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- Cancel anytime.
Estimated Time
6 Months
At 10 hrs/week
Enroll by
September 22, 2021
Get access to classroom immediately on enrollment
Prerequisites
Python & Mathematics
Built in partnership with
What You Will Learn
Syllabus
Quantitative Trading
Related NanodegreesBasic 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.
Data-driven investments have doubled in 5 years, to $1 trillion in 2018.
Program OfferingsFull list of offerings included:
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
Resume support
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
Reviews By the numbers
1,400+ project reviewers
2.7M projects reviewed
88/100 reviewer rating
1.1 hours 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
Mentors available to answer your questions
Mentors by the numbers
1,400+ technical mentors
0.85 hours median response time
Mentorship Services
- Support for all your technical questions
- Questions answered quickly by our team of technical mentors
Learn with the best
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
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
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
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
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
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
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
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
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
Instructor
Mat 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
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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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Program Details
PROGRAM OVERVIEW - WHY SHOULD I TAKE THIS PROGRAM?
Why should I enroll?
Demand for quantitative talent is growing at incredible rates. Data-driven traders are now responsible for more than 30% of all US stock trades by investors (or about $1 trillion USD worth of investments, up from 14% in 2013). This scenario represents incredible opportunity for individuals eager to apply cutting-edge technologies to trading and finance.
Whether you want to pursue a new job in finance, launch yourself on the path to a quant trading career, or master the latest AI applications in trading and quantitative finance, this program will give you the opportunity to build an impressive portfolio of real-world projects. You will build financial models on real data, and work on your own trading strategies using natural language processing, recurrent neural networks, and random forests. You’ll also enjoy direct access to leading experts in the field, and get personalized project and career support.
To create the curriculum for this program, we collaborated with WorldQuant, a global quantitative asset management firm, as well as top industry professionals with prior experience at JPMorgan, Morgan Stanley, Millennium Management, and more. If your goal is to learn from the leaders in the field, and to master the most valuable and in-demand skills, this program is an ideal choice for you.What jobs will this program prepare me for?
Graduates of this program will have the quantitative skills needed to be extremely valuable across many functions, and in many roles at hedge funds, investment banks, and FinTech startups.
Specific roles include:- Quantitative analyst
- Quantitative researcher
- Investment analyst
- Data intelligence analyst
- Risk analyst
- Desk quant
- Desk strategist
- Financial engineer
- Financial data scientist
How do I know if this program is right for me?
If you’re a programmer, data analyst or someone with a strong quantitative background, this program offers you the ideal path to pursue a quant trading career and prepares you to seek out data science jobs across the financial ecosystem.
ENROLLMENT AND ADMISSION
Do I need to apply? What are the admission criteria?
No. This Nanodegree program accepts all applicants regardless of experience and specific background.What are the prerequisites for enrollment?
The Artificial Intelligence for Trading Nanodegree program is designed for students with intermediate experience programming with Python and familiarity with statistics, linear algebra and calculus. In order to successfully complete this program, you should meet the following prerequisites:
Python programming- Basic data structures
- Basic Numpy
Statistics- Mean, median, mode
- Variance, standard deviation
- Random variables, independence
- Distributions, normal distribution
- T-test, p-value, statistical significance
Calculus and linear algebra- Integrals and derivatives
- Linear combination, independence
- Matrix operations
- Eigenvectors, eigenvalues
If I do not meet the requirements to enroll, what should I do?
We have a number of short free courses that can help you prepare, including:
TUITION AND TERM OF PROGRAM
How is this Nanodegree program structured?
The Artificial Intelligence for Trading Nanodegree program is comprised of content and curriculum to support eight (8) projects. We estimate that students can complete the program in six (6) months working 10 hours per week.
Each project will be reviewed by the Udacity reviewer network. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.How long is this Nanodegree program?
Access to this Nanodegree program runs for the length of time specified in the payment card above. If you do not graduate within that time period, you will continue learning with month to month payments. See the Terms of Use and FAQs for other policies regarding the terms of access to our Nanodegree programs.Can I switch my start date? Can I get a refund?
Please see the Udacity Program Terms of Use and FAQs for policies on enrollment in our programs.
SOFTWARE AND HARDWARE - WHAT DO I NEED FOR THIS PROGRAM?
What software and versions will I need in this program?
To successfully complete this Nanodegree program, you’ll need to be able to download and run Python 3.7.