Udacity – Artificial Intelligence for Trading Nanodegree Programs

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Udacity – Artificial Intelligence for Trading Nanodegree Programs

What You Will Learn
Syllabus
Quantitative Trading

Learn the basics of quantitative analysis, including data processing, trading signal generation, and portfolio management. Use Python to work with historical stock data, develop trading strategies, and construct a multi-factor model with optimization.

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Estimated 6 months to complete

Basic Quantitative Trading

Learn about market mechanics and how to generate signals with stock data. Work on developing a momentum-trading strategy in your first project.
Trading with momentum
Advanced Quantitative Trading

Learn the quant workflow for signal generation, and apply advanced quantitative methods commonly used in trading.
Breakout Strategy
Stocks, Indices, and ETFs

Learn about portfolio optimization, and financial securities formed by stocks, including market indices, vanilla ETFs, and Smart Beta ETFs.
Smart Beta and Portfolio Optimization
Factor Investing and Alpha Research

Learn about alpha and risk factors, and construct a portfolio with advanced optimization techniques.
Alpha Research and Factor Modeling
Sentiment Analysis with Natural Language Processing

Learn the fundamentals of text processing, and analyze corporate filings to generate sentiment-based trading signals.
Sentiment Analysis using NLP
Advanced Natural Language Processing with Deep Learning

Learn to apply deep learning in quantitative analysis and use recurrent neural networks and long short-term memory to generate trading signals.
Deep Neural Network with News Data
Combining Multiple Signals

Learn advanced techniques to select and combine the factors you’ve generated from both traditional and alternative data.
Combine Signals for Enhanced Alpha
Simulating Trades with Historical Data

Learn to refine trading signals by running rigorous back tests. Track your P&L while your algorithm buys and sells.
Backtesting

Need to prepare?

New to Python programming? Check out our free Intro to Data Analysis course.

Need to refresh your statistical and algebra knowledge? Check out our free Intro to Statistics and Linear Algebra courses.
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All Our Programs Include

Real-world projects from industry experts

Real-world projects from industry experts
With real world projects and immersive content built in partnership with top tier companies, you’ll master the tech skills companies want.
Technical mentor support

Technical mentor support
Our knowledgeable mentors guide your learning and are focused on answering your questions, motivating you and keeping you on track.
Personal career coach and career services

Personal career coach and career services
You’ll have access to career coaching sessions, interview prep advice, and resume and online professional profile reviews to help you grow in your career.
Flexible learning program

Flexible learning program
Get a custom learning plan tailored to fit your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.
Program Offerings
Enrollment includes:
Class content
Content co-created with WorldQuant
Real-world projects
Project reviews
Project feedback from experienced reviewers
Student Services
Technical mentor support
New
Student community
Improved
Career services
Personal career coaching
New
Interview preparations
Resume services
Github review
LinkedIn profile review
Succeed with Personalized Services
We provide services customized for your needs at every step of your learning journey to ensure your success!
Experienced Project Reviewers
Technical Mentor Support
Personal Career Coach
Get personalized feedback on your projects
Reviews By the numbers
2000+ project reviewers
1.8M projects reviewed
4.85/5 reviewer ratings
3 hour avg project review turnaround time
Reviewer Services

Personalized feedback
Unlimited submissions and feedback loops
Practical tips and industry best practices
Additional suggested resources to improve

Learn with the best
Cindy Lin
Cindy Lin

Curriculum Lead

Cindy is a quantitative analyst with experience working for financial institutions such as Bank of America Merrill Lynch, Morgan Stanley, and Ping An Securities. She has an MS in Computational Finance from Carnegie Mellon University.
Arpan Chakraborty
Arpan Chakraborty

Instructor

Arpan is a computer scientist with a PhD from North Carolina State University. He teaches at Georgia Tech (within the Masters in Computer Science program), and is a coauthor of the book Practical Graph Mining with R.
Elizabeth Otto Hamel
Elizabeth Otto Hamel

Instructor

Elizabeth received her PhD in Applied Physics from Stanford University, where she used optical and analytical techniques to study activity patterns of large ensembles of neurons. She formerly taught data science at The Data Incubator.
Eddy Shyu
Eddy Shyu

Instructor

Eddy has worked at BlackRock, Thomson Reuters, and Morgan Stanley, and has an MS in Financial Engineering from HEC Lausanne. Eddy taught data analytics at UC Berkeley and contributed to Udacity’s Self-Driving Car program.
Brok Bucholtz
Brok Bucholtz

Instructor

Brok has a background of over five years of software engineering experience from companies like Optimal Blue. Brok has built Udacity projects for the Self Driving Car, Deep Learning, and AI Nanodegree programs.
Parnian Barekatain
Parnian Barekatain

Instructor

Parnian is a self-taught AI programmer and researcher. Previously, she interned at OpenAI on multi-agent Reinforcement Learning and organized the first OpenAI hackathon. She also runs a ShannonLabs fellowship to support the next generation of independent researchers.
Juan Delgado
Juan Delgado

Content Developer

Juan is a computational physicist with a Masters in Astronomy. He is finishing his PhD in Biophysics. He previously worked at NASA developing space instruments and writing software to analyze large amounts of scientific data using machine learning techniques.
Luis Serrano
Luis Serrano

Instructor

Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in mathematics from the University of Michigan, and a Postdoctoral Fellowship at the University of Quebec at Montreal.
Cezanne Camacho
Cezanne Camacho

Curriculum Lead

Cezanne is a machine learning educator with a Masters in Electrical Engineering from Stanford University. As a former researcher in genomics and biomedical imaging, she’s applied machine learning to medical diagnostic applications.
Mat Leonard
Mat Leonard

Get Udacity – Artificial Intelligence for Trading Nanodegree Programs download

Instructor

Mat is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.
Top Student Reviews
4.6

(350)
Eduardo P.

I loved the program. The first 3 projects were very basic, but everything after project 4 was great. You get introduced to alpha research, portfolio optimization and backtesting. In some of the projects you use Zipline, Quantopian’s open source library. While of course, it’s not expected for them to provide trading strategies to you, the applications of AI to trading seem relevant. You use neural networks, NLP, and random forests, among other models, in ways that are appliable to real trading research. Most of the instructors from Udacity do a very good job explaining the courses concepts, both theory and programming. The first 3 projects touch some subjects very briefly and without much depth, which was a bit disappointing, but understandable because of the breath of knowledge required for Quantitative Trading. The pace after project 4 picks up considerably though, and you get a challenging sequence with many subjects and approaches that were new for me, as well as suggestions to deepen your knowledge. The instructors they brought from the industry were excellent too.
Hsin-Wen C.

I received a lot of great advice from Udacity reviewers which I haven’t arranged a time to continue to organize project portfolio video demo with the reviews. And I should write each project a readme to demonstrate what I know and speak for each project, I work through. It matched my need in the part of the healing of my heart and soul like a puppy, sleep, meditation and Godzilla and the machine learning section give me a lot of lift. It’s about Thanksgiving, I want to tell Udacity thank you for guiding me to enroll this Nanodegree program :D! It is super worth this journey. I have a great life during this Nanodegree program.
Anunay b.

Its a great program, I already knew most of the things from the AI Algorithms section but the Quantitative Trading section helped me understand how stock markets operate and how to make money (LOL). I initially had almost no understanding about stocks and now i am confident enough to make a proper stock portfolio and calculate the risks and expected volatility to help myself invest money better. Thanks Instructors !!
Frank Salvador Y.

So far, the program is going very well. I would have preferred a greater depth in the application of Machine Learning from the beginning (due to my intermediate level in that field). However, I understand that not all students have the same level and that they need some training, especially in financial matters. Except for that detail, I like how the Nanodegree is structured.
SANGEET MOY D.

Well, this has been the best thing that happened to me during the COVID-19 outbreak as I’m completely isolated and work from home with no human interaction. Being a Quant at a research lab myself, the Part-I of this nanodegree has been a great refresher for me making it easy to glide through the projects. Let’s see what Part-II has in store for me.
Hailu T.

The program is of to a great start. I have already learned about techniques such as resampling to have a stock market data view of specified frequency, identifying stocks for long and short trading, use statistical analysis (t-test) to determine if a portfolio results in a positive return. I can’t wait to learn more in the up coming topics.

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