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Algorithmic Short Selling with Python

You're reading from   Algorithmic Short Selling with Python Refine your algorithmic trading edge, consistently generate investment ideas, and build a robust long/short product

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781801815192
Length 376 pages
Edition 1st Edition
Languages
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Author (1):
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Laurent Bernut Laurent Bernut
Author Profile Icon Laurent Bernut
Laurent Bernut
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Table of Contents (17) Chapters Close

Preface The Stock Market Game 10 Classic Myths About Short Selling FREE CHAPTER Take a Walk on the Wild Short Side Long/Short Methodologies: Absolute and Relative Regime Definition The Trading Edge is a Number, and Here is the Formula Improve Your Trading Edge Position Sizing: Money is Made in the Money Management Module Risk is a Number Refining the Investment Universe The Long/Short Toolbox Signals and Execution Portfolio Management System Other Books You May Enjoy
Index
Appendix: Stock Screening

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% 
 ...
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