As detailed in The Wall Street Journal, “investors now have at their fingertips an expanding ocean of data about the global economy and financial data, such as changes in earnings estimates and accounts receivable.” Traders who can leverage data and technology, including artificial intelligence, are gaining significant ground accordingly:

“Quantitative hedge funds are now responsible for 27% of all U.S. stock trades by investors, up from 14% in 2013.”

It’s no surprise that talent wars are heating up in this field. One need look no further than this headline to understand that finance, and trading in particular, is entering a new era: Silicon Valley keeps losing top talent to quant hedge funds.

Udacity and WorldQuant partner to launch the Artificial Intelligence for Trading Nanodegree program

Our research found that there is massive interest in learning how to build financial models for trading, particularly among data analysts, software engineers, and programmers with Python skills. This is why we’re so excited to announce our new Artificial Intelligence for Trading Nanodegree program! We’re especially pleased to have partnered with WorldQuant, a global quantitative asset management firm, to build this program, as it brings together our groundbreaking learning platform with one of the true pioneers in the quantitative trading space.

When we interviewed Igor Tulchinsky, Founder, Chairman, and CEO of WorldQuant, he made clear his company’s commitment to nurturing new talent, and how this Nanodegree program will enable the next generation of talent to enter the field:

“WorldQuant is committed to bringing opportunity to talent globally. This Nanodegree program is like a laser, it focuses exactly on what you need today to succeed.”

WorldQuant to actively consider graduates for job opportunities

Our proven track record preparing students for AI-focused roles is one of the key reasons why WorldQuant is excited to collaborate with Udacity, and why they’re pledging to consider graduates for job opportunities at the firm. Here is Jeffrey Scott, Deputy General Manager of WorldQuant’s Virtual Research Center, highlighting some of the ways this program will prepare students for trading and quantitative finance roles:

“Students will have the opportunity to use real-world open-source and proprietary technologies that we offer. For example, our proprietary web-based simulation platform WebSim will give students the opportunity to build predictive trading signals, what we call alphas, from mathematical models and test them in a real-world environment using historical market data. In contrast to traditional educational programs, this Nanodegree program requires less time and financial investment and provides increased opportunities to explore practical applications that resemble real-world experiences.”

Real-world experience is the cornerstone of the program. Students will be able to leverage a vast amount of proprietary industry data, and work on unique projects custom-designed by WorldQuant and top industry professionals.

A new way to learn AI for Finance

We have worked closely with our partners at WorldQuant, and leveraged the best of our two organizations, to bring you a truly valuable learning experience. Founded in 2007, WorldQuant has over 650 employees spread across more than 25 offices in 15 countries. Our curriculum is deeply informed by contributions from the WorldQuant team, including Igor Tulchinsky.

Udacity’s School of AI welcomes finance experts with decades of prior experience in top hedge funds, investments banks, and FinTech startups

To date, more than 8,000 alumni have graduated from our School of Artificial Intelligence Nanodegree programs, and we’ve reached nearly 1 million students worldwide through our free AI courses. This program brings together the core instructors from Udacity’s School of AI and an outstanding group of industry experts, including:

● Jonathan Larkin: Jonathan has previously held leadership roles such as Global Head of Equities at Millennium Management and Co-Head of Americas Equity Derivatives Trading at JPMorgan.
● Kendall Lo: Kendall has been a quant trader and researcher at Citadel, Millennium Partners, and JPMorgan.
● Murat Ahmed: Murat is a quant researcher at Radix Trading and has worked for JPMorgan and Citadel.
● Harry Mendell: Harry has worked on algorithmic trading programs and risk management at Morgan Stanley and Apogee Fund Management and was previously CTO at Carlyle Blue Wave.
● Justin Sheetz: Justin has been an investment strategist in the Scientific Active Equity Group at BlackRock and a quant research analyst at MUFG/HighMark Capital.

Proprietary data and real-world projects

The program is made up of two terms. In the first term, you’ll learn the basics of quantitative analysis, covering data processing, trading signal generation, and portfolio management. You’ll use Python to work with historical stock data, develop trading strategies, and construct a multi-factor model with optimization. Featured projects include:

● Trading with Momentum: Learn to implement momentum-trading strategies and test if they have the potential to be profitable.
● Smart Beta and Portfolio Optimization: Create two portfolios utilizing smart beta methodology and optimization, and evaluate the performance of the portfolios by calculating tracking errors.
● Multi-factor Model: Research and generate multiple alpha factors and apply various techniques to evaluate performance, and learn to pick the best factors for your portfolio.

The second term is focused on AI Algorithms for Trading. In this term, you’ll work with alternative data and use machine learning to generate trading signals. You’ll run backtests to evaluate your trading signals and use advanced techniques to combine your top performers. Featured projects include:

● Sentiment Analysis using NLP: Apply natural language processing on corporate filings, such as 10Q and 10K statements, covering everything from cleaning data and text processing to feature extraction and modeling.
● Deep Neural Network with News Data: Build deep neural networks to process and interpret news data. Construct and train LSTM networks for sentiment classification, run backtests, and apply financial models to news data for signal generation.
● Combine Trading Signals for Enhanced Alpha: Create a prediction model for the S&P 500 and its constituent stocks by performing model selection for a large data set, which includes market, fundamental, and alternative data.

Term 1 is open for enrollment now at a cost of $999. To succeed in the program, you will need some experience programming with Python, and familiarity with statistics, linear algebra and calculus. No finance experience is required!
Applying machine learning and artificial intelligence techniques
Entire sections of this curriculum have been designed by industry professionals. One of these experts is Jeffrey Scott, who provided additional detail on how we designed this curriculum:

“We intentionally wanted to create a program that would be highly practical and applicable to quant trading, focusing on the application of AI and machine learning and analyzing big data. In today’s war for talent, we’ve created this program to help develop the next generation of quants.”

Jonathan Larkin, lead instructor for the program, echoes Jeffrey’s sentiments in highlighting the value of the program:

“I’m confident that this program will produce highly-skilled entrants to the field.”

Whether you want to launch yourself on the path to a quant trading career or master the latest AI applications in quantitative finance and start building your own financial models, this program offers a learning opportunity not found anywhere else in the world. Enroll today and get started mastering AI for Trading!