This is the ninth video that goes through the fortitudo-tech Python package available at: https://github.com/fortitudo-tech/fortitudo.tech1
The video goes through the eighth example, which shows you how you can use Entropy Pooling2 to implement (discretionary) views and stress-test for fully general risk factor and return distributions.
The risk factor computations will also be used in next week’s video, where the Causal and Predictive Market Views and Stress-Testing framework3 will be presented.
For a deep and pedagogical walkthrough of the investment framework and methods, see Portfolio Construction and Risk Management book4.
You can still contribute to the project and get perks for your contribution by becoming a paid subscriber to this publication, which will give you access to the Applied Quantitative Investment Management course and the expanding collection of exclusive case studies.
Watch the next video here:
8. Causal and Predictive Views and Stress-Testing
This is the tenth video that goes through the fortitudo-tech Python package available at: https://github.com/fortitudo-tech/fortitudo.tech
This video is also available on YouTube5 if you prefer watching it there.
GitHub repository for the fortitudo.tech Python package: https://github.com/fortitudo-tech/fortitudo.tech
Entropy Pooling Collection: https://antonvorobets.substack.com/p/entropy-pooling-collection
Causal and Predictive Market Views and Stress-Testing SSRN article: https://ssrn.com/abstract=4444291
Portfolio Construction and Risk Management Book latest PDF: https://antonvorobets.substack.com/p/pcrm-book
fortitudo.tech Python package walkthrough YouTube playlist: https://www.youtube.com/playlist?list=PLfI2BKNVj_b2rurUsCtc2F8lqtPWqcs2K
