fbp_news_running_fbp.py

def runningFBP(prices, forecast_period, training_days):
    """
    Runs Facebook Prophet to get predictions over a set period 
    of time. Uses FBP to train and predict every N days and gets the 
    price forecasts.
    """
    # DF for the predicted values
    pred_df = pd.DataFrame()

    # Running the model each day
    for i in tqdm(range(training_days, len(prices))):

        # Training and Predicting the last day on the forecast
        forecast = fbpTrainPredict(
            prices[i-training_days:i], 
            forecast_period
        ).tail(1)[[
            'ds',
            'yhat',
            'yhat_lower',
            'yhat_upper'
        ]]
        
        # Adding the forecast predicted
        pred_df = pred_df.append(forecast, ignore_index=True)
        
    # Prepping for merge by converting date values to be the same type
    pred_df['ds'] = pred_df['ds'].apply(lambda x: str(x)[:10])

    prices['ds'] = prices['ds'].apply(lambda x: str(x)[:10])
    
    # Shifting the forecasts back in order to compare it to the 'current' open values
    pred_df[['yhat', 'yhat_lower', 'yhat_upper']] = pred_df[['yhat', 'yhat_lower', 'yhat_upper']].shift(-forecast_period)
    
    # Merging with the prices DF in order to compare values for positions later
    merge_df = prices[['ds', 'open']].merge(
        pred_df,
        on='ds',
        how='outer'
    ).dropna().set_index('ds')

    return merge_df