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.
This post is about publishing your first Python package on PyPI.
This post features an overview of the development process in Python.
This post features some practical examples on how to parse different date formats from flat text files, using Pandas.
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.