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Nanodegree Program

Artificial Intelligence for Trading

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.

  • Estimated Time
    6 Months

    At 10 hrs/week

  • Enroll by
    December 10, 2019

    Get access to classroom immediately on enrollment

  • Prerequisites
    Python & Mathematics

    See prerequisites in detail

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What You Will Learn

Download Syllabus
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.

Learn quantitative analysis basics, and work on real-world projects from trading strategies to portfolio 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
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Data-driven investments have doubled in 5 years, to $1 trillion in 2018.

All Our Nanodegree Programs Include

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.

1-on-1 technical mentor

Get a knowledgeable mentor who guides your learning and is focused on answering your questions, motivating you and keeping you on track.

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

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.
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
Individual 1-on-1 Mentorship
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
Succeed with Personalized Services
We provide services customized for your needs at every step of your learning journey to ensure your success!
Project Reviewers
1-on-1 Mentors
Career Coaching
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

Instructor

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

The Udacity Impact

Numbers don't lie. See what difference it makes in career searches.*

84%
Better Jobs

Career-seeking and job-ready graduates found a new, better job within six months of graduation.

$24,000
Salary Increase

Average salary increase for graduates who found a new, better job within six months of graduation.

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 Nanodegree program 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.

Artificial Intelligence for Trading