A problem with a script of Hurst exponent with 9 periods

AI & Machine learning
1

Hello there:



 I was actually writing a code in order to  evaluate the Hurst exponent of 9 periods in python, after that the code reads an excel sheet and puts the data into a pandas dataframe,  and evaluates the hurst exponent for the column of the lows and the column of the highs (using only 9 periods of data)  but I get this error:





  File "C:\Users\Ghery\anaconda3\lib\site-packages\pandas\core\indexing.py", line 761, in _validate_key_length

    raise IndexingError("Too many indexers")



IndexingError: Too many indexers



The code I used was this:



from scipy import stats

import pandas as pd

import numpy as np

from scipy import stats







def Hurst9(df):

    # calculate returns and eliminate the first row

    df = df.pct_change()    

    df = df.iloc[1:]

    # split the dataframe in 2 dataframes each one with the first and the last rows

    df1 = df.iloc[:4,:]

    df2 = df.iloc[4:,:]

    # split the later dataframes in 2 other dataframes 

    df1_1 = df1.iloc[:2,:]

    df1_2 = df1.iloc[2:,:]

    df2_1 = df2.iloc[:2,:]

    df2_2 = df2.iloc[2:,:]

    # calculate the standard deviation of every dataframe

    stdev = df.std()

    stdev1 = df1.std()

    stdev2 = df2.std()

    stdev1_1 = df1_1.std()

    stdev1_2 = df1_2.std()

    stdev2_1 = df2_1.std()

    stdev2_2 = df2_2.std()

    # Rest the mean for every column

    df = df.add(-df.mean())

    df1 = df1.add(-df1.mean())

    df2 = df2.add(-df2.mean())

    df1_1 = df1_1.add(-df1_1.mean())

    df1_2 = df1_2.add(-df1_2.mean())

    df2_1 = df2_1.add(-df2_1.mean())

    df2_2 = df2_2.add(-df2_2.mean())

    # Calculate the cumulative sum of every dataframe

    df = df.cumsum()

    df1 = df1.cumsum()

    df2 = df2.cumsum()

    df1_1 = df1_1.cumsum()

    df1_2 = df1_2.cumsum()

    df2_1 = df2_1.cumsum()

    df2_2 = df2_2.cumsum()

    # Calculate the range for each column

    r = df.max() - df.min()

    r1 = df1.max() - df1.min()

    r2 = df2.max() - df2.min()

    r1_1 = df1_1.max() - df1_1.min()

    r1_2 = df1_2.max() - df1_2.min()

    r2_1 = df2_1.max() - df2_1.min()

    r2_2 = df2_2.max() - df2_2.min()

    # Calculate the rescaled range

    rs = r/stdev 

    rs1 = r1 / stdev1

    rs2 = r2 / stdev2

    rs1_1 = r1_1 / stdev1_1

    rs1_2 = r1_2 / stdev1_2

    rs2_1 = r2_1 / stdev2_1

    rs2_2 = r2_2 / stdev2_2

    # Calculate average rescaled range for each chunk

    ave_RS = float(rs) 

    ave_RS_1 = float(0.5*(rs1 + rs2))

    ave_RS_2 = float((0.25*(rs1_1 + rs1_2 + rs2_1 + rs2_2)))

    # Evaluate the natural logarithm for each size and each average rescaled range

    x = np.log(np.array([8,4,2]))

    y = np.log(np.array([ave_RS,ave_RS_1,ave_RS_2]))

    slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)

    return slope





data = pd.read_excel('file.xlsx')

Hurst_low = Hurst9(data['low'])

Hurst_high = Hurst9(data['high'])



print('Hurst low is',  Hurst_low)



print('Hurst high is',  Hurst_high)





Can Anyone tell me how to solve this???  whats wrong with my code??









 

2

Hi Ghery,



This may happen sometimes when indexes are similar or contain similar values.

For more details, you can also refer to the following thread - 



https://stackoverflow.com/questions/30781037/too-many-indexers-with-dataframe-loc





Regards,

Akshay

3

Ok, thanks… but I reviewed the link and I am still not sure how to fix

4

Hi Ghery,



One possible solution can be splitting the rows simply using df.iloc[:X]

For example - 



df1 = df.iloc[:4]

df2 = df.iloc[4:]



I hope this helps.



Thanks,

Akshay