AI for Trading

Welcome to the exciting world of Quantitative Trading! Say hello to your instructors and get an overview of the program.

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

What to do if you have questions about your account or general questions about the program.

Learn about stocks and common terminology used when analyzing stocks.

Learn about how modern stock markets function, how trades are executed and prices are set. Study market behavior, and analyze price and volume data to identify potential trading signals.

Learn how to adjust market data for corporate actions, include fundamental information in your analysis and compute technical indicators.

Learn how to calculate stock returns, and log returns in particular. Learn why log returns are used to analyze financial data.

Learn about alpha signals, and how they can be applied to a long/short trading strategy. Learn about momentum, a common alpha signal used in trading strategies.

Learn to implement a trading strategy on your own and test to see if it has the potential to be profitable.

Learn about the overall quant workflow, including alpha signal generation, alpha combination, portfolio optimization, and trading.

Learn the importance of outliers and how to detect them. Learn about methods designed to handle outliers.

Learn about regression, and related statistical tools that pre-process data before regression analysis. See how regression relates to trading and other more advanced methods.

Learn about advanced methods for time series analysis, including ARMA, ARIMA, Kalman Filters, Particle Filters, and recurrent neural networks.

Learn about stock volatility, and how the GARCH model analysis volatility. See how volatility is used in equity trading.

Learn about pairs trading, and study the tools used in identifying stock pairs and making trading decisions.

Implement the breakout strategy, find and remove outliers, and test to see if it can be a profitable strategy.

Gain an overview of stocks, indices and funds. Also learn how to construct an index.

Learn about Exchanged Traded Funds (ETFs) and how they are used by investors and fund managers.

Learn the fundamentals of portfolio theory, which are key to designing portfolios for mutual funds, hedge funds and ETFs.

Learn how to optimize portfolios to meet certain criteria and constraints. Get hands on experience in optimizing a portfolio with the cvxpy Python library.

Build a smart beta portfolio against an index and optimize a portfolio using quadratic programming.

In the next 7 lessons and project, learn about factor investing and alpha research. These lessons and the project were designed by Jonathan Larkin, equities trader and quant investor.

Learn the theory of factor models, distinguish between alpha and risk factors, and get an overview of types of factors.

Learn how to model portfolio risk using factors.

Learn about two important types of risk models: time series and cross-sectional risk models.

Learn about Principle Component Analysis and how it's used to build risk factor models.

Learn about alpha generation and evaluation from a practitioner's perspective.

Learn about alpha research from a practitioner's perspective.

Learn about portfolio optimization using alpha factors and risk factor models.

Research and implement alpha factors, build a risk factor model. Use alpha factors and risk factors to optimize a portfolio.

Welcome to Term 2! Say hello to your instructors and get an overview of the program.

Learn how to build a Natural Language Processing pipeline.

Learn to prepare text obtained from different sources for further processing, by cleaning, normalizing and splitting it into individual words or tokens.

Transform text using methods like Bag-of-Words, TF-IDF, Word2Vec and GloVE to extract features that you can use in machine learning models.

Learn how to scrape data from financial documents using Regular Expressions and BeautifulSoup

Learn how to apply to NLP to financial statements

NLP Analysis on 10-k financial statements to generate an alpha factor.

In this lesson, Luis will teach you the foundations of deep learning and neural networks. You'll also implement gradient descent and backpropagation in python, right here in the classroom!

Now that you know what neural networks are, in this lesson you will learn several techniques to improve their training.

Learn how to use PyTorch for building deep learning models

Learn how to use recurrent neural networks to learn from sequential data such as text. Build a network that can generate realistic text one letter at a time.

In this lesson, you'll learn about embeddings in neural networks by implementing the Word2Vec model.

Implement a sentiment prediction RNN for predicting whether a movie review is positive or negative!

Build a deep learning model to classify the sentiment of messages.

Learn about machine learning from a bird's-eye-view.

Decision trees are a structure for decision-making where each decision leads to a set of consequences or additional decisions.

Learn about metrics to evaluate models and about how to avoid over- and underfitting.

Learn about random forest models and how to use them to combine alpha factors.

Learn to engineer features such as market dispersion, market volatility, sector and date parts. Also learn to engineer targets (labels) that are robust to market changes over time.

Learn about an issue with non-independent labels that comes up during alpha combination with machine learning models.

Feature importance helps us decide how relevant each feature is to a machine learning model's predictions. Learn about two methods for calculating feature importance.

Build a random forest to generate better alpha.

Backtesting helps you determine whether or not your strategies can be generalizable to future unseen data.

Learn about how to make the portfolio optimization in a backtest more realistic, and also more computationally efficient.

Use performance attribution to determine how each factor contributed to the portfolio's results.

Build a backtester using Barra data.

Welcome to Introduction to Python! Here's an overview of the course.

Familiarize yourself with the building blocks of Python! Learn about data types and operators, compound data structures, type conversion, built-in functions, and style guidelines.

Build logic into your code with control flow tools! Learn about conditional statements, repeating code with loops and useful built-in functions, and list comprehensions.

Learn how to use functions to improve and reuse your code! Learn about functions, variable scope, documentation, lambda expressions, iterators, and generators.

Setup your own programming environment to write and run Python scripts locally! Learn good scripting practices, interact with different inputs, and discover awesome tools.

Take a sneak peek into the beautiful world of Linear Algebra and learn why it is such an important mathematical tool.

Learn about vectors, the basic building block of Linear Algebra.

Learn how to scale and add vectors and how to visualize the process.

What is a linear transformation and how is it directly related to matrices? Learn how to apply the math and visualize the concept.