How can I calculate the Maximum Drawdown MDD in python

  • I need to calculate the a time dynamic Maximum Drawdown in Python. The problem is that e.g.:

    ( df.CLOSE_SPX.max() - df.CLOSE_SPX.min() ) / df.CLOSE_SPX.max()

    can't work since these functions use all data and not e.g. considering the minimum only from a given maximum onwards on the timeline. Does anone know how to implement that in python?

    This is a short example of the dataframe used:

                  CLOSE_SPX    Close_iBoxx  A_Returns  B_Returns  A_Vola    B_Vola
    2014-05-15    1870.85      234.3017    -0.009362   0.003412   0.170535  0.075468   
    2014-05-16    1877.86      234.0216     0.003747  -0.001195   0.170153  0.075378
    2014-05-19    1885.08      233.7717     0.003845  -0.001068   0.170059  0.075384   
    2014-05-20    1872.83      234.2596    -0.006498   0.002087   0.170135  0.075410   
    2014-05-21    1888.03      233.9101     0.008116  -0.001492   0.169560  0.075326   
    2014-05-22    1892.49      233.5429     0.002362  -0.001570   0.169370  0.075341   
    2014-05-23    1900.53      233.8605     0.004248   0.001360   0.168716  0.075333   
    2014-05-27    1911.91      234.0368     0.005988   0.000754   0.168797  0.075294   
    2014-05-28    1909.78      235.4454    -0.001114   0.006019   0.168805  0.075474   
    2014-05-29    1920.03      235.1813     0.005367  -0.001122   0.168866  0.075451   
    2014-05-30    1923.57      235.2161     0.001844   0.000148   0.168844  0.075430   
    2014-06-02    1924.97      233.8868     0.000728  -0.005651   0.168528  0.075641   
    2014-06-03    1924.24      232.9049    -0.000379  -0.004198   0.167852  0.075267
    
  • MarkD

    MarkD Correct answer

    6 years ago

    You can get this using a pandas rolling_max to find the past maximum in a window to calculate the current day's drawdown, then use a rolling_min to determine the maximum drawdown that has been experienced.

    Lets say we wanted the moving 1-year (252 trading day) maximum drawdown experienced by a particular symbol. The following should do the trick:

    import pandas as pd
    import pandas_datareader as  web
    import matplotlib.pyplot as pp
    import datetime
    
    # Get SPY data for past several years
    
    SPY_Dat = web.DataReader('SPY', 'yahoo', datetime.date(2007,1,1))
    
    # We are going to use a trailing 252 trading day window
    window = 252
    
    # Calculate the max drawdown in the past window days for each day in the series.
    # Use min_periods=1 if you want to let the first 252 days data have an expanding window
    Roll_Max = SPY_Dat['Adj Close'].rolling(window, min_periods=1).max()
    Daily_Drawdown = SPY_Dat['Adj Close']/Roll_Max - 1.0
    
    # Next we calculate the minimum (negative) daily drawdown in that window.
    # Again, use min_periods=1 if you want to allow the expanding window
    Max_Daily_Drawdown = Daily_Drawdown.rolling(window, min_periods=1).min()
    
    # Plot the results
    Daily_Drawdown.plot()
    Max_Daily_Drawdown.plot()
    pp.show()
    

    Which yields (Blue is daily running 252-day drawdown, green is maximum experienced 252-day drawdown in the past year): Max Drawdown Result

    Note: with the newest

    Looks good, but it returns a value error: ValueError Traceback (most recent call last) D:\Python Modules\MDDown.pyx in () 20 21 # Plot the results ---> 22 Daily_Drawdown.plot() 23 Max_Daily_Drawdown.plot() 24 pp.show()

    Looks like there might be a problem with your pandas/matplotlib integration.. check the Max_Daily_Drawdown variable.. it should contain what you need.

    Works, perfect! Thanks a lot, MarkD! I highly appreciate your support!

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Content dated before 7/24/2021 11:53 AM

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