Learn Foundations of Python Natural Language Processing and Computer Vision with my Video Course: Applications of Statistical Learning with Python

I’m pleased to announce my fourth and final video course. The course has already been out for a couple months by now, but that doesn’t mean it’s too late for me to write about it!

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Unpacking NumPy and Pandas: The Book Is Coming Soon!

I have heard from my publisher, Packt Publishing, that my video course Unpacking NumPy and Pandas will become a book!

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Learn Basic Python and scikit-learn Machine Learning Hands-On with My Course: Training Your Systems with Python Statistical Modelling

This post is actually months late, but like with my last video course announcement, it’s better late than never. And besides, of my video courses, I had the most fun writing this one.

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Start Getting and Working with Data with “Data Acquisition and Manipulation with Python”

This news is a few weeks late, but better late than never!

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Get Started Learning Python for Data Science with “Unpacking NumPy and Pandas”

I have exciting news!

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Stock Trading Analytics and Optimization in Python with PyFolio, R’s PerformanceAnalytics, and backtrader

DISCLAIMER: Any losses incurred based on the content of this post are the responsibility of the trader, not me. I, the author, neither take responsibility for the conduct of others nor offer any guarantees. None of this should be considered as financial advice; the content of this article is only for educational/entertainment purposes.

Introduction

Having figured out how to perform walk-forward analysis in Python with backtrader, I want to have a look at evaluating a strategy’s performance. So far, I have cared about only one metric: the final value of the account at the end of a backtest relative. This should not be the only metric considered. Most people care not only about how much money was made but how much risk was taken on. People are risk-averse; one of finance’s leading principles is that higher risk should be compensated by higher returns. Thus many metrics exist that adjust returns for how much risk was taken on. Perhaps when optimizing only with respect to the final return of the strategy we end up choosing highly volatile strategies that lead to huge losses in out-of-sample data. Adjusting for risk may lead to better strategies being chosen.

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