SARIMAX prediction: including 'deterministic polynomial trend'

I'm wanting to use Statsmodel's SARIMAX model to predict sharemarket values into the future. This is part of a larger effort towards helping strictly non-profits and social-enterprise organisations. Question : I'm having an issue (maybe conceptual) where when I introduce a 'deterministic polynomial trend', using Statsmodels' implementation, I can't see any effects on the subsequent prediction (I would have thought the prediction would shift upwards if I introduced a larger constant, for example). Am I missing something fundemental? Typical code is below, but without source data (I can provide data if needed): # SARIMA forecasting model trendCoeffsList = [15000, 2.069764822521753, 0.0012385250036528476] def forecast_sarima(data, order, sOrder): try: model = SARIMAX(data, trend=trendCoeffsList, order=order, seasonal_order=sOrder, enforce_stationarity=False, enforce_invertibility=False) print(model.trend) # coefficients exist within model model_fit = model.fit(disp=False) start = 1 end = len(dataWhole) - dataRange predictData = model_fit.predict(start, end) except: predictData = False return predictData