def main(cp, conn, cursor):
sql3 = (
"""SELECT neues_kursziel, zieldatum, analyst, avg FROM analyst_avg_2 WHERE unternehmen = %d AND neues_kursziel >0 AND avg_datum<(SELECT CURDATE()) ORDER BY avg_datum, zieldatum """
% (cp)
)
sql = "SELECT close , `datum` FROM kursdaten WHERE unternehmen =%d ORDER BY `datum" % (cp)
sql4 = """SELECT neues_kursziel, zieldatum, analyst FROM prognose
WHERE unternehmen = %d
AND `zieldatum`>(SELECT CURDATE()) AND neues_kursziel >0
ORDER BY zieldatum""" % (
cp
)
avg_kurse = calculate_data.get_select(sql, cursor, conn)
ziel_kurse = calculate_data.get_select(sql3, cursor, conn)
prognose = calculate_data.get_select(sql4, cursor, conn)
avg = [q[0] for q in avg_kurse]
datum_avg = [q[1] for q in avg_kurse]
datum_avg = dates.date2num(datum_avg)
datum_ziel = [q[1] for q in ziel_kurse]
datum_ziel = dates.date2num(datum_ziel)
datum_prognose = [q[1] for q in prognose]
datum_prognose = dates.date2num(datum_prognose)
analysten_dict = calculate_data.get_analysten_dict(ziel_kurse)
analysten_prognosen_dict = calculate_data.get_analysten_prognosen_dict(prognose)
predictions_dict = calculate_data.get_prediction_dictionary(analysten_dict, analysten_prognosen_dict)
predictions_and_dates_list = calculate_data.get_predictions_and_dates(predictions_dict)
mittelwert = calculate_data.get_mittelwert(predictions_and_dates_list)
Varianz = calculate_data.get_varianz(mittelwert, [q[1] for q in predictions_and_dates_list])
sigma = np.sqrt(Varianz)
data_plot_future = calculate_data.get_data_for_plot_future(analysten_dict, analysten_prognosen_dict)
data_plot_own_forecast_ponts = calculate_data.get_data_for_plot_own_forecast_points()
# fig = figure()
# ax = fig.add_subplot(111)
# plot.plot_avg(datum_avg,avg,ax,fig)
# plot.plot_future([q[0] for q in data_plot_future], [q[1] for q in data_plot_future],'yellow',ax,fig)
# plot.plot_own_forecast_line([q[1] for q in predictions_and_dates_list], [q[0] for q in predictions_and_dates_list],sigma,ax,fig)
# plot.plot_own_forecast_points([q[0] for q in data_plot_own_forecast_ponts],[q[1] for q in data_plot_own_forecast_ponts],ax,fig)
# plot.show_plot(ax,fig)
konfidenz_intervall_95_sigma_oben = [
[(q[1] + 1.9600 * sigma) for q in predictions_and_dates_list],
[q[0] for q in predictions_and_dates_list],
]
konfidenz_intervall_95_sigma_unten = [
[(q[1] - 1.9600 * sigma) for q in predictions_and_dates_list],
[q[0] for q in predictions_and_dates_list],
]
tats_kurse_datum = [datum_avg, avg]
prognosekurse_analysten_datum = [q[0] for q in data_plot_future], [q[1] for q in data_plot_future]
unsere_vorhersage_linie = [q[1] for q in predictions_and_dates_list], [q[0] for q in predictions_and_dates_list]
unsere_vorhersage_punkte = [
[q[0] for q in data_plot_own_forecast_ponts],
[q[1] for q in data_plot_own_forecast_ponts],
]
result_set = []
result_set.append(konfidenz_intervall_95_sigma_oben)
result_set.append(konfidenz_intervall_95_sigma_unten)
result_set.append(tats_kurse_datum)
result_set.append(prognosekurse_analysten_datum)
result_set.append(unsere_vorhersage_linie)
result_set.append(unsere_vorhersage_punkte)
result_set.append(sigma)
calculate_data.reset_global_variables()
calculate_data.initialize_global_variables()
return result_set
def main(cp):
sql3 = """SELECT `new_price`, `target_date`, `analyst_id`, `avg`
FROM analyst_avg_2
WHERE `company_id`= %d
AND `new_price`>0
AND `avg_date`<(SELECT CURDATE())
ORDER BY `avg_date`, `target_date`"""%(cp)
sql = """SELECT `closing_price`, `date`
FROM secop_quote
WHERE `company_id`=%d
ORDER BY `date`"""%(cp)
sql4 = """SELECT `new_price`, `target_date`, `analyst_id`
FROM secop_prediction
WHERE `company_id`= %d
AND `target_date`>(SELECT CURDATE())
AND `new_price`>0
ORDER BY `target_date`"""%(cp)
avg_kurse = connector.get_select(sql)
ziel_kurse = connector.get_select(sql3)
prognose = connector.get_select(sql4)
avg = [q[0] for q in avg_kurse]
datum_avg = [q[1] for q in avg_kurse]
analysten_dict = calculate_data.get_analysten_dict(ziel_kurse)
analysten_prognosen_dict = calculate_data.get_analysten_prognosen_dict(prognose)
predictions_dict = calculate_data.get_prediction_dictionary(analysten_dict,analysten_prognosen_dict)
predictions_and_dates_list = calculate_data.get_predictions_and_dates(predictions_dict)
mittelwert = calculate_data.get_mittelwert(predictions_and_dates_list)
Varianz = calculate_data.get_varianz(mittelwert,[q[1] for q in predictions_and_dates_list])
sigma = np.sqrt(Varianz)
data_plot_future = calculate_data.get_data_for_plot_future(analysten_dict,analysten_prognosen_dict)
data_plot_own_forecast_ponts = calculate_data.get_data_for_plot_own_forecast_points()
konfidenz_intervall_95_sigma_oben = [[(q[1]+ 1.9600 * sigma) for q in predictions_and_dates_list], [q[0] for q in predictions_and_dates_list]]
konfidenz_intervall_95_sigma_unten = [[(q[1]- 1.9600 * sigma) for q in predictions_and_dates_list], [q[0] for q in predictions_and_dates_list]]
tats_kurse_datum = [datum_avg,avg]
prognosekurse_analysten_datum = [q[0] for q in data_plot_future], [q[1] for q in data_plot_future]
unsere_vorhersage_linie = [q[1] for q in predictions_and_dates_list], [q[0] for q in predictions_and_dates_list]
unsere_vorhersage_punkte = [[q[0] for q in data_plot_own_forecast_ponts],[q[1] for q in data_plot_own_forecast_ponts]]
result_set = []
result_set.append(konfidenz_intervall_95_sigma_oben)
result_set.append(konfidenz_intervall_95_sigma_unten)
result_set.append(tats_kurse_datum)
result_set.append(prognosekurse_analysten_datum)
result_set.append(unsere_vorhersage_linie)
result_set.append(unsere_vorhersage_punkte)
result_set.append(sigma)
calculate_data.reset_global_variables()
calculate_data.initialize_global_variables()
return result_set
def main(cp):
sql_training = """SELECT `new_price`, `target_date`,`avg`
FROM `analyst_avg_2`
WHERE `company_id` = %d
AND `avg_date` > '2010-01-01'
AND `avg_date` < '2012-03-01'"""%(cp)
sql_testing = """SELECT `new_price`, `target_date`, `analyst_id`, `avg`
FROM `analyst_avg_2`
WHERE `company_id` = %d
AND `avg_date` > '2012-03-01'
AND `avg_date` < (SELECT CURDATE())"""%(cp)
sql = """SELECT `closing_price`, `date`
FROM secop_quote
WHERE `company_id`=%d
ORDER BY `date`"""%(cp)
sql_prognose = """SELECT `new_price`, `target_date`
FROM `secop_prediction`
WHERE `company_id` = %d
AND `target_date` > (SELECT CURDATE())
AND `new_price` > 0"""%(cp)
#fig = figure()
#ax = fig.add_subplot(111)
avg_kurse = connector.get_select(sql)
kurse_training = connector.get_select(sql_training)
kurse_testing = connector.get_select(sql_testing)
prognose = connector.get_select(sql_prognose)
avg = [q[0] for q in avg_kurse]
datum_avg = [q[1] for q in avg_kurse]
#datum_avg =dates.date2num(datum_avg)
trainya = calculate_data.train_machine(kurse_training)
testinya = calculate_data.get_testing_data(kurse_testing)
###################################################
#predict training and testing data --> consistency, validity
##################################################
consistency = calculate_data.predict_own(trainya)
testing_check = calculate_data.predict_own(testinya)
sigma_training = calculate_data.get_sigma(consistency,[q[1] for q in calculate_data.training_predictions_and_dates_list])
#print sigma_training
#konsitenz = [consistency, [q[0] for q in calculate_data.training_predictions_and_dates_list]]
konsistenz_sigma = sigma_training
sigma_testing = calculate_data.get_sigma(testing_check,[q[1] for q in calculate_data.testing_predictions_and_dates_list])
#print sigma_testing
#plot.plot_own_forecast_line_2(testing_check, [q[0] for q in calculate_data.testing_predictions_and_dates_list],sigma_testing,'yellow',ax,fig)
validity = [testing_check, [q[0] for q in calculate_data.testing_predictions_and_dates_list]]
validity_sigma = sigma_testing
################################# predict future
prognose_kurs = calculate_data.get_future_data(prognose)
predictions = calculate_data.predict_own(prognose_kurs)
mittelwert = calculate_data.get_mittelwert_2(predictions)
Varianz_prog = calculate_data.get_varianz(mittelwert,[q[1] for q in calculate_data.forecast_predictions_and_dates_list])
sigma_prog = np.sqrt(Varianz_prog)
#print sigma_prog
#plot.plot_own_forecast_line_2(predictions, [q[0] for q in calculate_data.forecast_predictions_and_dates_list],sigma_prog,'red',ax,fig)
prognosis = [predictions, [q[0] for q in calculate_data.forecast_predictions_and_dates_list]]
prognosis_sigma = sigma_prog
#plot analysten prognosen zum vergleich
#plot.plot_future([q[1] for q in calculate_data.forecast_predictions_and_dates_list],[q[0] for q in calculate_data.forecast_predictions_and_dates_list],'yellow',ax,fig)
#plot.plot_avg(datum_avg,avg,ax,fig)
#plot.show_plot(ax,fig)
#training_konfidenz_intervall_95_sigma_oben = [[(q+ 1.9600 * konsistenz_sigma) for q in consistency], [dates.num2date(q[0]) for q in calculate_data.training_predictions_and_dates_list]]
training_konfidenz_intervall_95_sigma_oben = [[(q+ 1.9600 * konsistenz_sigma) for q in consistency], [q[0] for q in calculate_data.training_predictions_and_dates_list]]
#training_konfidenz_intervall_95_sigma_unten = [[(q- 1.9600 * konsistenz_sigma) for q in consistency], [dates.num2date(q[0]) for q in calculate_data.training_predictions_and_dates_list]]
training_konfidenz_intervall_95_sigma_unten = [[(q- 1.9600 * konsistenz_sigma) for q in consistency], [q[0] for q in calculate_data.training_predictions_and_dates_list]]
#testing_konfidenz_intervall_95_sigma_oben = [[(q+ 1.9600 * validity_sigma) for q in validity], [dates.num2date(q[0]) for q in calculate_data.testing_predictions_and_dates_list]]
testing_konfidenz_intervall_95_sigma_oben = [[(q+ 1.9600 * validity_sigma) for q in validity], [q[0] for q in calculate_data.testing_predictions_and_dates_list]]
#testing_konfidenz_intervall_95_sigma_unten = [[(q- 1.9600 * validity_sigma) for q in validity], [dates.num2date(q[0]) for q in calculate_data.testing_predictions_and_dates_list]]
testing_konfidenz_intervall_95_sigma_unten = [[(q- 1.9600 * validity_sigma) for q in validity], [q[0] for q in calculate_data.testing_predictions_and_dates_list]]
#prognose_konfidenz_intervall_95_sigma_oben = [[(q+ 1.9600 * prognosis_sigma) for q in prognosis], [dates.num2date(q[0]) for q in calculate_data.forecast_predictions_and_dates_list]]
prognose_konfidenz_intervall_95_sigma_oben = [[(q+ 1.9600 * prognosis_sigma) for q in prognosis], [q[0] for q in calculate_data.forecast_predictions_and_dates_list]]
#prognose_konfidenz_intervall_95_sigma_unten = [[(q- 1.9600 * prognosis_sigma) for q in prognosis], [dates.num2date(q[0]) for q in calculate_data.forecast_predictions_and_dates_list]]
prognose_konfidenz_intervall_95_sigma_unten = [[(q- 1.9600 * prognosis_sigma) for q in prognosis], [q[0] for q in calculate_data.forecast_predictions_and_dates_list]]
#tats_kurse_datum = [dates.num2date(datum_avg),avg]
tats_kurse_datum = [datum_avg,avg]
#prognosekurse_analysten_datum = [[q[1] for q in calculate_data.forecast_predictions_and_dates_list],[dates.num2date(q[0]) for q in calculate_data.forecast_predictions_and_dates_list]]
prognosekurse_analysten_datum = [[q[1] for q in calculate_data.forecast_predictions_and_dates_list],[q[0] for q in calculate_data.forecast_predictions_and_dates_list]]
#training_unsere_vorhersage_linie = [consistency, [dates.num2date(q[0]) for q in calculate_data.training_predictions_and_dates_list]]
training_unsere_vorhersage_linie = [consistency, [q[0] for q in calculate_data.training_predictions_and_dates_list]]
#testing_unsere_vorhersage_linie = [testing_check, [dates.num2date(q[0]) for q in calculate_data.testing_predictions_and_dates_list]]
testing_unsere_vorhersage_linie = [testing_check, [q[0] for q in calculate_data.testing_predictions_and_dates_list]]
#prognose_unsere_vorhersage_linie = [predictions, [dates.num2date(q[0]) for q in calculate_data.forecast_predictions_and_dates_list]]
prognose_unsere_vorhersage_linie = [predictions, [q[0] for q in calculate_data.forecast_predictions_and_dates_list]]
result_set = []
result_set.append(training_konfidenz_intervall_95_sigma_oben)
result_set.append(training_konfidenz_intervall_95_sigma_unten)
result_set.append(testing_konfidenz_intervall_95_sigma_oben)
result_set.append(testing_konfidenz_intervall_95_sigma_unten)
result_set.append(prognose_konfidenz_intervall_95_sigma_oben)
result_set.append(prognose_konfidenz_intervall_95_sigma_unten)
result_set.append(tats_kurse_datum)
result_set.append(prognosekurse_analysten_datum)
result_set.append(training_unsere_vorhersage_linie)
result_set.append(testing_unsere_vorhersage_linie)
result_set.append(prognose_unsere_vorhersage_linie)
result_set.append(konsistenz_sigma)
result_set.append(validity_sigma)
result_set.append(prognosis_sigma)
calculate_data.reset_global_variables()
calculate_data.initialize_global_variables()
return result_set
def main(cp,conn,cursor):
sql_training = """SELECT neues_kursziel, zieldatum,avg FROM analyst_avg_2 WHERE unternehmen = %d AND avg_datum>'2010-01-01'AND avg_datum<'2012-03-01'"""%(cp)
sql_testing = """SELECT neues_kursziel, zieldatum, analyst, avg FROM analyst_avg_2 WHERE unternehmen = %d AND avg_datum>'2012-03-01' AND avg_datum<(SELECT CURDATE())"""%(cp)
sql = "SELECT close , `datum` FROM kursdaten WHERE unternehmen =%d ORDER BY `datum"%(cp)
sql_prognose = """SELECT neues_kursziel, zieldatum FROM prognose
WHERE unternehmen = %d
AND `zieldatum`>(SELECT CURDATE()) AND neues_kursziel >0
"""%(cp)
#fig = figure()
#ax = fig.add_subplot(111)
avg_kurse = calculate_data.get_select(sql,cursor,conn)
kurse_training = calculate_data.get_select(sql_training,cursor,conn)
kurse_testing = calculate_data.get_select(sql_testing,cursor,conn)
prognose = calculate_data.get_select(sql_prognose,cursor,conn)
avg = [q[0] for q in avg_kurse]
datum_avg = [q[1] for q in avg_kurse]
datum_avg =dates.date2num(datum_avg)
trainya = calculate_data.train_machine(kurse_training)
testinya = calculate_data.get_testing_data(kurse_testing)
###################################################
#predict training and testing data --> consistency, validity
##################################################
consistency = calculate_data.predict_own(trainya)
testing_check = calculate_data.predict_own(testinya)
sigma_training = calculate_data.get_sigma(consistency,[q[1] for q in calculate_data.training_predictions_and_dates_list])
#print sigma_training
#plot.plot_own_forecast_line_2(consistency, [q[0] for q in calculate_data.training_predictions_and_dates_list],sigma_training,'green',ax,fig)
konsitenz = [consistency, [q[0] for q in calculate_data.training_predictions_and_dates_list]]
konsistenz_sigma = sigma_training
sigma_testing = calculate_data.get_sigma(testing_check,[q[1] for q in calculate_data.testing_predictions_and_dates_list])
#print sigma_testing
#plot.plot_own_forecast_line_2(testing_check, [q[0] for q in calculate_data.testing_predictions_and_dates_list],sigma_testing,'yellow',ax,fig)
validity = [testing_check, [q[0] for q in calculate_data.testing_predictions_and_dates_list]]
validity_sigma = sigma_testing
################################# predict future
prognose_kurs = calculate_data.get_future_data(prognose)
predictions = calculate_data.predict_own(prognose_kurs)
mittelwert = calculate_data.get_mittelwert_2(predictions)
Varianz_prog = calculate_data.get_varianz(mittelwert,[q[1] for q in calculate_data.forecast_predictions_and_dates_list])
sigma_prog = np.sqrt(Varianz_prog)
#print sigma_prog
#plot.plot_own_forecast_line_2(predictions, [q[0] for q in calculate_data.forecast_predictions_and_dates_list],sigma_prog,'red',ax,fig)
prognosis = [predictions, [q[0] for q in calculate_data.forecast_predictions_and_dates_list]]
prognosis_sigma = sigma_prog
#plot analysten prognosen zum vergleich
#plot.plot_future([q[1] for q in calculate_data.forecast_predictions_and_dates_list],[q[0] for q in calculate_data.forecast_predictions_and_dates_list],'yellow',ax,fig)
#plot.plot_avg(datum_avg,avg,ax,fig)
#plot.show_plot(ax,fig)
training_konfidenz_intervall_95_sigma_oben = [[(q+ 1.9600 * konsistenz_sigma) for q in consistency], [q[0] for q in calculate_data.training_predictions_and_dates_list]]
training_konfidenz_intervall_95_sigma_unten = [[(q- 1.9600 * konsistenz_sigma) for q in consistency], [q[0] for q in calculate_data.training_predictions_and_dates_list]]
testing_konfidenz_intervall_95_sigma_oben = [[(q+ 1.9600 * validity_sigma) for q in validity], [q[0] for q in calculate_data.testing_predictions_and_dates_list]]
testing_konfidenz_intervall_95_sigma_unten = [[(q- 1.9600 * validity_sigma) for q in validity], [q[0] for q in calculate_data.testing_predictions_and_dates_list]]
prognose_konfidenz_intervall_95_sigma_oben = [[(q+ 1.9600 * prognosis_sigma) for q in prognosis], [q[0] for q in calculate_data.forecast_predictions_and_dates_list]]
prognose_konfidenz_intervall_95_sigma_unten = [[(q- 1.9600 * prognosis_sigma) for q in prognosis], [q[0] for q in calculate_data.forecast_predictions_and_dates_list]]
tats_kurse_datum = [datum_avg,avg]
prognosekurse_analysten_datum = [[q[1] for q in calculate_data.forecast_predictions_and_dates_list],[q[0] for q in calculate_data.forecast_predictions_and_dates_list]]
training_unsere_vorhersage_linie = [consistency, [q[0] for q in calculate_data.training_predictions_and_dates_list]]
testing_unsere_vorhersage_linie = [testing_check, [q[0] for q in calculate_data.testing_predictions_and_dates_list]]
prognose_unsere_vorhersage_linie = [predictions, [q[0] for q in calculate_data.forecast_predictions_and_dates_list]]
result_set = []
result_set.append(training_konfidenz_intervall_95_sigma_oben)
result_set.append(training_konfidenz_intervall_95_sigma_unten)
result_set.append(testing_konfidenz_intervall_95_sigma_oben)
result_set.append(testing_konfidenz_intervall_95_sigma_unten)
result_set.append(prognose_konfidenz_intervall_95_sigma_oben)
result_set.append(prognose_konfidenz_intervall_95_sigma_unten)
result_set.append(tats_kurse_datum)
result_set.append(prognosekurse_analysten_datum)
result_set.append(training_unsere_vorhersage_linie)
result_set.append(testing_unsere_vorhersage_linie)
result_set.append(prognose_unsere_vorhersage_linie)
result_set.append(konsistenz_sigma)
result_set.append(validity_sigma)
result_set.append(prognosis_sigma)
calculate_data.reset_global_variables()
calculate_data.initialize_global_variables()
return result_set
def main(cp):
sql3 = """SELECT `new_price`, `target_date`, `analyst_id`, `avg`
FROM `analyst_avg_2`
WHERE `company_id` = %d
AND `new_price` >0
AND `avg_date`<'2012-01-01'
ORDER BY `avg_date`, `target_date` """%(cp)
sql = """SELECT `closing_price`, `date`
FROM secop_quote
WHERE `company_id`=%d
ORDER BY `date`"""%(cp)
sql4 = """SELECT `new_price`, `target_date`, `analyst_id`
FROM secop_prediction
WHERE `company_id`= %d
AND `target_date`>'2012-01-01'
AND `target_date`<(SELECT CURDATE())
AND `new_price`>0
ORDER BY `target_date`"""%(cp)
avg_kurse = connector.get_select(sql)
ziel_kurse = connector.get_select(sql3)
prognose = connector.get_select(sql4)
avg = [q[0] for q in avg_kurse]
datum_avg = [q[1] for q in avg_kurse]
#datum_avg =dates.date2num(datum_avg)
#datum_ziel = [q[1] for q in ziel_kurse]
#datum_ziel =dates.date2num(datum_ziel)
#datum_prognose = [q[1] for q in prognose]
#datum_prognose = dates.date2num(datum_prognose)
#analysten_list = [q[2] for q in ziel_kurse]
analysten_dict = calculate_data.get_analysten_dict(ziel_kurse)
analysten_prognosen_dict = calculate_data.get_analysten_prognosen_dict(prognose)
predictions_dict = calculate_data.get_prediction_dictionary(analysten_dict,analysten_prognosen_dict)
predictions_and_dates_list = calculate_data.get_predictions_and_dates(predictions_dict)
mittelwert = calculate_data.get_mittelwert(predictions_and_dates_list)
Varianz = calculate_data.get_varianz(mittelwert,[q[1] for q in predictions_and_dates_list])
standardabweichung = np.sqrt(Varianz)
data_plot_future = calculate_data.get_data_for_plot_future(analysten_dict,analysten_prognosen_dict)
data_plot_own_forecast_ponts = calculate_data.get_data_for_plot_own_forecast_points()
# fig = figure()
# ax = fig.add_subplot(111)
# plot.plot_avg(datum_avg,avg,ax,fig)
# plot.plot_future([q[0] for q in data_plot_future], [q[1] for q in data_plot_future],'yellow',ax,fig)
# plot.plot_own_forecast_line([q[1] for q in predictions_and_dates_list], [q[0] for q in predictions_and_dates_list],standardabweichung,ax,fig)
# plot.plot_own_forecast_points([q[0] for q in data_plot_own_forecast_ponts],[q[1] for q in data_plot_own_forecast_ponts],ax,fig)
# plot.show_plot(ax,fig)
#konfidenz_intervall_95_sigma_oben = [[(q[1]+ 1.9600 * standardabweichung) for q in predictions_and_dates_list], [dates.num2date(q[0]) for q in predictions_and_dates_list]]
konfidenz_intervall_95_sigma_oben = [[(q[1]+ 1.9600 * standardabweichung) for q in predictions_and_dates_list], [q[0] for q in predictions_and_dates_list]]
#konfidenz_intervall_95_sigma_unten = [[(q[1]- 1.9600 * standardabweichung) for q in predictions_and_dates_list], [dates.num2date(q[0]) for q in predictions_and_dates_list]]
konfidenz_intervall_95_sigma_unten = [[(q[1]- 1.9600 * standardabweichung) for q in predictions_and_dates_list], [q[0] for q in predictions_and_dates_list]]
#tats_kurse_datum = [dates.num2date(datum_avg),avg]
tats_kurse_datum = [datum_avg,avg]
#prognosekurse_analysten_datum = [q[0] for q in data_plot_future], [dates.num2date(q[1]) for q in data_plot_future]
prognosekurse_analysten_datum = [q[0] for q in data_plot_future], [q[1] for q in data_plot_future]
#unsere_vorhersage_linie = [q[1] for q in predictions_and_dates_list], [dates.num2date(q[0]) for q in predictions_and_dates_list]
unsere_vorhersage_linie = [q[1] for q in predictions_and_dates_list], [q[0] for q in predictions_and_dates_list]
#unsere_vorhersage_punkte = [[q[0] for q in data_plot_own_forecast_ponts],[dates.num2date(q[1]) for q in data_plot_own_forecast_ponts]]
unsere_vorhersage_punkte = [[q[0] for q in data_plot_own_forecast_ponts],[q[1] for q in data_plot_own_forecast_ponts]]
standardfehler = calculate_data.get_data_test_MSE(data_plot_own_forecast_ponts,avg_kurse)
standardfehler = calculate_data.get_sigma([q[0] for q in standardfehler],[q[1] for q in standardfehler])
print standardfehler
result_set = []
result_set.append(konfidenz_intervall_95_sigma_oben)
result_set.append(konfidenz_intervall_95_sigma_unten)
result_set.append(tats_kurse_datum)
result_set.append(prognosekurse_analysten_datum)
result_set.append(unsere_vorhersage_linie)
result_set.append(unsere_vorhersage_punkte)
result_set.append(standardabweichung)
result_set.append(standardfehler)
calculate_data.reset_global_variables()
return result_set