AI for Trading training
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.
Skills you'll learn
Pandas ? Financial portfolio risk models ? Financial trading ? Word2vec
Prerequisites
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
Quantitative Trading
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.
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.
AI Algorithms in Trading
Learn how to analyze alternative data and use machine learning to generate trading signals. Run a backtest to evaluate and combine top performing signals.
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.
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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.
Python Refresher
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.
Linear Algebra
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.
contact us
email - [email protected]