Book Image

Algorithmic Short Selling with Python

By : Laurent Bernut
Book Image

Algorithmic Short Selling with Python

By: Laurent Bernut

Overview of this book

If you are in the long/short business, learning how to sell short is not a choice. Short selling is the key to raising assets under management. This book will help you demystify and hone the short selling craft, providing Python source code to construct a robust long/short portfolio. It discusses fundamental and advanced trading concepts from the perspective of a veteran short seller. This book will take you on a journey from an idea (“buy bullish stocks, sell bearish ones”) to becoming part of the elite club of long/short hedge fund algorithmic traders. You’ll explore key concepts such as trading psychology, trading edge, regime definition, signal processing, position sizing, risk management, and asset allocation, one obstacle at a time. Along the way, you’ll will discover simple methods to consistently generate investment ideas, and consider variables that impact returns, volatility, and overall attractiveness of returns. By the end of this book, you’ll not only become familiar with some of the most sophisticated concepts in capital markets, but also have Python source code to construct a long/short product that investors are bound to find attractive.
Table of Contents (17 chapters)
14
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15
Index

Robustness score

"Masala: a varying blend of spices used in Indian cooking."

– Merriam Webster dictionary

The Grit Index, Common Sense Ratio, and Van Tharp's SQN all measure robustness. The Grit Index is probably the most elegant and accessible metric for non-finance people. The CSR is a good canary in the coal mine to ferret out dodgy mean reversion strategies. SQN is a solid staple measure of quality. They all do the job. They measure a specific aspect of robustness:

  1. The Grit Index integrates losses throughout the period. It gives an accurate vision of performance of all aspects of downside: magnitude, frequency, and duration.
  2. The CSR combines risks endemic to the two types of strategies in a single measure. It shows how risk is balanced for each metric.
  3. The t-stat SQN incorporates trading frequency into the trading edge formula to show the most efficient use of capital.

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