python api with live ercot real time prices

1def retrieve_time_series(api, series_ID):
2    """
3    Return the time series dataframe, based on API and unique Series ID
4    api: API that we're connected to
5    series_ID: string. Name of the series that we want to pull from the EIA API
6    """
7    #Retrieve Data By Series ID 
8    series_search = api.data_by_series(series=series_ID)
9    ##Create a pandas dataframe from the retrieved time series
10    df = pd.DataFrame(series_search)
11    return df
12    
13###Execute in the main block
14#Create EIA API using your specific API key
15api_key = "YOR API KEY HERE"
16api = eia.API(api_key)
17    
18#Pull the electricity price data
19series_ID='ELEC.PRICE.TX-ALL.M'
20electricity_df=retrieve_time_series(api, series_ID)
21electricity_df.reset_index(level=0, inplace=True)
22#Rename the columns for easer analysis
23electricity_df.rename(columns={'index':'Date',
24            electricity_df.columns[1]:'Electricity_Price'}, 
25            inplace=True)

1def calculate_model_accuracy_metrics(actual, predicted):
2    """
3    Output model accuracy metrics, comparing predicted values
4    to actual values.
5    Arguments:
6        actual: list. Time series of actual values.
7        predicted: list. Time series of predicted values
8    Outputs:
9        Forecast bias metrics, mean absolute error, mean squared error,
10        and root mean squared error in the console
11    """
12    #Calculate forecast bias
13    forecast_errors = [actual[i]-predicted[i] for i in range(len(actual))]
14    bias = sum(forecast_errors) * 1.0/len(actual)
15    print('Bias: %f' % bias)
16    #Calculate mean absolute error
17    mae = mean_absolute_error(actual, predicted)
18    print('MAE: %f' % mae)
19    #Calculate mean squared error and root mean squared error
20    mse = mean_squared_error(actual, predicted)
21    print('MSE: %f' % mse)
22    rmse = sqrt(mse)
23    print('RMSE: %f' % rmse)
24#Execute in the main block
25#Un-difference the data
26for i in range(1,len(master_df.index)-1):
27    master_df.at[i,'Electricity_Price_Transformed']= master_df.at[i-1,'Electricity_Price_Transformed']+master_df.at[i,'Electricity_Price_Transformed_Differenced_PostProcess']
28    
29#Back-transform the data
30master_df.loc[:,'Predicted_Electricity_Price']=np.exp(master_df['Electricity_Price_Transformed'])
31    
32#Compare the forecasted data to the real data
33print(master_df[master_df['Predicted']==1][['Date','Electricity_Price', 'Predicted_Electricity_Price']])
34#Evaluate the accuracy of the results
35calculate_model_accuracy_metrics(list(master_df[master_df['Predicted']==1]['Electricity_Price']), 
36                                    list(master_df[master_df['Predicted']==1 ['Predicted_Electricity_Price']))

1#Pull in natural gas time series data
2series_ID='NG.N3035TX3.M'
3nat_gas_df=retrieve_time_series(api, series_ID)
4nat_gas_df.reset_index(level=0, inplace=True)
5#Rename the columns
6nat_gas_df.rename(columns={'index':'Date',
7            nat_gas_df.columns[1]:'Nat_Gas_Price_MCF'}, 
8            inplace=True)
9#Convert the Date column into a date object
10nat_gas_df['Date']=pd.to_datetime(nat_gas_df['Date'])
11#Set Date as a Pandas DatetimeIndex
12nat_gas_df.index=pd.DatetimeIndex(nat_gas_df['Date'])
13#Decompose the time series into parts
14decompose_time_series(nat_gas_df['Nat_Gas_Price_MCF'])
15    
16#Merge the two time series together based on Date Index
17master_df=pd.merge(electricity_df['Electricity_Price'], nat_gas_df['Nat_Gas_Price_MCF'], 
18                       left_index=True, right_index=True)
19master_df.reset_index(level=0, inplace=True)
20    
21#Plot the two variables in the same plot
22plt.plot(master_df['Date'], 
23             master_df['Electricity_Price'], label="Electricity_Price")
24plt.plot(master_df['Date'], 
25             master_df['Nat_Gas_Price_MCF'], label="Nat_Gas_Price")
26# Place a legend to the right of this smaller subplot.
27plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
28plt.title('Natural Gas Price vs. TX Electricity Price over Time')
29plt.show()

1#Conver the dataframe to a numpy array
2master_array=np.array(master_df[['Electricity_Price_Transformed_Differenced', 
3                                     'Nat_Gas_Price_MCF_Transformed_Differenced']].dropna())
4    
5#Generate a training and test set for building the model: 95/5 split
6training_set = master_array[:int(0.95*(len(master_array)))]
7test_set = master_array[int(0.95*(len(master_array))):]
8    
9#Fit to a VAR model
10model = VAR(endog=training_set)
11model_fit = model.fit()
12#Print a summary of the model results
13model_fit.summary()

1def decompose_time_series(series):
2    """
3    Decompose a time series and plot it in the console
4    Arguments: 
5        series: series. Time series that we want to decompose
6    Outputs: 
7        Decomposition plot in the console
8    """
9    result = seasonal_decompose(series, model='additive')
10    result.plot()
11    pyplot.show()
12#Execute in the main block
13#Convert the Date column into a date object
14electricity_df['Date']=pd.to_datetime(electricity_df['Date'])
15#Set Date as a Pandas DatetimeIndex
16electricity_df.index=pd.DatetimeIndex(electricity_df['Date'])
17#Decompose the time series into parts
18decompose_time_series(electricity_df['Electricity_Price'])

1#Transform the columns using natural log
2master_df['Electricity_Price_Transformed']=np.log(master_df['Electricity_Price'])
3master_df['Nat_Gas_Price_MCF_Transformed']=np.log(master_df['Nat_Gas_Price_MCF'])
4    
5#Difference the data by 1 month
6n=1
7master_df['Electricity_Price_Transformed_Differenced'] = master_df['Electricity_Price_Transformed'] - master_df['Electricity_Price_Transformed'].shift(n)
8master_df['Nat_Gas_Price_MCF_Transformed_Differenced'] = master_df['Nat_Gas_Price_MCF_Transformed'] - master_df['Nat_Gas_Price_MCF_Transformed'].shift(n)