I have many (4000+) CSVs of stock data (Date, Open, High, Low, Close) which I import into individual Pandas dataframes to perform analysis. I am new to Python and want to calculate a rolling 12month beta for each stock, I found a post to calculate rolling beta (Python pandas calculate rolling stock beta using rolling apply to groupby object in vectorized fashion) however when used in my code below takes over 2.5 hours! Considering I can run the exact same calculations in SQL tables in under 3 minutes this is too slow.

How can I improve the performance of my below code to match that of SQL? I understand Pandas/Python has that capability. My current method loops over each row which I know slows performance but I am unaware of any aggregate way to perform a rolling window beta calculation on a dataframe.

Note: the first 2 steps of loading the CSVs into individual dataframes and calculating daily returns only takes ~20seconds. All my CSV dataframes are stored in the dictionary called FilesLoaded with names such as XAO.

import pandas as pd, numpy as np
import datetime
import ntpath
pd.set_option('precision',10)  #Set the Decimal Point precision to DISPLAY

MarketIndex = 'XAO'
period = 250
MinBetaPeriod = period
# ***********************************************************************************************
# ***********************************************************************************************
for File in FilesLoaded:
    FilesLoaded[File]['Return'] = FilesLoaded[File]['Close'].pct_change()
# ***********************************************************************************************
# ***********************************************************************************************
def calc_beta(df):
    np_array = df.values
    m = np_array[:,0] # market returns are column zero from numpy array
    s = np_array[:,1] # stock returns are column one from numpy array
    covariance = np.cov(s,m) # Calculate covariance between stock and market
    beta = covariance[0,1]/covariance[1,1]
    return beta

#Build Custom "Rolling_Apply" function
def rolling_apply(df, period, func, min_periods=None):
    if min_periods is None:
        min_periods = period
    result = pd.Series(np.nan, index=df.index)
    for i in range(1, len(df)+1):
        sub_df = df.iloc[max(i-period, 0):i,:]
        if len(sub_df) >= min_periods:  
            idx = sub_df.index[-1]
            result[idx] = func(sub_df)
    return result

#Create empty BETA dataframe with same index as RETURNS dataframe
df_join = pd.DataFrame(index=FilesLoaded[MarketIndex].index)    
df_join['market'] = FilesLoaded[MarketIndex]['Return']
df_join['stock'] = np.nan

for File in FilesLoaded:
    df_join  = df_join.replace(np.inf, np.nan) #get rid of infinite values "inf" (SQL won't take "Inf")
    df_join  = df_join.replace(-np.inf, np.nan)#get rid of infinite values "inf" (SQL won't take "Inf")
    df_join  = df_join.fillna(0) #get rid of the NaNs in the return data
    FilesLoaded[File]['Beta'] = rolling_apply(df_join[['market','stock']], period, calc_beta, min_periods = MinBetaPeriod)

# ***********************************************************************************************
# ***********************************************************************************************
print('Run-time: {0}'.format(datetime.datetime.now() - start_time))
  • \$\begingroup\$ It might be useful to look at the documentation for pandas linear regression (pandas.pydata.org/pandas-docs/version/0.9.0/…) , which has an example of computing a linear regression model and calculating beta with a rolling window. \$\endgroup\$ – newToProgramming Sep 15 '16 at 2:34
  • \$\begingroup\$ Have you looked into the dask package? Rather than doing this individually for each DataFrame, dask will create a single "virtual" DataFrame out of your on-disk data and figure out itself how best to run it. \$\endgroup\$ – TheBlackCat Sep 16 '16 at 14:05
  • \$\begingroup\$ @newToProgramming those rolling window functions are being removed from Pandas in place of Statsmodel. \$\endgroup\$ – cwse Sep 18 '16 at 22:38
  • \$\begingroup\$ @TheBlackCat any Ideas how I can apply Dask? Any examples for my situation? \$\endgroup\$ – cwse Sep 18 '16 at 22:38
  • \$\begingroup\$ @cwse: Try here: dask.pydata.org/en/latest/dataframe-create.html , then you probably want to you use rolling_apply: dask.pydata.org/en/latest/…. rolling_apply is also available for regular pandas, but dask can operate on your entire set of csv files at once. \$\endgroup\$ – TheBlackCat Sep 19 '16 at 14:55

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