AI for Trading

Welcome to the Nanodegree Program

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

Getting Help

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

Get Help with Your Account

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

Stock Prices

Learn about stocks and common terminology used when analyzing stocks.

Market Mechanics

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.

Data Processing

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

Stock Returns

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

Momentum Trading

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.

Project 1: Trading with Momentum

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

Quant Workflow

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

Outliers and Filtering

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

Regression

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.

Time Series Modeling

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

Volatility

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

Pairs Trading and Mean Reversion

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

Project 2: Breakout Strategy

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

Stocks, Indices, Funds

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

ETFs

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

Portfolio Risk and Return

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

Portfolio Optimization

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

Project 3: Smart Beta and Portfolio Optimization

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

Factors

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.

Factor Models and Types of Factors

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

Risk Factor Models

Learn how to model portfolio risk using factors.

Time Series and Cross Sectional Risk Models

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

Risk Factor Models with PCA

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

Alpha Factors

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

Alpha Factor Research Methods

Learn about alpha research from a practitioner's perspective.

Advanced Portfolio Optimization

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

Project 4: Alpha Research and Factor Modeling

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

Welcome To Term II

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

Intro to Natural Language Processing

Learn how to build a Natural Language Processing pipeline.

Text Processing

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

Feature Extraction

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

Financial Statements

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

Basic NLP Analysis

Learn how to apply to NLP to financial statements

Project 5: NLP on Financial Statements

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

Introduction to Neural Networks

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!

Training Neural Networks

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

Deep Learning with PyTorch

Learn how to use PyTorch for building deep learning models

Recurrent Neural Networks

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.

Embeddings & Word2Vec

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

Sentiment Prediction RNN

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

Project 6: Sentiment Analysis with Neural Networks

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

Overview

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

Decision Trees

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

Model Testing and Evaluation

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

Random Forests

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

Feature Engineering

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.

Overlapping Labels

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

Feature Importance

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.

Project 7: Combining Signals for Enhanced Alpha

Build a random forest to generate better alpha.

Intro to Backtesting

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

Optimization with Transaction Costs

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

Attribution

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

Project 8: Backtesting

Build a backtester using Barra data.

Why Python Programming

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

Data Types and Operators

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.

Control Flow

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.

Functions

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

Scripting

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

Introduction

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

Vectors

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

Linear Combination

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

Linear Transformation and Matrices

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

Jupyter Notebooks

Learn how to use Jupyter Notebooks to create documents combining code, text, images, and more.

NumPy

Learn the basics of NumPy and how to use it to create and manipulate arrays.

Pandas

Learn the basics of Pandas Series and DataFrames and how to use them to load and process data.

Descriptive Statistics - Part I

In this lesson, you will learn about data types, measures of center, and the basics of statistical notation.

Descriptive Statistics - Part II

In this lesson, you will learn about measures of spread, shape, and outliers as associated with quantitative data. You will also get a first look at inferential statistics.

Admissions Case Study

Learn to ask the right questions, as you learn about Simpson's Paradox.

Probability

Gain the basics of probability using coins and die.

Binomial Distribution

Learn about one of the most popular distributions in probability - the Binomial Distribution.

Conditional Probability

Not all events are independent. Learn the probability rules for dependent events.

Bayes Rule

Learn one of the most popular rules in all of statistics - Bayes rule.

Python Probability Practice

Take what you have learned in the last lessons and put it to practice in Python.

Normal Distribution Theory

Learn the mathematics behind moving from a coin flip to a normal distribution.

Sampling distributions and the Central Limit Theorem

Learn all about the underpinning of confidence intervals and hypothesis testing - sampling distributions.

Confidence Intervals

Learn how to use sampling distributions and bootstrapping to create a confidence interval for any parameter of interest.

Hypothesis Testing

Learn the necessary skills to create and analyze the results in hypothesis testing.

Case Study: A/B tests

Work through a case study of how A/B testing works for an online education company called Audacity.

Linear Regression

Linear regression is a very effective algorithm to predict numerical data.

Naive Bayes

Naive Bayesian Algorithms are powerful tools for creating classifiers for incoming labeled data.

Clustering

Clustering is one of the most common methods of unsupervised learning. Here, we'll discuss the K-means clustering algorithm.

Decision Trees

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

Introduction to Kalman Filters

Learn the intuition behind the Kalman Filter, a vehicle tracking algorithm, and implement a one-dimensional tracker of your own.

Introduction to Neural Networks

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!

Intro to Computer Vision

Learn what computer vision is all about, its applications in the field of artificial and emotional intelligence.

Intro to NLP

Arpan will give you an overview of how to build a Natural Language Processing pipeline.

Take 30 Min to Improve your LinkedIn

Find your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.

Optimize Your GitHub Profile

Other professionals are collaborating on GitHub and growing their network. Submit your profile to ensure your profile is on par with leaders in your field.