Pandas Ewm Var And Std

I unsuccessfully tried to replicate the calculation of exponential weighted moving variance. here is the code I used. import pandas as pd import numpy as np l = [12., 12.5, 13.1,

Solution 1:

Copy-pasted the code posted by kosnik and build it up to answer this question. below:

# Import librariesimport numpy as np
import pandas as pd

# Create DataFrame
l = [12., 12.5, 13.1, 14.6, 17.8, 19.1, 24.5]
df = pd.DataFrame(data=l, columns=['data'])


# Initialize 
N = 5# Span
a = 2./(1+N) # Alpha# Use .evm() to calculate 'exponential moving variance' directly
var_pandas = df.ewm(span=N).var()

# Initialize variable
varcalc=[]

# Calculate exponential moving variancefor i inrange(0,len(df.data)):

    # Get window
    z = np.array(df.data.iloc[0:i+1].tolist())

    # Get weights: w
    n = len(z)
    w = (1-a)**np.arange(n-1, -1, -1) # This is reverse order to match Series order# Calculate exponential moving average
    ewma = np.sum(w * z) / np.sum(w)

    # Calculate bias
    bias = np.sum(w)**2 / (np.sum(w)**2 - np.sum(w**2))

    # Calculate exponential moving variance with bias
    ewmvar = bias * np.sum(w * (z - ewma)**2) / np.sum(w)

    # Calculate standard deviation
    ewmstd = np.sqrt(ewmvar)

    varcalc.append(ewmvar)
    #print('ewmvar:',ewmvar)#varcalc
df['var_pandas'] = var_pandas
df['varcalc'] = varcalc
df