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Jack Farmer
In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks.
Read more
In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks.
Read more
In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks.
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/ibe1h4/introduction
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What's inside
Syllabus
Introduction to Trading with Machine Learning on Google Cloud
In this module you will be introduced to the fundamentals of trading. You will also be introduced to machine learning. Machine Learning is both an art that involves knowledge of the right mix of parameters that yields accurate, generalized models and a science that involves knowledge of the theory to solve specific types of problems.
Read more
Syllabus
Introduction to Trading with Machine Learning on Google Cloud
In this module you will be introduced to the fundamentals of trading. You will also be introduced to machine learning. Machine Learning is both an art that involves knowledge of the right mix of parameters that yields accurate, generalized models and a science that involves knowledge of the theory to solve specific types of problems.
Read more
Supervised Learning with BigQuery ML
In this module you will be introduced to supervised machine learning and some relevant algorithms commonly applied to trading problems. You will get some hands-on experience building a regression model using BigQuery Machine Learning
Time Series and ARIMA Modeling
In this module you will learn about ARIMA modeling and how it is applied to time series data. You will get hands-on experience building an ARIMA model for a financial dataset.
Introduction to Neural Networks and Deep Learning
In this module you'll learn about neural networks and how they relate to deep learning. You'll also learn how to gauge model generalization using regularization, and cross-validation. Also, you'll be introduced to Google Cloud Platform (GCP). Specifically, you'll be shown how to leverage GCP for implementing trading techniques.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines the fundamentals of trading with machine learning on Google Cloud, which is standard in industry
Introduces machine learning, which is helpful for generalized models
Explores supervised learning with BigQuery ML, which helps learners build regression models
Instructs the theory of ARIMA modeling and how it is applied to time series data, which is helpful for solving specific types of trading problems
Guides the way to leverage GCP for implementing trading techniques, which is relevant to industry
Prerequisites include advanced competency in Python programming and familiarity with pertinent libraries for machine learning
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Reviews summary
Ml for trading on gcp
learners say this course introduces artificial intelligence concepts and Google tools used in the trading industry. With a mix of finance theory and labs, students learn to apply their knowledge to real-world scenarios. Despite comments on the course being somewhat disjointed and lacking in some areas, students largely view this as a positive introduction to trading and machine learning.
foundational but not exhaustive
"learners say this course introduces artificial intelligence concepts and Google tools used in the trading industry."
mix of finance and tech theory and hands-on labs
"learners say this course introduces artificial intelligence concepts and Google tools used in the trading industry."
"With a mix of finance theory and labs, students learn to apply their knowledge to real-world scenarios."
some disjointedness and unevenness
"Despite comments on the course being somewhat disjointed and lacking in some areas, students largely view this as a positive introduction to trading and machine learning."
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 Introduction to Trading, Machine Learning & GCP with these
activities:
Reading: An Introduction to Statistical Learning
Show steps
Review the foundational concepts of statistical learning and supervised learning algorithms to strengthen your understanding of the course material.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read Chapter 1: Introduction
-
Read Chapter 2: Linear Regression
-
Complete the end-of-chapter exercises
Solving Practice Problems on Supervised Learning
Show steps
Reinforce your understanding of supervised learning algorithms and their applications by solving a series of practice problems.
Browse courses on
Supervised Learning
Show steps
-
Find practice problems online or in textbooks
-
Attempt to solve the problems on your own
-
Review the solutions and compare your answers
Read Introduction to Machine Learning with Python
Show steps
Get a solid foundation on machine learning concepts, algorithms, and Python libraries commonly used in trading.
View
Introduction to Machine Learning with Python: A...
on Amazon
Show steps
-
Read Chapters 1-3 to understand fundamental concepts.
-
Install Python libraries and code examples from the book.
-
Work through practice exercises to reinforce learning.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Complete Google Cloud's Machine Learning Crash Course
Show steps
Gain hands-on experience building machine learning models on Google Cloud Platform.
Browse courses on
Machine Learning
Show steps
-
Follow the tutorials in the provided link.
-
Experiment with different machine learning algorithms.
-
Deploy a trained model to the cloud.
Participating in Study Groups for ARIMA Modeling
Show steps
Enhance your understanding of ARIMA modeling by participating in study groups, discussing concepts, and collaborating on projects.
Show steps
-
Find or create a study group with peers
-
Meet regularly to discuss course material
-
Work together on practice problems and projects
Solve ARIMA Modeling Practice Problems
Show steps
Develop a deep understanding of ARIMA modeling and its application to time series data.
Show steps
-
Collect practice problems from online resources.
-
Solve problems using the ARIMA modeling process.
-
Check solutions and identify areas for improvement.
Following Tutorials on Neural Networks and Deep Learning
Show steps
Expand your knowledge of neural networks and deep learning by following guided tutorials and implementing practical examples.
Browse courses on
Neural Networks
Show steps
-
Identify relevant tutorials online or in books
-
Follow the tutorials step-by-step
-
Experiment with different parameters and settings
Creating a Machine Learning Model for Stock Price Prediction
Show steps
Apply the concepts learned in the course to develop a practical application for stock price prediction, enhancing your understanding of model building and evaluation.
Browse courses on
Model Creation
Show steps
-
Gather historical stock data
-
Clean and prepare the data
-
Build a machine learning model
-
Evaluate the model's performance
Attending a Workshop on Machine Learning for Trading
Show steps
Supplement your learning by attending a workshop focused on applying machine learning techniques in the context of trading, gaining insights from experts and networking with peers.
Show steps
-
Identify and register for a relevant workshop
-
Attend the workshop and actively participate
-
Network with other attendees
Build a Simple Trading Bot with Python
Show steps
Apply course concepts to building a real-world trading bot.
Browse courses on
Trading
Show steps
-
Choose a trading strategy and collect historical data.
-
Develop a machine learning model to predict price movements.
-
Implement the trading bot and test its performance.
Building a Trading Strategy Backtester
Show steps
Demonstrate your mastery of trading strategy evaluation by creating a backtester that simulates the performance of different trading strategies using historical data.
Browse courses on
Trading Strategies
Show steps
-
Design the backtester
-
Implement the backtester
-
Test the backtester using different trading strategies
Writing: A Comprehensive Report on Time Series Analysis Techniques
Show steps
Consolidate your knowledge of time series analysis techniques by creating a comprehensive report, summarizing the key concepts and their applications in trading.
Browse courses on
Time Series Analysis
Show steps
-
Research and gather information on different time series analysis techniques
-
Organize and structure the report
-
Write the report
Mentoring New Students in the Course
Show steps
Solidify your own understanding of the course material by helping new students navigate the content, providing support and guidance.
Show steps
-
Volunteer to be a mentor
-
Meet with mentees regularly
-
Provide guidance and support
Reading: An Introduction to Statistical Learning
Show steps
Review the foundational concepts of statistical learning and supervised learning algorithms to strengthen your understanding of the course material.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read Chapter 1: Introduction
- Read Chapter 2: Linear Regression
- Complete the end-of-chapter exercises
Solving Practice Problems on Supervised Learning
Show steps
Reinforce your understanding of supervised learning algorithms and their applications by solving a series of practice problems.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks
- Attempt to solve the problems on your own
- Review the solutions and compare your answers
Read Introduction to Machine Learning with Python
Show steps
Get a solid foundation on machine learning concepts, algorithms, and Python libraries commonly used in trading.
View
Introduction to Machine Learning with Python: A...
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-3 to understand fundamental concepts.
- Install Python libraries and code examples from the book.
- Work through practice exercises to reinforce learning.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Complete Google Cloud's Machine Learning Crash Course
Show steps
Gain hands-on experience building machine learning models on Google Cloud Platform.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Follow the tutorials in the provided link.
- Experiment with different machine learning algorithms.
- Deploy a trained model to the cloud.
Participating in Study Groups for ARIMA Modeling
Show steps
Enhance your understanding of ARIMA modeling by participating in study groups, discussing concepts, and collaborating on projects.
Show steps
Show steps
Show steps
- Find or create a study group with peers
- Meet regularly to discuss course material
- Work together on practice problems and projects
Solve ARIMA Modeling Practice Problems
Show steps
Develop a deep understanding of ARIMA modeling and its application to time series data.
Show steps
Show steps
Show steps
- Collect practice problems from online resources.
- Solve problems using the ARIMA modeling process.
- Check solutions and identify areas for improvement.
Following Tutorials on Neural Networks and Deep Learning
Show steps
Expand your knowledge of neural networks and deep learning by following guided tutorials and implementing practical examples.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Identify relevant tutorials online or in books
- Follow the tutorials step-by-step
- Experiment with different parameters and settings
Creating a Machine Learning Model for Stock Price Prediction
Show steps
Apply the concepts learned in the course to develop a practical application for stock price prediction, enhancing your understanding of model building and evaluation.
Browse courses on
Model Creation
Show steps
Show steps
Show steps
- Gather historical stock data
- Clean and prepare the data
- Build a machine learning model
- Evaluate the model's performance
Attending a Workshop on Machine Learning for Trading
Show steps
Supplement your learning by attending a workshop focused on applying machine learning techniques in the context of trading, gaining insights from experts and networking with peers.
Show steps
Show steps
Show steps
- Identify and register for a relevant workshop
- Attend the workshop and actively participate
- Network with other attendees
Build a Simple Trading Bot with Python
Show steps
Apply course concepts to building a real-world trading bot.
Browse courses on
Trading
Show steps
Show steps
Show steps
- Choose a trading strategy and collect historical data.
- Develop a machine learning model to predict price movements.
- Implement the trading bot and test its performance.
Building a Trading Strategy Backtester
Show steps
Demonstrate your mastery of trading strategy evaluation by creating a backtester that simulates the performance of different trading strategies using historical data.
Browse courses on
Trading Strategies
Show steps
Show steps
Show steps
- Design the backtester
- Implement the backtester
- Test the backtester using different trading strategies
Writing: A Comprehensive Report on Time Series Analysis Techniques
Show steps
Consolidate your knowledge of time series analysis techniques by creating a comprehensive report, summarizing the key concepts and their applications in trading.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Research and gather information on different time series analysis techniques
- Organize and structure the report
- Write the report
Mentoring New Students in the Course
Show steps
Solidify your own understanding of the course material by helping new students navigate the content, providing support and guidance.
Show steps
Show steps
Show steps
- Volunteer to be a mentor
- Meet with mentees regularly
- Provide guidance and support
Career center
Learners who complete Introduction to Trading, Machine Learning & GCP will develop knowledge and skills
that may be useful to these careers:
Quantitative Analyst
Quantitative Analyst
Quantitative analysts build quantitative models to develop trading strategies. This course provides the theoretical foundation necessary to build such models. You will learn how to identify profit sources, identify steps needed to create development and implementation backtesters, and gauge how well a model generalizes its learning. You will also gain practical experience using the Google Cloud Platform to implement trading techniques, which is in high demand.
Trading Analyst
Trading Analyst
Trading analysts identify trading opportunities based on analysis of data. This course can help you begin or advance a career as a trading analyst. It will help you build a foundation in financial markets, trading techniques, and machine learning. You will also gain practical experience using the Google Cloud Platform to implement trading techniques, which is in high demand.
Financial Analyst
Financial Analyst
Financial analysts evaluate the performance of companies and make investment recommendations. This course provides the quantitative foundation necessary to succeed in this role. You will learn how to identify profit sources, identify steps needed to create development and implementation backtesters, and gauge how well a model generalizes its learning. You will also gain practical experience using the Google Cloud Platform to implement trading techniques, which is in high demand.
Data Scientist
Data Scientist
Data scientists use data to solve business problems. This course provides a strong foundation in the machine learning techniques used by data scientists. You will learn how to build predictive models, gauge how well a model generalizes its learning, and identify steps needed to create development and implementation backtesters.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers build and deploy machine learning models. This course provides a strong foundation in the machine learning techniques used by machine learning engineers. You will learn how to build predictive models, gauge how well a model generalizes its learning, and identify steps needed to create development and implementation backtesters.
Software Engineer
Software Engineer
Software engineers develop and maintain software applications. This course provides a strong foundation in the machine learning techniques used by software engineers. You will learn how to identify profit sources, identify steps needed to create development and implementation backtesters, and gauge how well a model generalizes its learning.
Financial Engineer
Financial Engineer
Financial engineers design and develop financial products. This course provides the quantitative foundation necessary to succeed in this role. You will learn how to identify profit sources, identify steps needed to create development and implementation backtesters, and gauge how well a model generalizes its learning.
Risk Manager
Risk Manager
Risk managers identify and assess financial risks. This course provides a strong foundation in the quantitative techniques used by risk managers. You will learn how to identify profit sources, identify steps needed to create development and implementation backtesters, and gauge how well a model generalizes its learning.
Portfolio Manager
Portfolio Manager
Portfolio managers manage investment portfolios. This course provides a strong foundation in the quantitative techniques used by portfolio managers. You will learn how to identify profit sources, identify steps needed to create development and implementation backtesters, and gauge how well a model generalizes its learning.
Investment Analyst
Investment Analyst
Investment analysts evaluate the performance of companies and make investment recommendations. This course provides a strong foundation in the quantitative techniques used by investment analysts. You will learn how to identify profit sources, identify steps needed to create development and implementation backtesters, and gauge how well a model generalizes its learning.
Hedge Fund Manager
Hedge Fund Manager
Hedge fund managers manage investment portfolios. This course provides a quantitative foundation necessary to succeed in this role. You will learn how to identify profit sources, identify steps needed to create development and implementation backtesters, and gauge how well a model generalizes its learning.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk. This course provides a strong foundation in the quantitative techniques used by actuaries. You will learn how to identify profit sources, identify steps needed to create development and implementation backtesters, and gauge how well a model generalizes its learning.
Data Analyst
Data Analyst
Data analysts use data to solve business problems. This course provides a strong foundation in the quantitative techniques used by data analysts. You will learn how to identify profit sources, identify steps needed to create development and implementation backtesters, and gauge how well a model generalizes its learning.
Statistician
Statistician
Statisticians use mathematical and statistical techniques to analyze data. This course provides a strong foundation in the quantitative techniques used by statisticians. You will learn how to identify profit sources, identify steps needed to create development and implementation backtesters, and gauge how well a model generalizes its learning.
Economist
Economist
Economists study the economy and make predictions about its future. This course provides a strong foundation in the quantitative techniques used by economists. You will learn how to identify profit sources, identify steps needed to create development and implementation backtesters, and gauge how well a model generalizes its learning.
For more career information including salaries, visit:
OpenCourser.com/course/ibe1h4/introduction
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
Introduction to Trading, Machine Learning & GCP.
Provides a thorough examination of machine learning techniques and their applications in asset management. It offers valuable insights into data preparation, model building, and performance evaluation, making it a highly relevant resource for the course.
Save
This classic work provides a rigorous foundation for time series analysis, including ARIMA modeling. It offers a comprehensive treatment of the subject, making it a valuable reference for understanding the time series concepts covered in the course.
Offers a practical guide to using Python for financial data analysis and trading. It covers essential topics such as data acquisition, manipulation, and visualization, providing a solid foundation for the course's machine learning component.
Provides a comprehensive overview of market microstructure, including trading mechanisms, order types, and market liquidity. It offers valuable insights into the practical aspects of trading, complementing the course's theoretical foundations.
Provides a comprehensive guide to building machine learning models using popular Python libraries. It covers topics such as data preprocessing, feature engineering, and model evaluation, offering practical insights for the course's machine learning projects.
Provides a comprehensive introduction to deep learning using Python and the Keras library. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks, offering a valuable resource for understanding the deep learning concepts in the course.
Provides a comprehensive overview of the mathematical foundations of machine learning. It covers topics such as linear algebra, calculus, and probability theory, offering a valuable resource for learners who wish to delve deeper into the theoretical underpinnings of machine learning.
Introduces deep learning concepts and techniques using the fastai library. It provides a hands-on approach to building and training neural networks, complementing the course's coverage of deep learning fundamentals.
Offers a comprehensive overview of risk and portfolio management principles. It covers topics such as risk measurement, asset allocation, and performance evaluation, providing a solid grounding in the financial aspects of trading.
Serves as a comprehensive guide to data analysis using Python. It covers essential libraries such as NumPy, Pandas, and Matplotlib, providing a strong foundation for the data manipulation and visualization tasks involved in the course.
Provides a comprehensive overview of deep learning techniques for natural language processing. It covers topics such as word embeddings, sequence modeling, and attention mechanisms, offering valuable insights for understanding the course's machine learning applications in text analysis.
Provides a comprehensive introduction to machine learning using R. It covers topics such as data preprocessing, model building, and model evaluation, offering a valuable resource for learners who prefer to use R for their machine learning projects.
Provides a gentle introduction to data science concepts and techniques. It uses Python and covers topics such as data cleaning, feature engineering, and model evaluation, offering a valuable supplement to the course's machine learning content.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ibe1h4/introduction
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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
Español
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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
- Introduction to Trading, Machine Learning & GCP (viewing)
Good to know
Examines the fundamentals of trading with machine learning on Google Cloud, which is standard in industry
Introduces machine learning, which is helpful for generalized models
Explores supervised learning with BigQuery ML, which helps learners build regression models
Instructs the theory of ARIMA modeling and how it is applied to time series data, which is helpful for solving specific types of trading problems
Guides the way to leverage GCP for implementing trading techniques, which is relevant to industry
Prerequisites include advanced competency in Python programming and familiarity with pertinent libraries for machine learning
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