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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

Moving average crossover

Moving averages are another popular regime definition method. This method is so simple and prevalent that even the most hardcore fundamental analysts who claim never to look at charts still like to have a 200-day simple moving average. This method is also computationally easy. There may be further refinements as to the type of moving averages from simple to exponential, triangular, adaptive. Yet, the principle is the same. When the faster moving average is above the slower one, the regime is bullish. When it is below the slower one, the regime is bearish. The code below shows how to calculate the regime with two moving averages using simple and exponential moving averages (SMA and EMA respectively):

#### Regime SMA EMA ####
def regime_sma(df,_c,st,lt):
    '''
    bull +1: sma_st >= sma_lt , bear -1: sma_st <= sma_lt
    '''
    sma_lt = df[_c].rolling(lt).mean()
    sma_st = df[_c].rolling(st).mean()
    rg_sma...
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