Actuarial Data Science

Bridging the Gap between Actuarial and Data Science

Fri 28 December 2018

PCA from several perspectives

Posted by Pieter Marres in Articles   

Principal Component Analysis (PCA) is a useful technique when you want to get a better understanding of a dataset containing a large number of correlated features. For example, in image processing, PCA is used for data compression. The main objective is to reduce the complexity of the data with a minimum loss of information.

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Mon 23 April 2018

Creating Performance Reports with Backtrader

Posted by Pieter Marres in Articles   

When it comes to testing and comparing investment strategies, the Python ecosystem offers an interesting alternative for R’s quantstrat. I’m talking here about backtrader, a library that has been around for a while now. In this post, I propose a 1 page PDF report featuring both graphical output and essential performance statistics for making a sound judgment about the quality of a trading strategy.

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