Premium: Annual Pass with Ongoing Support and Updates

Master Machine Learning for finance with this practitioner’s guide. Bridge the gap between theory and results using real-world data. From feature engineering to neural networks, learn the techniques driving modern alpha. Includes access to 30 interactive dashboards. This is the only book on machine learning and finance you will ever need.

Our ML XG Boost model was configured for Long/Cash Stock Selection strategy across 25 assets with 10 proprietary engineered features matched for financial time-series.

Note: Our free code templates are no longer open for subscription. We encourage everyone to sign up for the Projects and Premium Code Templates for $5 / month.

In this short course we show you how to code some of the most common functions you will use repeatedly throughout our materials. We also show you how to save them into a .py file that you can then import to all your future projects. This course will put you on the path of creating your own monster investment analysis app!

In this course, we will introduce you to the basic visualizations used in portfolio management. We will show you the most common graphs used, and how you can produce them using two popular Python packages: Seaborn and Plotly. We also teach you some novel graphing techniques you won't find anywhere else. Oh, and did we mention there is a challenging assignment at the end? You will love it!

In this notebook, you will learn the foundations and important aspects of the most widely used Python libraries in data science: Pandas. We will teach you the foundational data manipulation techniques that are must haves in your data science tool kit. This course is available through the Python Programming Basics bundle.

In this introductory course, we will teach the basics of Python, and get you ready for everything you need to become a quantitative finance expert. This course is available through the Python Programming Basics bundle.

In this course, you will build on what you learned in part 1. You will dive into some of the most important and widely used features in any programming language: loops and if statements . This course is available through the Python Programming Basics bundle.

This section contains the main_functions.py files for annual subscribers. These files contain all functions, from every course, that you can use in all of our courses or in your own projects. We update these files frequently, so please make sure to check for updates. Last Update: 2025-05-01

In these projects, we will test what you have learned, reinforce important material, and introduce new concepts that can be taught within a half-hour. Are you ready for a challenge that will elevate your game? We also provide some of our premium code templates that will teach you some advanced financial, machine learning and economic concepts that only pros with experience know.

Build a production-ready momentum trading strategy using GPT as your research copilot. 8 hands-on episodes take you from raw data to backtested strategy. Includes 8 Jupyter notebooks, complete dataset, and 80-page strategy book. Audit-first approach with walk-forward validation.

Process is everything. That is the secret of top portfolio managers. In this short series, we show you the elements of successful portfolio construction. We introduce you to the investment management workflow, which outlines every step in the portfolio management process. In our courses, we will show you how to do each one of these steps and how we can use AI to improve them.

In this course, we will introduce you to machine learning in investment management through four targeted case studies. Each case is designed to help you improve one step in the investment management pathway. This is all about giving you practical skills and showing you how experts do this in the real world!

The materials for Cordell's CFA Society North Carolina presentation on January 29, 2025, are available below. Please note that these will be moved to the Projects section within Digital Hub Labs on February 28, 2025.

In this course, you will learn how to use OpenBB, FMP and natural language processing to create investment peer groups. This allows you to build your own ETF universes based on keyword searches. With these categories, you can then perform investment due diligence and find the funds that match your target criteria. This can also be used to create your own dashboards!

In this course, we introduce you to the basic concepts of asset allocation: analyzing asset classes for inclusion in a strategic asset allocation policy. Many say that the majority of the returns of a portfolio are driven primarily by the asset classes you select. We will show how to use Python to analyze asset classes so that you can get a full understanding of how they behave.

Practical Machine Learning in Finance is a hands-on course that teaches you how to apply machine learning to real investment problems. You’ll learn by doing—building classification models, engineering features, handling class imbalance, evaluating performance, and avoiding common pitfalls. Designed for finance professionals and students, the course focuses on practical implementation, not just theory.

Practical Machine Learning in Finance is a hands-on course that teaches you how to apply machine learning to real investment problems. You’ll learn by doing—building classification models, engineering features, handling class imbalance, evaluating performance, and avoiding common pitfalls. Designed for finance professionals and students, the course focuses on practical implementation, not just theory.

Practical Machine Learning in Finance is a hands-on course that teaches you how to apply machine learning to real investment problems. You’ll learn by doing—building classification models, engineering features, handling class imbalance, evaluating performance, and avoiding common pitfalls. Designed for finance professionals and students, the course focuses on practical implementation, not just theory.

Practical Machine Learning in Finance is a hands-on course that teaches you how to apply machine learning to real investment problems. You’ll learn by doing—building classification models, engineering features, handling class imbalance, evaluating performance, and avoiding common pitfalls. Designed for finance professionals and students, the course focuses on practical implementation, not just theory.

This section provides information about navigating our site, the suggested course progression, and other materials to help guide you along your journey with us.

Go beyond theory: build a complete, data-driven momentum strategy. First, validate the idea with data visualization. Next, build the strategy: gather/clean ETF data & generate signals. Finally, code a realistic backtest with rebalancing & build a professional 'tear sheet' to analyze risk, drawdowns, & Sharpe ratios. Walk away with a working strategy & reusable analysis library.

Learn to design and implement a systematic investment trading strategy using Python. In this course we will show you how to develop s trading strategy that rotates between 2 ETFs: The market-cap weighted SPY and the equal weighted S&P 500 RSP. This strategy has outperformed the S&P 500 over the past 20 years and you will learn how to deploy it!