In this section you can download the projects_functions files which should be imported into project and premium code template notebooks. Please watch the Main Functions primer in the Tutorials section.
This short video and notebook will provide a basic review of:
Python data structures
Basic pandas methods
Advanced pandas methods
In this short project, you will test the old adage of whether it is better to sell in May and reinvest in the fall. In this project, you will learn how to create seasonal performance heatmaps.
In this project, we will demonstrate why alternative investments present misleading risk statistics. We will also show you how to correct their returns for auto-correlations, which artificially understate their volatility.
In this project, you will reinforce what you learned in case study 1 of our Introduction to Machine Learning in Portfolio Construction course.
You will classify various fixed income funds based on different risk and return features.
This notebook provides a detailed analysis of yearly returns for various asset classes, using a calendar year heatmap to visualize performance trends. The analysis aims to help understand asset performance over a multi-year period, assisting in the identification of diversification benefits and overall portfolio risk-return characteristics.
In this section, you will find all notebooks related to macro analysis.
This premium code template provides you with some insight into quarterly US GDP releases. We show you the tricks that the pros use to get as much information as possible from the release. We also show you some advanced visualization techniques, and create a summary table that gives you all of the important information from the GDP report.
This is a great way to learn Python, economics in less than a half hour!
Please make sure to download the accompanying xlsx file that contains the FRED identifiers that you will need to download the data. This xlsx file should be copied into your Colab instance.