Artificial Intelligence for Trading Nanodegree

Data-driven investments have doubled in 5 years, to $1 trillion in 2018.

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.

• Detail Syllabus - download

Data-driven investments have doubled in 5 years, to $1 trillion in 2018.

On average, successful students take 6 months to complete this program.

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.

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.

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

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