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.
Artificial Intelligence for Trading
Nanodegree Program
Complete real-world projects designed by industry experts, covering topics from asset management to trading signal generation. Master AI algorithms for trading, and build your career-ready portfolio.
05Days07Hrs48Min25Sec
Estimated time
6 Months
At 10 hrs/week
Enroll by
August 24, 2022
Get access to classroom immediately on enrollment
Prerequisites
Python & Mathematics
Built in partnership with
What you will learn
Quantitative Trading
Estimated 6 months to completeLearn 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.
Prerequisite knowledge
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.
Advanced Quantitative Trading
Learn the quant workflow for signal generation, and apply advanced quantitative methods commonly used in trading.
Stocks, Indices, and ETFs
Learn about portfolio optimization, and financial securities formed by stocks, including market indices, vanilla ETFs, and Smart Beta ETFs.
Factor Investing and Alpha Research
Learn about alpha and risk factors, and construct a portfolio with advanced optimization techniques.
Sentiment Analysis with Natural Language Processing
Learn the fundamentals of text processing, and analyze corporate filings to generate sentiment-based trading signals.
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.
Combining Multiple Signals
Learn advanced techniques to select and combine the factors you’ve generated from both traditional and alternative data.
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.
All our programs include:
Technical mentor support
Our knowledgeable mentors guide your learning and are focused on answering your questions, motivating you, and keeping you on track.
Career services
You’ll have access to Github portfolio review and LinkedIn profile optimization to help you advance your career and land a high-paying role.
Flexible learning program
Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.
Program offerings
Class content
- Content co-created with WorldQuant
- Real-world projects
- Project reviews
- Project feedback from experienced reviewers
Student services
- Technical mentor support
- Student community
Career services
- Github review
- Linkedin profile optimization
Succeed with personalized services.
We provide services customized for your needs at every step of your learning journey to ensure your success.
Get timely feedback on your projects.
- Personalized feedback
- Unlimited submissions and feedback loops
- Practical tips and industry best practices
- Additional suggested resources to improve
1,400+ project reviewers
2.7M projects reviewed
88/100 reviewer rating
1.1 hours avg project review turnaround time
Mentors available to answer your questions.
- Support for all your technical questions
- Questions answered quickly by our team of technical mentors
1,400+ technical mentors
0.85 hours median response time
Learn with the best.
Learn with the best.
Cindy Lin
Curriculum LeadCindy 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
InstructorArpan 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
InstructorElizabeth 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
InstructorEddy 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
InstructorBrok 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
InstructorParnian 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
Content DeveloperJuan 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
InstructorLuis 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
Curriculum LeadCezanne 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
InstructorMat is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.
AI for Trading
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Best Value
Learn
Learn the basics of quantitative analysis, and work on real-world projects from trading strategies to portfolio optimization.
Average Time
On average, successful students take 6 months to complete this program.
Benefits include
- Real-world projects from industry experts
- Technical mentor support
- Career services
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