stock-correlations-log_returns.py

# create empty dataframe for log returns information
log_returns_df = pd.DataFrame()

# calculate log returns of each asset
# loop through each column in dataframe and and calculate the daily log returns 
# add log returns column to new a dataframe
for col in raw_asset_prices_df.columns:
    # dates are given in reverse order so need to set diff to -1.
    log_returns_df[col] = np.log(raw_asset_prices_df[col]).diff(-1)
    
#check output of log returns dataframe
log_returns_df.head()