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()

def calculate_model_accuracy_metrics(actual, predicted):
    """
    Output model accuracy metrics, comparing predicted values
    to actual values.
    Arguments:
        actual: list. Time series of actual values.
        predicted: list. Time series of predicted values
    Outputs:
        Forecast bias metrics, mean absolute error, mean squared error,
        and root mean squared error in the console
    """
    #Calculate forecast bias
    forecast_errors = [actual[i]-predicted[i] for i in range(len(actual))]
    bias = sum(forecast_errors) * 1.0/len(actual)
    print('Bias: %f' % bias)
    #Calculate mean absolute error
    mae = mean_absolute_error(actual, predicted)
    print('MAE: %f' % mae)
    #Calculate mean squared error and root mean squared error
    mse = mean_squared_error(actual, predicted)
    print('MSE: %f' % mse)
    rmse = sqrt(mse)
    print('RMSE: %f' % rmse)
#Execute in the main block
#Un-difference the data
for i in range(1,len(master_df.index)-1):
    master_df.at[i,'Electricity_Price_Transformed']= master_df.at[i-1,'Electricity_Price_Transformed']+master_df.at[i,'Electricity_Price_Transformed_Differenced_PostProcess']
    
#Back-transform the data
master_df.loc[:,'Predicted_Electricity_Price']=np.exp(master_df['Electricity_Price_Transformed'])
    
#Compare the forecasted data to the real data
print(master_df[master_df['Predicted']==1][['Date','Electricity_Price', 'Predicted_Electricity_Price']])
#Evaluate the accuracy of the results
calculate_model_accuracy_metrics(list(master_df[master_df['Predicted']==1]['Electricity_Price']), 
                                    list(master_df[master_df['Predicted']==1 ['Predicted_Electricity_Price']))

def retrieve_time_series(api, series_ID):
    """
    Return the time series dataframe, based on API and unique Series ID
    api: API that we're connected to
    series_ID: string. Name of the series that we want to pull from the EIA API
    """
    #Retrieve Data By Series ID 
    series_search = api.data_by_series(series=series_ID)
    ##Create a pandas dataframe from the retrieved time series
    df = pd.DataFrame(series_search)
    return df
    
###Execute in the main block
#Create EIA API using your specific API key
api_key = "YOR API KEY HERE"
api = eia.API(api_key)
    
#Pull the electricity price data
series_ID='ELEC.PRICE.TX-ALL.M'
electricity_df=retrieve_time_series(api, series_ID)
electricity_df.reset_index(level=0, inplace=True)
#Rename the columns for easer analysis
electricity_df.rename(columns={'index':'Date',
            electricity_df.columns[1]:'Electricity_Price'}, 
            inplace=True)

def decompose_time_series(series):
    """
    Decompose a time series and plot it in the console
    Arguments: 
        series: series. Time series that we want to decompose
    Outputs: 
        Decomposition plot in the console
    """
    result = seasonal_decompose(series, model='additive')
    result.plot()
    pyplot.show()
#Execute in the main block
#Convert the Date column into a date object
electricity_df['Date']=pd.to_datetime(electricity_df['Date'])
#Set Date as a Pandas DatetimeIndex
electricity_df.index=pd.DatetimeIndex(electricity_df['Date'])
#Decompose the time series into parts
decompose_time_series(electricity_df['Electricity_Price'])

#Pull in natural gas time series data
series_ID='NG.N3035TX3.M'
nat_gas_df=retrieve_time_series(api, series_ID)
nat_gas_df.reset_index(level=0, inplace=True)
#Rename the columns
nat_gas_df.rename(columns={'index':'Date',
            nat_gas_df.columns[1]:'Nat_Gas_Price_MCF'}, 
            inplace=True)
#Convert the Date column into a date object
nat_gas_df['Date']=pd.to_datetime(nat_gas_df['Date'])
#Set Date as a Pandas DatetimeIndex
nat_gas_df.index=pd.DatetimeIndex(nat_gas_df['Date'])
#Decompose the time series into parts
decompose_time_series(nat_gas_df['Nat_Gas_Price_MCF'])
    
#Merge the two time series together based on Date Index
master_df=pd.merge(electricity_df['Electricity_Price'], nat_gas_df['Nat_Gas_Price_MCF'], 
                       left_index=True, right_index=True)
master_df.reset_index(level=0, inplace=True)
    
#Plot the two variables in the same plot
plt.plot(master_df['Date'], 
             master_df['Electricity_Price'], label="Electricity_Price")
plt.plot(master_df['Date'], 
             master_df['Nat_Gas_Price_MCF'], label="Nat_Gas_Price")
# Place a legend to the right of this smaller subplot.
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.title('Natural Gas Price vs. TX Electricity Price over Time')
plt.show()

#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)