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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.

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  • Estimated time
    6 Months

    At 10 hrs/week

  • Enroll by
    May 31, 2023

    Get access to classroom immediately on enrollment

  • Skills acquired
    Financial Trading, Portfolio Optimization, Recurrent Neural Networks
Built in partnership with
  • WorldQuant

What you will learn

  1. Quantitative Trading

    Estimated 6 months to complete

    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.

    Prerequisite knowledge

    1. 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.

    2. Advanced Quantitative Trading

      Learn the quant workflow for signal generation, and apply advanced quantitative methods commonly used in trading.

    3. Stocks, Indices, and ETFs

      Learn about portfolio optimization, and financial securities formed by stocks, including market indices, vanilla ETFs, and Smart Beta ETFs.

    4. Factor Investing and Alpha Research

      Learn about alpha and risk factors, and construct a portfolio with advanced optimization techniques.

    5. Sentiment Analysis with Natural Language Processing

      Learn the fundamentals of text processing, and analyze corporate filings to generate sentiment-based trading signals.

    6. 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.

    7. Combining Multiple Signals

      Learn advanced techniques to select and combine the factors you’ve generated from both traditional and alternative data.

    8. 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

  • 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.

  • Real-time support

    On demand help. Receive instant help with your learning directly in the classroom. Stay on track and get unstuck.

  • 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

    • Student community
    • Real-time support
  • 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

Learn with the best.

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.

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AI for Trading

Get started today

    • 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
      • Real-time classroom support
      • Career services

    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 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 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

    Enroll Now