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Algorithmic Short Selling with Python

Algorithmic Short Selling with Python - Second Edition

By : Laurent Bernut
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Algorithmic Short Selling with Python

Algorithmic Short Selling with Python

By: Laurent Bernut

Overview of this book

Algorithmic Short Selling with Python, Second Edition is a practical guide to building, testing, and managing systematic short-selling strategies in today's markets. Structured around the core challenges every short seller faces, the book provides a framework for continuously generating long/short ideas, identifying bullish/bearish market regimes, detecting sector rotation ahead of consensus, constructing robust long/short portfolios, and managing the unique risks of the short side. Through real-world examples and working Python code based on S&P 500 data, readers learn how to develop quantitative strategies that address position sizing, crowded trades, portfolio exposures, and capital allocation across changing market conditions. The book also explores advanced topics such as relative strength analysis, fractals, convexity, long/short portfolio management, asset allocation, and the use of AI-powered trading journals to uncover the behavioral patterns that influence trading decisions. Every concept is supported by implementation, bridging the gap between theory and execution. Expanding on the first edition, this updated version transforms ideas into fully coded solutions, providing readers with the tools to design, evaluate, and deploy systematic short-selling strategies with confidence, discipline, and consistency.
Table of Contents (16 chapters)
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1
Part 1: The Short Selling Game
5
Part 2: The Outer Game: The Trading Edge
10
Part 3: The Long/Short Game: Portfolio Construction
15
Index

4

Regime Definition

During the Napoleonic wars, field surgeons with limited resources had to make quick decisions as to who would need surgery, who could survive without it, and the unfortunate ones for whom nothing could be done. Triage was born out of necessity to allocate limited time and resources in the most efficient and humane way possible. In the stock market, regime is another word for triage. Some are bullish, some are bearish, and some are inconclusive.

Markets tend to stay wrong a lot longer than investors tend to stick with you. Segregating stocks into different regime buckets—triaging them—before performing in-depth analysis is an efficient allocation of resources. The objective of this initial triage is not to predict where stocks could, would, or should be headed, but to practice the long-lost art of actively listening to what the market has to say.

Some market participants like to spend time and resources building bearish cases for bullish...

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