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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

Technical requirements

The code in this chapter requires Python 3.9 or later with the requests and pandas packages installed. All Airtable communication uses the REST API directly via requests, so no Airtable SDK is needed. Credentials are stored in a .env file at the project root containing three keys:

  • AIRTABLE_API_KEY: your personal access token from Airtable developer settings
  • AIRTABLE_BASE_ID: the base identifier, beginning with app
  • AIRTABLE_TABLE: set to TradeLog

The notebook file is trading‑journal‑v5.ipynb in the companion repository.

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