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Python api with live ercot real time prices

#Conver the dataframe to a numpy array
master_array=np.array(master_df[['Electricity_Price_Transformed_Differenced', 
                                     'Nat_Gas_Price_MCF_Transformed_Differenced']].dropna())
    
#Generate a training and test set for building the model: 95/5 split
training_set = master_array[:int(0.95*(len(master_array)))]
test_set = master_array[int(0.95*(len(master_array))):]
    
#Fit to a VAR model
model = VAR(endog=training_set)
model_fit = model.fit()
#Print a summary of the model results
model_fit.summary()

Source: techrando.com

Python api with live ercot real time prices

#Transform the columns using natural log
master_df['Electricity_Price_Transformed']=np.log(master_df['Electricity_Price'])
master_df['Nat_Gas_Price_MCF_Transformed']=np.log(master_df['Nat_Gas_Price_MCF'])
    
#Difference the data by 1 month
n=1
master_df['Electricity_Price_Transformed_Differenced'] = master_df['Electricity_Price_Transformed'] - master_df['Electricity_Price_Transformed'].shift(n)
master_df['Nat_Gas_Price_MCF_Transformed_Differenced'] = master_df['Nat_Gas_Price_MCF_Transformed'] - master_df['Nat_Gas_Price_MCF_Transformed'].shift(n)

Source: techrando.com

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