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Mat Leonard,
Parnian Barekatain,
Eddy Shyu,
Brok Bucholtz,
Elizabeth Otto Hamel,
Cindy Lin,
Cezanne Camacho,
Arpan Chakraborty,
Luis Serrano,
and
Juan Delgado
Learn how to analyze alternative data and use machine learning to generate trading signals. Run a backtest to evaluate and combine top performing signals.
Register for this course and see more details by visiting:
OpenCourser.com/course/1r02ac/ai
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What's inside
Syllabus
Welcome to Term 2! Say hello to your instructors and get an overview of the program.
Learn how to build a Natural Language Processing pipeline.
Read more
Syllabus
Welcome to Term 2! Say hello to your instructors and get an overview of the program.
Learn how to build a Natural Language Processing pipeline.
Read more
Learn to prepare text obtained from different sources for further processing, by cleaning, normalizing and splitting it into individual words or tokens.
Transform text using methods like Bag-of-Words, TF-IDF, Word2Vec and GloVE to extract features that you can use in machine learning models.
Learn how to scrape data from financial documents using Regular Expressions and BeautifulSoup
Learn how to apply to NLP to financial statements
NLP Analysis on 10-k financial statements to generate an alpha factor.
In this lesson, Luis will teach you the foundations of deep learning and neural networks. You'll also implement gradient descent and backpropagation in python, right here in the classroom!
Now that you know what neural networks are, in this lesson you will learn several techniques to improve their training.
Learn how to use PyTorch for building deep learning models
Learn how to use recurrent neural networks to learn from sequential data such as text. Build a network that can generate realistic text one letter at a time.
In this lesson, you'll learn about embeddings in neural networks by implementing the Word2Vec model.
Implement a sentiment prediction RNN for predicting whether a movie review is positive or negative!
Build a deep learning model to classify the sentiment of messages.
Learn about machine learning from a bird's-eye-view.
Decision trees are a structure for decision-making where each decision leads to a set of consequences or additional decisions.
Learn about metrics to evaluate models and about how to avoid over- and underfitting.
Learn about random forest models and how to use them to combine alpha factors.
Learn to engineer features such as market dispersion, market volatility, sector and date parts. Also learn to engineer targets (labels) that are robust to market changes over time.
Learn about an issue with non-independent labels that comes up during alpha combination with machine learning models.
Feature importance helps us decide how relevant each feature is to a machine learning model's predictions. Learn about two methods for calculating feature importance.
Build a random forest to generate better alpha.
Backtesting helps you determine whether or not your strategies can be generalizable to future unseen data.
Learn about how to make the portfolio optimization in a backtest more realistic, and also more computationally efficient.
Use performance attribution to determine how each factor contributed to the portfolio's results.
Build a backtester using Barra data.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores a range of machine learning techniques, including NLP, neural networks, deep learning, decision trees, and random forests
Taught by industry experts with a proven track record in data analysis and machine learning
Develops practical skills in building machine learning models for financial trading
Provides hands-on experience through a backtesting project
Save this course
Save AI Algorithms in Trading to your list so you can find it easily later:
Save
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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 AI Algorithms in Trading with these
activities:
NLP Analysis on 10-k financial statements
Show steps
Practice analyzing financial statements using NLP, reinforcing your understanding of text processing and feature extraction.
Show steps
-
Gather a dataset of 10-k financial statements
-
Preprocess the text using NLP techniques
-
Extract features using methods like Bag-of-Words or TF-IDF
-
Train a machine learning model to identify patterns
Implement gradient descent and backpropagation in Python
Show steps
Follow a tutorial to implement gradient descent and backpropagation, solidifying your understanding of neural network training.
Show steps
-
Find a tutorial on gradient descent and backpropagation
-
Follow the tutorial and implement the algorithms in Python
-
Test your implementation on a simple dataset
Discuss machine learning metrics and model evaluation techniques
Show steps
Engage in peer discussions to reinforce your understanding of model evaluation and improve your ability to assess model performance.
Show steps
-
Join or create a study group or discussion forum
-
Prepare questions and topics for discussion
-
Actively participate in discussions and share your insights
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend a workshop on advanced NLP techniques
Show steps
Attend a workshop to expand your NLP skills and learn about advanced techniques for text processing and analysis.
Show steps
-
Identify and register for a relevant workshop
-
Attend the workshop and actively participate
-
Apply the techniques learned to your projects
Engineer features for machine learning models
Show steps
Start a project to engineer features for machine learning models, putting your knowledge of feature engineering into practice.
Show steps
-
Identify a dataset and problem statement
-
Research and select feature engineering techniques
-
Implement feature engineering in Python
-
Evaluate the impact of feature engineering on model performance
Build a backtester using Barra data
Show steps
Build a backtester to evaluate and combine your trading signals, putting your knowledge of backtesting into practice.
Show steps
-
Gather the necessary data and tools
-
Set up the backtesting environment
-
Implement the backtesting logic
-
Run the backtest and analyze the results
Build a deep learning model to classify the sentiment of messages
Show steps
Create a deep learning model to classify sentiment, applying your knowledge of model building and evaluation.
Show steps
-
Gather a dataset of messages with sentiment labels
-
Preprocess the text and extract features
-
Design and build a deep learning model for sentiment classification
-
Train and evaluate the model
Contribute to an open-source machine learning library
Show steps
Contribute to an open-source project to gain practical experience in machine learning and software development.
Show steps
-
Identify an open-source machine learning library
-
Read the documentation and familiarize yourself with the codebase
-
Propose and implement a new feature or fix a bug
-
Submit a pull request and collaborate with the maintainers
NLP Analysis on 10-k financial statements
Show steps
Practice analyzing financial statements using NLP, reinforcing your understanding of text processing and feature extraction.
Show steps
Show steps
Show steps
- Gather a dataset of 10-k financial statements
- Preprocess the text using NLP techniques
- Extract features using methods like Bag-of-Words or TF-IDF
- Train a machine learning model to identify patterns
Implement gradient descent and backpropagation in Python
Show steps
Follow a tutorial to implement gradient descent and backpropagation, solidifying your understanding of neural network training.
Show steps
Show steps
Show steps
- Find a tutorial on gradient descent and backpropagation
- Follow the tutorial and implement the algorithms in Python
- Test your implementation on a simple dataset
Discuss machine learning metrics and model evaluation techniques
Show steps
Engage in peer discussions to reinforce your understanding of model evaluation and improve your ability to assess model performance.
Show steps
Show steps
Show steps
- Join or create a study group or discussion forum
- Prepare questions and topics for discussion
- Actively participate in discussions and share your insights
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend a workshop on advanced NLP techniques
Show steps
Attend a workshop to expand your NLP skills and learn about advanced techniques for text processing and analysis.
Show steps
Show steps
Show steps
- Identify and register for a relevant workshop
- Attend the workshop and actively participate
- Apply the techniques learned to your projects
Engineer features for machine learning models
Show steps
Start a project to engineer features for machine learning models, putting your knowledge of feature engineering into practice.
Show steps
Show steps
Show steps
- Identify a dataset and problem statement
- Research and select feature engineering techniques
- Implement feature engineering in Python
- Evaluate the impact of feature engineering on model performance
Build a backtester using Barra data
Show steps
Build a backtester to evaluate and combine your trading signals, putting your knowledge of backtesting into practice.
Show steps
Show steps
Show steps
- Gather the necessary data and tools
- Set up the backtesting environment
- Implement the backtesting logic
- Run the backtest and analyze the results
Build a deep learning model to classify the sentiment of messages
Show steps
Create a deep learning model to classify sentiment, applying your knowledge of model building and evaluation.
Show steps
Show steps
Show steps
- Gather a dataset of messages with sentiment labels
- Preprocess the text and extract features
- Design and build a deep learning model for sentiment classification
- Train and evaluate the model
Contribute to an open-source machine learning library
Show steps
Contribute to an open-source project to gain practical experience in machine learning and software development.
Show steps
Show steps
Show steps
- Identify an open-source machine learning library
- Read the documentation and familiarize yourself with the codebase
- Propose and implement a new feature or fix a bug
- Submit a pull request and collaborate with the maintainers
Career center
Learners who complete AI Algorithms in Trading will develop knowledge and skills
that may be useful to these careers:
Financial Analyst
Financial Analyst
Financial Analysts are experts in evaluating and analyzing financial data. Those interested in this field should take the AI Algorithms in Trading course to learn how to analyze alternative data and use machine learning to generate trading signals. This course can help build a foundation in the use of AI and machine learning in the financial industry.
Data Scientist
Data Scientist
Data Scientists use their knowledge of data analysis, machine learning, and statistics to extract meaningful insights from data. Those interested in this field should take the AI Algorithms in Trading course to learn how to use machine learning to generate trading signals and evaluate and combine top-performing signals.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts (Quants) use mathematical and statistical models to analyze and predict financial markets. Those interested in this field should take the AI Algorithms in Trading course to learn how to use machine learning to generate trading signals and evaluate and combine top-performing signals.
Trader
Trader
Traders buy and sell stocks, bonds, and other financial instruments on behalf of clients or for their own account. Those interested in this field should take the AI Algorithms in Trading course to learn how to use machine learning to generate trading signals and evaluate and combine top-performing signals.
Investment Manager
Investment Manager
Investment Managers make investment decisions for clients or for their own account. Those interested in this field should take the AI Algorithms in Trading course to learn how to use machine learning to generate trading signals and evaluate and combine top-performing signals.
Risk Manager
Risk Manager
Risk Managers identify and assess financial risks and develop strategies to mitigate those risks. Those interested in this field should take the AI Algorithms in Trading course to learn how to use machine learning to generate trading signals and evaluate and combine top-performing signals.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. Those interested in this field should take the AI Algorithms in Trading course to learn how to use machine learning to generate trading signals and evaluate and combine top-performing signals.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure. Those interested in this field should take the AI Algorithms in Trading course to learn how to use machine learning to generate trading signals and evaluate and combine top-performing signals.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. Those interested in this field should take the AI Algorithms in Trading course to learn how to use machine learning to generate trading signals and evaluate and combine top-performing signals.
Business Analyst
Business Analyst
Business Analysts use data analysis and modeling to identify and solve business problems. Those interested in this field should take the AI Algorithms in Trading course to learn how to use machine learning to generate trading signals to identify new business opportunities.
Financial Advisor
Financial Advisor
Financial Advisors provide financial advice to individuals and families. Those interested in this field should take the AI Algorithms in Trading course to learn how to use machine learning to generate trading signals to help clients make informed investment decisions.
Economist
Economist
Economists study and analyze economic data to make predictions about the economy. Those interested in this field should take the AI Algorithms in Trading course to learn how to use machine learning to generate trading signals based on economic data.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to draw conclusions. Those interested in this field should take the AI Algorithms in Trading course to learn how to use machine learning to generate trading signals based on statistical analysis.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use quantitative methods to solve business problems. Those interested in this field should take the AI Algorithms in Trading course to learn how to use machine learning to generate trading signals to optimize business operations.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. Those interested in this field may find the AI Algorithms in Trading course helpful for learning how to use machine learning to generate trading signals to manage risk.
For more career information including salaries, visit:
OpenCourser.com/course/1r02ac/ai
Reading list
We've selected nine 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
AI Algorithms in Trading.
Provides a comprehensive overview of backtesting and performance evaluation techniques for algorithmic trading. It covers a wide range of topics, including data quality assessment, model validation, and risk management.
Provides a comprehensive overview of machine learning techniques for finance. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Provides a detailed guide to feature engineering for machine learning. It covers a wide range of topics, including data cleaning, feature selection, and feature transformation.
Provides a comprehensive overview of quantitative value investing techniques. It covers a wide range of topics, including factor models, risk management, and performance evaluation.
Great introduction to the field of data science. It covers the basics of data mining and data-analytic thinking, and it provides many real-world examples of how data science is being used in business.
Provides a comprehensive overview of deep learning techniques for natural language processing. It covers a wide range of topics, including word embeddings, convolutional neural networks, recurrent neural networks, and transformers.
Provides a practical introduction to natural language processing with Python. It covers a wide range of topics, including tokenization, stemming, lemmatization, parsing, and machine learning.
Provides a practical introduction to artificial intelligence and machine learning for finance. It covers a wide range of topics, including natural language processing, computer vision, and deep learning.
Provides a comprehensive overview of MATLAB for beginners. It covers a wide range of topics, including data types, operators, and functions.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1r02ac/ai
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Time
131040 hours
Level
Advanced
Via
Udacity
Instructors
Mat Leonard
Parnian Barekatain
Eddy Shyu
Brok Bucholtz
Elizabeth Otto Hamel
Cindy Lin
Cezanne Camacho
Arpan Chakraborty
Luis Serrano
Juan Delgado
Language
English
Good to know
Explores a range of machine learning techniques, including NLP, neural networks, deep learning, decision trees, and random forests
Taught by industry experts with a proven track record in data analysis and machine learning
Develops practical skills in building machine learning models for financial trading
Provides hands-on experience through a backtesting project
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