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

Position sizing is the link between emotional and financial capital

"This is a great experiment for many reasons. It ought to become part of the basic education of anyone interested in finance or gambling."

– Edward Thorp, a (super)man for all markets

Victor Haghani, founder of Elm and former trader at LTCM, conducted an experiment on 61 volunteers, bright students in finance and sophisticated investment professionals. Participants were given $25 starting capital and were told to flip a virtual coin for 30 minutes, being told, "the coin is biased to come up heads with a 60% probability, and you can bet as much as you like on heads or tails on each flip." How much would you bet? It appears there is a formula to calculate the optimal bet size that would maximize long-term geometric returns. The Kelly criterion formula is:

def kelly(win_rate,avg_win,avg_loss):  
    # Kelly = win% / abs(avg_loss%) - loss% / avg_win% 
 ...