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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

Summary

This chapter started from a single premise: debugging the programmer is harder than debugging the code, and it requires its own systematic tooling.

You now have that tooling. The Airtable base holds two tables: the TradeLog captures every trade, missed trade, and override in a FIFO-aware structure that distinguishes between intent and execution; the journal table captures the pre-market and post-market mental state through two mobile forms that together require seven minutes of daily effort.

Part A automates the reconciliation of those two realities. A single outer merge classifies every order-versus-execution pair into matched, missed, or override, routes matched trades through four position handlers that correctly manage pyramids, partial exits, and reversals, and writes the full audit trail to Airtable without manual intervention.

Part B automates the psychology loop. A gamified scoring system rewards completeness over perfection. A streak multiplier compounding...

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