running_fbp.py

def runningFBP(ticker, window=730, ma_period=5, days_to_train=365, forecast_period=10):
    """
    Runs the facebook prophet model over the provided ticker.  Trains with last N days given 
    by days_to_train.  Forecast N days into the future based on given forecast_period.  Moving average 
    is applied to the dataset based on given ma_period. Returns the root mean squared error and a DF 
    of the actual values and the predicted values for the same day.
    """

    # Getting and Formatting Data
    df = getData(ticker, window=window, ma_period=ma_period)
    
    # DF for the predicted values
    pred_df = pd.DataFrame()

    # Running the model on each day
    for i in tqdm(range(days_to_train, window-forecast_period, forecast_period)):

        # Training and Predicting the last day on the forecast
        forecast = fbpTrainPredict(df[i-days_to_train:i], 
                                   forecast_period=forecast_period).tail(forecast_period)[['ds',
                                                                                           'yhat',
                                                                                           'yhat_lower',
                                                                                           'yhat_upper']]

        # Adding the last day predicted
        pred_df = pred_df.append(forecast, ignore_index=True)
        
    # Combining the predicted df and original df
    comb_df = df[['ds', 'Open']].merge(pred_df, 
                                       on='ds', 
                                       how='outer').sort_values(by='ds')
    
    # Setting the index to the dates
    comb_df.set_index('ds', inplace=True)

    return comb_df