New technologies are transforming the insurance industry. Tomorrow’s winners will be those insurers able to convert vast amounts of data into actionable insights about clients and products alike. An increasing demand for machine learning methods, combined with a growing shortage of data scientists able to create, implement and communicate these methods, call for a data pipeline that is as efficient as possible.

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This post is about publishing your first Python package on PyPI.

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This post features an overview of the development process in Python.

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This post features some practical examples on how to parse different date formats from flat text files, using Pandas.

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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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