introduced to reinforcement learning (RL) and the benefits of using reinforcement learning in trading strategies. You will learn how RL has been integrated with neural networks and review LSTMs and how they can be applied to time series data. By the end of the course, you will be able to build trading strategies using reinforcement learning, differentiate between actor-based policies and value-based policies, and incorporate RL into a momentum trading strategy.
To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
Register for this course and see more details by visiting:
OpenCourser.com/course/tzykdm/reinforcement
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What's inside
Syllabus
Introduction to Course and Reinforcement Learning
In this module, reinforcement learning is introduced at a high level. The history and evolution of reinforcement learning is presented, including key concepts like value and policy iteration. Also, the benefits and examples of using reinforcement learning in trading strategies is described. We also introduce LSTM and AutoML as additional tools in your toolkit to use in implementing trading strategies.
Read more
Syllabus
Introduction to Course and Reinforcement Learning
In this module, reinforcement learning is introduced at a high level. The history and evolution of reinforcement learning is presented, including key concepts like value and policy iteration. Also, the benefits and examples of using reinforcement learning in trading strategies is described. We also introduce LSTM and AutoML as additional tools in your toolkit to use in implementing trading strategies.
Read more
Neural Network Based Reinforcement Learning
In the previous module, reinforcement learning was discussed before neural networks were introduced. In this module, we look at how reinforcement learning has been integrated with neural networks. We also look at LSTMs and how they can be applied to time series data.
Portfolio Optimization
In this module we discuss the practical steps required to create a reinforcement learning trading system. Also, we introduce AutoML, a powerful service on Google Cloud Platform for training machine learning models with minimal coding.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Reinforces key concepts of the Machine Learning for Trading specialization
Builds on foundational knowledge of financial markets and reinforcement learning
Requires advanced competency in Python programming and familiarity with machine learning libraries
Emphasizes the application of reinforcement learning in trading strategies
Provides practical guidance on building and implementing reinforcement learning trading systems
Save this course
Save Reinforcement Learning for Trading Strategies to your list so you can find it easily later:
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Reviews summary
Reinforcement learning for trading strategies
Learners say this advanced course offers a succinct explanation of reinforcement learning concepts with engaging examples. While the course provides a great introduction to the topic, it lacks practical examples of using reinforcement learning in stock trading. Students should have a strong understanding of machine learning, artificial intelligence, and trading before enrolling.
Provides a solid theoretical foundation in reinforcement learning concepts.
"I learned a few cool things."
"The lectures by the NYIF guy were immediately relevant to me, worth taking the course for."
"They should just have removed the Google stuff entirely and just started with an assumption of a basic knowledge of ML - just focus on the financial applications."
Course heavily promotes Google Cloud services.
"It was useful, but can be better"
"The labs need an update, otherwise interesting mix of content."
"Heavy on pushing Google cloud services."
Difficult concepts are covered quickly with limited time for practice.
"The part covering RL, MDP, TD and Q Learning is illustrated too fast to understand any subtle points, with too many details (equations quickly explained, code fragments gone through in a minute or too) put together roughly to be a qualitative introduction."
"The finance lectures, of course, do relate to trading strategies, but they're just advice - it's all "do x, don't do y," with no explanation of *how* to do x or avoid doing y."
Requires strong background in machine learning, artificial intelligence, and trading.
"You should not enroll if you expect to be able to be able to build a RL system."
"You should not enroll if you are expecting some simple intuitive introduction of RL. This is more difficult than an introduction but tells you nothing more than some introduction, so it is an introduction done in a difficult way. I think it is better to avoid it."
Lacks practical examples of using reinforcement learning in stock trading.
"The RL material consists of an introduction to RL in general, and some pre-done notebooks that execute RL on ai gym. None of it has anything to do with trading strategies."
"There is no real application of RL in trading in this course."
"It was ... OK. The lectures by the NYIF guy were immediately relevant to me, worth taking the course for. They should just have removed the Google stuff entirely and just started with an assumption of a basic knowledge of ML - just focus on the financial applications."
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 for Trading Strategies with these
activities:
Read ‘Machine Learning for Algorithmic Trading: Predictive Models and Trading Strategies’ by Stefan Jansen
Show steps
Get a head start on the material in this course by getting some foundational knowledge with this book.
View
Machine Learning for Algorithmic Trading:...
on Amazon
Show steps
-
Purchase the book online or from a bookstore.
-
Set aside some time each day to read the book.
-
Take notes as you read to help you remember the key points.
Practice with Python coding exercises
Show steps
Make sure your coding skills are up to par by completing these coding exercises.
Browse courses on
Python
Show steps
-
Find some online Python coding exercises.
-
Set aside some time each week to work on the exercises.
Create flashcards of key reinforcement learning concepts
Show steps
Easily recall key concepts by creating these flashcards.
Browse courses on
Reinforcement Learning
Show steps
-
Identify the key concepts in your notes or textbooks
-
Write each concept on a flashcard.
-
Review the flashcards regularly.
Three other activities
Expand to see all activities and additional details
Show all six activities
Take practice quizzes on reinforcement learning
Show steps
Test your understanding of the material and identify areas where you need more practice.
Browse courses on
Reinforcement Learning
Show steps
-
Find some online practice quizzes on reinforcement learning.
-
Take the quizzes and review your results.
Read ‘Reinforcement Learning: An Introduction’ by Richard Sutton and Andrew Barto
Show steps
Expand on the material in this course by reading this introductory reference book.
View
Reinforcement Learning, second edition: An...
on Amazon
Show steps
-
Purchase the book online or from a bookstore.
-
Set aside some time each day to read the book.
-
Take notes as you read to help you remember the key points.
Create a reinforcement learning model in Python
Show steps
Solidify your understanding by practicing the application of reinforcement learning.
Browse courses on
Reinforcement Learning
Show steps
-
Find a dataset that you can use to create a reinforcement learning model.
-
Choose a reinforcement learning algorithm to use.
-
Implement the algorithm in Python.
-
Train and test the model.
-
Evaluate the results.
Read ‘Machine Learning for Algorithmic Trading: Predictive Models and Trading Strategies’ by Stefan Jansen
Show steps
Get a head start on the material in this course by getting some foundational knowledge with this book.
View
Machine Learning for Algorithmic Trading:...
on Amazon
Show steps
Show steps
Show steps
- Purchase the book online or from a bookstore.
- Set aside some time each day to read the book.
- Take notes as you read to help you remember the key points.
Practice with Python coding exercises
Show steps
Make sure your coding skills are up to par by completing these coding exercises.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find some online Python coding exercises.
- Set aside some time each week to work on the exercises.
Create flashcards of key reinforcement learning concepts
Show steps
Easily recall key concepts by creating these flashcards.
Browse courses on
Reinforcement Learning
Show steps
Show steps
Show steps
- Identify the key concepts in your notes or textbooks
- Write each concept on a flashcard.
- Review the flashcards regularly.
Three other activities
Expand to see all activities and additional details
Show all six activities
Take practice quizzes on reinforcement learning
Show steps
Test your understanding of the material and identify areas where you need more practice.
Browse courses on
Reinforcement Learning
Show steps
Show steps
Show steps
- Find some online practice quizzes on reinforcement learning.
- Take the quizzes and review your results.
Read ‘Reinforcement Learning: An Introduction’ by Richard Sutton and Andrew Barto
Show steps
Expand on the material in this course by reading this introductory reference book.
View
Reinforcement Learning, second edition: An...
on Amazon
Show steps
Show steps
Show steps
- Purchase the book online or from a bookstore.
- Set aside some time each day to read the book.
- Take notes as you read to help you remember the key points.
Create a reinforcement learning model in Python
Show steps
Solidify your understanding by practicing the application of reinforcement learning.
Browse courses on
Reinforcement Learning
Show steps
Show steps
Show steps
- Find a dataset that you can use to create a reinforcement learning model.
- Choose a reinforcement learning algorithm to use.
- Implement the algorithm in Python.
- Train and test the model.
- Evaluate the results.
Career center
Learners who complete Reinforcement Learning for Trading Strategies will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts do much more than just generate reports and dashboards. They apply advanced skills to translate raw data into actionable insights, enabling their employers to solve complex strategic and operational challenges and to make the right business decisions. This course would be especially useful to a Data Analyst whose job focuses on reinforcement learning.
Data Scientist
Data Scientist
Data Scientists analyze data to implement solutions which maximize value and minimize risks. They make data-driven predictions and develop algorithms that allow computers to mimic complex cognitive functions. This course would be of particular interest to Data Scientists whose work focuses on reinforcement learning.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, test, deploy, and maintain machine learning models and systems. They collaborate with scientists and business stakeholders to gather specifications, define architectures, and develop solutions. This course provides a strong foundation for those who wish to enter this field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts, or Quants, use mathematical and statistical models to analyze financial data. As part of the financial industry, Quants have the potential to make a significant impact on investment decisions and other aspects of financial planning. This course is a great starting point for anyone who aspires to become a Quant.
Financial Analyst
Financial Analyst
Financial Analysts help companies, organizations, and individuals make well-informed investment decisions. They analyze financial data to interpret trends, forecast performance, and make recommendations. This course provides a strong foundation for those who wish to enter this field.
Risk Manager
Risk Manager
Risk Managers identify, assess, and mitigate risks that may affect an organization's operations and financial performance. They develop and implement risk management strategies and policies to minimize potential losses. This course would be especially useful to a Risk Manager whose job focuses on reinforcement learning and financial trading.
Actuary
Actuary
Actuaries use mathematical and statistical skills to assess and manage financial risks. They work in a variety of industries, including insurance, finance, and consulting. This course would be especially useful to an Actuary whose job focuses on reinforcement learning and financial trading.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. They work in a variety of industries, including finance, healthcare, and market research. This course would be of particular interest to Statisticians whose work focuses on reinforcement learning.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work in a variety of industries, including finance, healthcare, and technology. This course would be of particular interest to Software Engineers whose work focuses on reinforcement learning.
Computer Programmer
Computer Programmer
Computer Programmers write and maintain code for a variety of software applications. They work in a variety of industries, including finance, healthcare, and technology. This course would be of particular interest to Computer Programmers whose work focuses on reinforcement learning.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. They work in a variety of industries, including finance, healthcare, and technology. This course would be of particular interest to Data Engineers whose work focuses on reinforcement learning.
Database Administrator
Database Administrator
Database Administrators design, build, and maintain databases. They work in a variety of industries, including finance, healthcare, and technology. This course may be useful to a Database Administrator whose job focuses on reinforcement learning in conjunction with databases.
Systems Analyst
Systems Analyst
Systems Analysts design, build, and maintain computer systems. They work in a variety of industries, including finance, healthcare, and technology. This course may be useful to a Systems Analyst whose job focuses on reinforcement learning in conjunction with systems analysis.
Business Analyst
Business Analyst
Business Analysts help organizations improve their performance by analyzing business processes and identifying opportunities for improvement. They work in a variety of industries, including finance, healthcare, and technology. This course may be useful to a Business Analyst whose job focuses on reinforcement learning in conjunction with business analysis.
Project Manager
Project Manager
Project Managers plan, organize, and execute projects. They work in a variety of industries, including finance, healthcare, and technology. This course may be useful to a Project Manager whose job focuses on reinforcement learning in conjunction with project management.
For more career information including salaries, visit:
OpenCourser.com/course/tzykdm/reinforcement
Reading list
We've selected 13 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 for Trading Strategies.
Provides a comprehensive introduction to the field of reinforcement learning, covering the fundamental concepts and algorithms. It valuable resource for anyone looking to gain a deeper understanding of reinforcement learning and its applications.
Provides a comprehensive overview of deep learning techniques for natural language processing. It covers a wide range of topics, including word embeddings, sequence models, and attention mechanisms.
Save
Provides a comprehensive introduction to time series analysis and forecasting. It covers a wide range of topics, including stationarity, seasonality, and trend analysis.
Provides a comprehensive overview of financial trading and investment. It covers a wide range of topics, including asset pricing, portfolio management, and risk management.
Save
Provides a practical guide to algorithmic trading. It covers a wide range of topics, including strategy development, backtesting, and live trading.
Provides a comprehensive overview of machine learning techniques for finance. It covers a wide range of topics, including data preparation, feature engineering, model selection, and risk management.
Provides a comprehensive overview of artificial intelligence techniques for finance. It covers a wide range of topics, including machine learning, natural language processing, and computer vision.
Provides a classic guide to value investing. It valuable resource for anyone looking to learn more about the principles of investing.
Provides a comprehensive guide to security analysis. It valuable resource for anyone looking to learn more about the process of valuing stocks.
Provides a simple and effective approach to investing. It valuable resource for anyone looking to learn more about how to beat the market.
Provides a timeless guide to growth investing. It valuable resource for anyone looking to learn more about how to identify and invest in growing companies.
Save
Provides a practical guide to investing. It valuable resource for anyone looking to learn more about how to make money in the stock market.
Provides a collection of essays by Warren Buffett. It valuable resource for anyone looking to learn more about the principles of investing.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/tzykdm/reinforcement
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Effort
4 weeks of study, 5 hours per week
Level
Intermediate
Via
Coursera
Institutions
Google Cloud
New York Institute of Finance
Instructor
Jack Farmer
Language
English
Transcripts
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This course belongs to a
collection
called
Machine Learning for Trading. It's comprised of three courses:
- Using Machine Learning in Trading and Finance
- Reinforcement Learning for Trading Strategies (viewing)
- Introduction to Trading, Machine Learning & GCP
Good to know
Reinforces key concepts of the Machine Learning for Trading specialization
Builds on foundational knowledge of financial markets and reinforcement learning
Requires advanced competency in Python programming and familiarity with machine learning libraries
Emphasizes the application of reinforcement learning in trading strategies
Provides practical guidance on building and implementing reinforcement learning trading systems
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