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

Appendix: Stock Screening

This appendix provides a stock screener tool that will allow you to put everything we have learned in this book into practice. It addresses the most pressing issue for market participants: idea generation. We will build a screener across all the constituents of the S&P 500 index.

The sequence of events is as follows:

  1. Download all the current constituents of the S&P 500 from its Wikipedia webpage.
  2. Batch download OHLCV prices data from Yahoo Finance. We will drop the level to process each stock individually.
  3. Calculate the rebased relative series.
  4. Calculate regimes—breakout, turtle, moving averages (Simple Moving Average (SMA) and Exponential Moving Average (EMA)), and floor/ceiling—on both absolute and relative series. There will be an option to save each stock as a CSV file.
  5. Create a dictionary with the last row of each stock and append a list, from which we will create a dataframe.
  6. Sum up...