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Jack Farmer
This course provides the foundation for developing advanced trading strategies using machine learning techniques. In this course, you’ll review the key components that are common to every trading strategy, no matter how complex. You’ll be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading. By the end of the course, you will be able to design basic quantitative trading strategies, build machine learning models using Keras and TensorFlow, build a pair trading strategy prediction model and back test it, and build a momentum-based trading model and back test it.
Read more
This course provides the foundation for developing advanced trading strategies using machine learning techniques. In this course, you’ll review the key components that are common to every trading strategy, no matter how complex. You’ll be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading. By the end of the course, you will be able to design basic quantitative trading strategies, build machine learning models using Keras and TensorFlow, build a pair trading strategy prediction model and back test it, and build a momentum-based trading model and back test it.
Read more
This course provides the foundation for developing advanced trading strategies using machine learning techniques. In this course, you’ll review the key components that are common to every trading strategy, no matter how complex. You’ll be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading. By the end of the course, you will be able to design basic quantitative trading strategies, build machine learning models using Keras and TensorFlow, build a pair trading strategy prediction model and back test it, and build a momentum-based trading model and back test it.
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/biomj0/using
What's inside
Syllabus
Introduction to Quantitative Trading and TensorFlow
In this module we discuss the key components that are common to every trading strategy, no matter how complex. This foundation will help guide you as you develop more advanced strategies using machine learning techniques.
Read more
Syllabus
Introduction to Quantitative Trading and TensorFlow
In this module we discuss the key components that are common to every trading strategy, no matter how complex. This foundation will help guide you as you develop more advanced strategies using machine learning techniques.
Read more
Introduction to TensorFlow
Training neural networks with Tensorflow 2 and Keras
Build a Momentum-based Trading System
Momentum trading is a strategy in which traders buy or sell assets according to the strength of recent price trends. Price momentum is similar to momentum in physics, where mass multiplied by velocity determines the persistence with which an object will follow its current path (like a heavy train on a track). In financial markets, however, momentum is determined by other factors like trading volume and rate of price changes. Momentum traders bet that an asset price that is moving strongly in a given direction will continue to move in that direction until the trend loses strength or reverses. This module teaches you all about momentum trading.
Build a Pair Trading Strategy Prediction Model
In this module, we introduce pairs trading. We will discuss what pairs trading is, and how you can make money doing it. We will discuss what you need to know about the members to form a suitable pair.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches foundational knowledge of quantitative trading strategies, including pairs trading and momentum trading
Introduces TensorFlow, an industry-standard machine learning library
Provides hands-on exercises to build machine learning models for trading strategies, using Keras and TensorFlow
Assumes familiarity with Python programming and machine learning libraries, which may be a barrier for beginners
Requires advanced competency in Python programming, which may not be suitable for all learners
Assumes background knowledge in statistics and financial markets, which may be a prerequisite for some learners
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Reviews summary
Varied quality ml course for trading
Students taking this largely positive course say they appreciate the engaging trading strategies covered in this course, and note that the concepts and algorithms taught are great. They also comment that the labs are useful, especially for people who have prior knowledge of coding and trading basics. However, learners also note that a number of the labs in this course are broken, outdated, or difficult to use. These issues seem to be most frequent for labs related to Auquan, TensorFlow, and other Google Cloud platforms and tools.
Many students find that the teaching quality in this course is good, with insightful lectures and a logical flow of information.
"Teaching was really good."
"it is a very good specialization and it is easy to follow and learn."
"Really appreciate the learning and knowledge around the strategies and theory."
This course covers engaging trading strategies that many learners say are useful and applicable to real-world trading.
"I really like the material but the Google platform had bugs."
"This ML course gives you the necessary tools that will propel you to the new era of technology."
"The theories are good and very insightful."
Many learners mention that the Google Cloud Platform often has bugs and technical issues that can hinder progress.
"Although the often glitches in the Google Cloud platform prevented me to complete the exercises, the course material is very useful."
"The main problem with this specialization is that the Machine Learning Stuff and Finance stuff are really separated (Google, NY univ)."
"I don't think I got as much out of it as I would have liked. The concepts of the course are great and if they can fix the technical issues I encountered, it would be a really great learning vehicle. As it stands, it is a work in progress."
Learners note that some of the course material, especially the labs, is outdated and in need of updating.
"The Auquan part is directly useful as one needs a good backtesting tool and this is probably the best/only one."
"Looks more like mash up differt courses . Many Qwiklabs do not work esop those related to finance in Trading Strategies using auquan_toolbox"
"as I may end up using a different cloud and ML library."
A number of learners note that this course contains broken or outdated labs, which can be a major obstacle to completing the course.
"the code fix you suggested for the Yahoo Finance changes no longer works."
"The last two labs of the course couldn't work."
"Some of the labs in this course are simply broken and do not work properly."
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 Using Machine Learning in Trading and Finance with these
activities:
Review financial markets concepts
Show steps
Sharpen your understanding of financial markets and instruments, ensuring a strong foundation for the course content, which assumes some familiarity with these concepts.
Browse courses on
Financial Markets
Show steps
-
Read articles or online resources on financial markets.
-
Review your notes or textbooks on financial markets concepts.
-
Watch videos or attend webinars on financial markets.
Review statistics background
Show steps
Refresh your understanding of statistics, including concepts like expected values, standard deviation, and linear regression, which are fundamental to the course content.
Browse courses on
Statistics
Show steps
-
Review your notes or textbooks on basic statistics concepts.
-
Work through practice problems related to expected values, standard deviation, and linear regression.
-
Take an online quiz or mock test to assess your understanding.
Practice with Python coding exercises
Show steps
Strengthen your Python coding skills by solving coding exercises and puzzles, which will enhance your ability to apply these skills in the course assignments.
Browse courses on
Python Programming
Show steps
-
Find online coding challenges or exercises related to Python.
-
Allocate specific time each week to practice coding.
-
Review your solutions and identify areas for improvement.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Read 'Machine Learning with Python for Beginners'
Show steps
Gain a foundational understanding of machine learning concepts and Python implementation by reading this book, which provides a gentle introduction to the field.
Show steps
-
Purchase or borrow a copy of the book.
-
Set aside dedicated time each day for reading.
-
Take notes and highlight important concepts.
Follow tutorials on Tensorflow and Keras
Show steps
Expand your technical proficiency by following tutorials on Tensorflow and Keras, familiarizing yourself with their functionalities and enhancing your ability to apply them in the course assignments.
Browse courses on
TensorFlow
Show steps
-
Find online tutorials or documentation on Tensorflow and Keras.
-
Follow the tutorials step-by-step, implementing the code examples.
-
Experiment with different parameters and settings in your code.
Assist fellow students in the course forums
Show steps
Boost your understanding of the course material by helping and assisting other students in the course forums, reinforcing your own learning while fostering a supportive community.
Show steps
-
Regularly check the course forums for questions or discussions.
-
Provide thoughtful and informative responses to other students' queries.
-
Share your own insights and experiences related to the course topics.
Create a momentum trading strategy prediction model
Show steps
Solidify your understanding of momentum trading principles and develop practical skills in building machine learning models by creating a momentum trading strategy prediction model.
Browse courses on
Momentum Trading
Show steps
-
Gather historical data on financial markets.
-
Preprocess and clean the data.
-
Develop a momentum trading strategy.
-
Train a machine learning model using your strategy.
-
Evaluate and backtest the performance of the model.
Develop a visualization of quantitative trading strategies
Show steps
Deepen your understanding of quantitative trading strategies by creating a visualization that illustrates their components and key concepts, enhancing your ability to communicate these strategies effectively.
Browse courses on
Quantitative Trading
Show steps
-
Choose a specific quantitative trading strategy to visualize.
-
Gather data related to the strategy.
-
Select an appropriate visualization tool or library.
-
Design and create the visualization.
-
Share your visualization with others for feedback and discussion.
Participate in a machine learning hackathon
Show steps
Challenge yourself and enhance your problem-solving abilities by participating in a machine learning hackathon, where you can collaborate with others and apply your skills in a competitive environment.
Browse courses on
Machine Learning
Show steps
-
Identify and register for an upcoming machine learning hackathon.
-
Form a team or participate individually.
-
Develop and implement a machine learning solution within the given time frame.
-
Present your solution to a panel of judges or attendees.
Review financial markets concepts
Show steps
Sharpen your understanding of financial markets and instruments, ensuring a strong foundation for the course content, which assumes some familiarity with these concepts.
Browse courses on
Financial Markets
Show steps
Show steps
Show steps
- Read articles or online resources on financial markets.
- Review your notes or textbooks on financial markets concepts.
- Watch videos or attend webinars on financial markets.
Review statistics background
Show steps
Refresh your understanding of statistics, including concepts like expected values, standard deviation, and linear regression, which are fundamental to the course content.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review your notes or textbooks on basic statistics concepts.
- Work through practice problems related to expected values, standard deviation, and linear regression.
- Take an online quiz or mock test to assess your understanding.
Practice with Python coding exercises
Show steps
Strengthen your Python coding skills by solving coding exercises and puzzles, which will enhance your ability to apply these skills in the course assignments.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Find online coding challenges or exercises related to Python.
- Allocate specific time each week to practice coding.
- Review your solutions and identify areas for improvement.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Read 'Machine Learning with Python for Beginners'
Show steps
Gain a foundational understanding of machine learning concepts and Python implementation by reading this book, which provides a gentle introduction to the field.
Show steps
Show steps
Show steps
- Purchase or borrow a copy of the book.
- Set aside dedicated time each day for reading.
- Take notes and highlight important concepts.
Follow tutorials on Tensorflow and Keras
Show steps
Expand your technical proficiency by following tutorials on Tensorflow and Keras, familiarizing yourself with their functionalities and enhancing your ability to apply them in the course assignments.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Find online tutorials or documentation on Tensorflow and Keras.
- Follow the tutorials step-by-step, implementing the code examples.
- Experiment with different parameters and settings in your code.
Assist fellow students in the course forums
Show steps
Boost your understanding of the course material by helping and assisting other students in the course forums, reinforcing your own learning while fostering a supportive community.
Show steps
Show steps
Show steps
- Regularly check the course forums for questions or discussions.
- Provide thoughtful and informative responses to other students' queries.
- Share your own insights and experiences related to the course topics.
Create a momentum trading strategy prediction model
Show steps
Solidify your understanding of momentum trading principles and develop practical skills in building machine learning models by creating a momentum trading strategy prediction model.
Browse courses on
Momentum Trading
Show steps
Show steps
Show steps
- Gather historical data on financial markets.
- Preprocess and clean the data.
- Develop a momentum trading strategy.
- Train a machine learning model using your strategy.
- Evaluate and backtest the performance of the model.
Develop a visualization of quantitative trading strategies
Show steps
Deepen your understanding of quantitative trading strategies by creating a visualization that illustrates their components and key concepts, enhancing your ability to communicate these strategies effectively.
Browse courses on
Quantitative Trading
Show steps
Show steps
Show steps
- Choose a specific quantitative trading strategy to visualize.
- Gather data related to the strategy.
- Select an appropriate visualization tool or library.
- Design and create the visualization.
- Share your visualization with others for feedback and discussion.
Participate in a machine learning hackathon
Show steps
Challenge yourself and enhance your problem-solving abilities by participating in a machine learning hackathon, where you can collaborate with others and apply your skills in a competitive environment.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify and register for an upcoming machine learning hackathon.
- Form a team or participate individually.
- Develop and implement a machine learning solution within the given time frame.
- Present your solution to a panel of judges or attendees.
Career center
Learners who complete Using Machine Learning in Trading and Finance will develop knowledge and skills
that may be useful to these careers:
Quantitative Analyst
Quantitative Analyst
In this role, you will develop and use mathematical and statistical models to analyze financial data. You will use these models to make predictions about future market trends and to identify investment opportunities. The Using Machine Learning in Trading and Finance course can help you build a strong foundation in machine learning, which is an essential skill for Quantitative Analysts. You will also learn about specific applications of machine learning in finance, which will give you a competitive advantage in this field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. They work in a variety of industries, including finance, healthcare, and manufacturing. Machine Learning Engineers typically have a strong understanding of computer science, mathematics, and statistics. The Using Machine Learning in Trading and Finance course can help Machine Learning Engineers to improve their machine learning skills. The course covers specific applications of machine learning in finance, which will give Machine Learning Engineers the knowledge and skills they need to succeed in this industry.
Data Scientist
Data Scientist
Data Scientists use their knowledge of mathematics, statistics, and computer science to extract insights from data. They work in a variety of industries, including finance, healthcare, and retail. Data Scientists typically have a strong understanding of data analysis techniques and machine learning algorithms. The Using Machine Learning in Trading and Finance course can help Data Scientists to improve their machine learning skills. The course covers specific applications of machine learning in finance, which will give Data Scientists the knowledge and skills they need to succeed in this industry.
Data Analyst
Data Analyst
As a Data Analyst, you will lead your organization's data collection and analysis efforts, using this data to identify trends, make predictions, and improve decision making. The Using Machine Learning in Trading and Finance course can provide you with strong quantitative and analytical skills. You will learn about data cleaning, feature engineering, model building, and other techniques that are essential for success in this role. The course also covers specific applications of machine learning in finance, which will give you a strong foundation for working in this industry.
Statistician
Statistician
Statisticians use mathematical and statistical techniques to collect, analyze, and interpret data. They work in a variety of industries, including finance, healthcare, and manufacturing. Statisticians typically have a strong understanding of mathematics, statistics, and data analysis techniques. The Using Machine Learning in Trading and Finance course can help Statisticians to improve their machine learning skills. The course covers specific applications of machine learning in finance, which will give Statisticians the knowledge and skills they need to succeed in this industry.
Risk Manager
Risk Manager
Risk Managers are responsible for identifying and managing risks within organizations. They develop and implement risk management strategies to protect organizations from financial losses and other risks. Risk Managers typically have a strong understanding of financial markets and risk management principles. The Using Machine Learning in Trading and Finance course can help Risk Managers to improve their risk management skills. Machine learning can be used to analyze financial data, identify trends, and make predictions about future market movements. This information can help Risk Managers to identify and mitigate risks more effectively.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of financial markets to help businesses make informed decisions about investments and other financial matters. They analyze financial data, identify trends, and make recommendations based on their findings. The Using Machine Learning in Trading and Finance course provides a strong foundation in machine learning, which can be a valuable tool for Financial Analysts. Machine learning can be used to automate data analysis tasks, identify patterns in data, and make predictions about future trends. This can help Financial Analysts to make more informed decisions and to improve their overall performance.
Investment Banker
Investment Banker
Investment Bankers provide financial advice to corporations and governments. They help these organizations to raise capital, make acquisitions, and manage their finances. Investment Bankers typically have a strong understanding of financial markets and investment strategies. The Using Machine Learning in Trading and Finance course can help Investment Bankers to improve their financial analysis skills. Machine learning can be used to analyze financial data, identify trends, and make predictions about future market movements. This information can help Investment Bankers to provide better advice to their clients and to help them make better financial decisions.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess and manage risk. They work in a variety of industries, including insurance, finance, and healthcare. Actuaries typically have a strong understanding of mathematics, statistics, and risk management principles. The Using Machine Learning in Trading and Finance course can help Actuaries to improve their risk assessment skills. Machine learning can be used to analyze financial data, identify trends, and make predictions about future market movements. This information can help Actuaries to assess and manage risks more effectively.
Portfolio Manager
Portfolio Manager
Portfolio Managers are responsible for managing investment portfolios on behalf of their clients. They make decisions about which investments to buy and sell, and they monitor the performance of these investments. The Using Machine Learning in Trading and Finance course can help Portfolio Managers to make more informed investment decisions. Machine learning can be used to analyze financial data, identify trends, and make predictions about future market movements. This information can help Portfolio Managers to select the right investments for their clients and to maximize their returns.
Hedge Fund Manager
Hedge Fund Manager
Hedge Fund Managers are responsible for managing hedge funds, which are investment funds that use advanced investment strategies to generate high returns. Hedge Fund Managers typically have a strong understanding of financial markets and investment strategies. The Using Machine Learning in Trading and Finance course can help Hedge Fund Managers to improve their investment performance. Machine learning can be used to analyze financial data, identify trends, and make predictions about future market movements. This information can help Hedge Fund Managers to make better investment decisions and to generate higher returns for their clients.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work in a variety of industries, including finance, healthcare, and retail. Software Engineers typically have a strong understanding of computer science and programming languages. The Using Machine Learning in Trading and Finance course may provide you with some useful skills for this role, such as machine learning and data analysis. However, it is important to note that this course is not a substitute for a computer science degree.
Financial Planner
Financial Planner
Financial Planners help individuals and families plan for their financial future. They provide advice on a variety of topics, including budgeting, saving, investing, and retirement planning. Financial Planners typically have a strong understanding of financial planning principles and investment strategies. The Using Machine Learning in Trading and Finance course may provide you with some useful skills for this role, such as data analysis and investment strategies. However, it is important to note that this course is not a substitute for a financial planning degree.
Market Research Analyst
Market Research Analyst
Market Research Analysts conduct research to understand the needs of customers and the competitive landscape. They use this research to develop marketing strategies and products. Market Research Analysts typically have a strong understanding of research methods and data analysis techniques. The Using Machine Learning in Trading and Finance course may provide you with some useful skills for this role, such as data analysis and problem solving. However, it is important to note that this course is not a substitute for a market research degree.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business and technology to analyze business problems and develop solutions. They work in a variety of industries, including finance, healthcare, and manufacturing. Business Analysts typically have a strong understanding of business analysis techniques and data analysis techniques. The Using Machine Learning in Trading and Finance course may provide you with some useful skills for this role, such as data analysis and problem solving. However, it is important to note that this course is not a substitute for a business analysis degree.
For more career information including salaries, visit:
OpenCourser.com/course/biomj0/using
Reading list
We've selected 11 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
Using Machine Learning in Trading and Finance.
Provides essential concepts and methods for applying machine learning to asset management. It is particularly useful for gaining a deeper understanding of the technical aspects of machine learning as well as practical applications.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It is particularly useful for those interested in learning the theoretical foundations of machine learning.
Provides a comprehensive overview of statistical learning. It is particularly useful for those interested in learning the theoretical foundations of statistical learning.
Provides a comprehensive overview of machine learning in finance. It is particularly useful for those interested in learning how to use machine learning for financial applications.
Provides a comprehensive overview of mathematics for machine learning. It is particularly useful for those interested in learning the mathematical foundations of machine learning.
Provides a comprehensive overview of statistical learning from a data mining perspective. It is particularly useful for those interested in learning how to use statistical learning for data mining tasks.
Provides a practical guide to using machine learning with Scikit-Learn, Keras, and TensorFlow. It is particularly useful for those interested in learning how to use these libraries for machine learning tasks.
Provides a comprehensive guide to deep learning using Python. It is particularly useful for those interested in learning the fundamentals of deep learning.
Provides a comprehensive guide to using Python for data analysis. It is particularly useful for those interested in learning how to use Python for data manipulation and analysis.
Provides a hands-on introduction to deep learning using Fastai and PyTorch. It is particularly useful for those interested in building their own deep learning models.
Provides a comprehensive guide to data science from scratch. It is particularly useful for those interested in learning the fundamentals of data science.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/biomj0/using
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Trading Basics
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 (viewing)
- Reinforcement Learning for Trading Strategies
- Introduction to Trading, Machine Learning & GCP
Good to know
Teaches foundational knowledge of quantitative trading strategies, including pairs trading and momentum trading
Introduces TensorFlow, an industry-standard machine learning library
Provides hands-on exercises to build machine learning models for trading strategies, using Keras and TensorFlow
Assumes familiarity with Python programming and machine learning libraries, which may be a barrier for beginners
Requires advanced competency in Python programming, which may not be suitable for all learners
Assumes background knowledge in statistics and financial markets, which may be a prerequisite for some learners
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