TICKER_id   DXJ  EWA  EWC  EWG  EWI  EWP  EWQ  EWU  EWW  EWY  EWZ
DATE
2019-05-20 -1.0  1.5  0.0 -0.5  0.0  0.0  0.5  0.0 -0.5  0.0  0.0
2019-05-21 -1.0  1.5  0.5 -0.5  0.0  0.0  0.0  0.0 -0.5 -0.5  0.0
2019-05-22 -1.0  1.5  0.0 -1.0  0.0  0.0  0.0  0.0 -0.5 -0.5  0.0

[{'id': 00001, 'DATE': datetime.date(2019, 7, 8), 'TICKER_id': 'DXJ', 'FINAL_SCORE': -0.5, 'MODEL_SCORE': -0.5, 'G': 0.0, 'L': 0.0, 'R': 0.0, 'RV': -1.0, 'SN': -1.0, 'TC': -1.0, 'FN': 0.0}

scores = overall_scores.objects.filter(TICKER__in = countries, DATE = last_date).values()
    lag_scores_df = pd.DataFrame([x for x in overall_scores.objects.filter(TICKER__in = countries, DATE__gte = lag_date).values()])
    lag_scores_df = lag_scores_df.pivot_table(index = 'DATE', columns = 'TICKER_id', values = 'FINAL_SCORE')
    lag_scores_df.fillna(method= 'ffill', inplace = True)
    lag_scores_df.diff(21)