npe bugfix

ai/EstimateMarketTrendV20.py

@@ -4,12 +4,13 @@ from py_jftech import autowired
 
 from ai.data_access import DataAccess
 from ai.model_trainer import ModelTrainer
+from ai.noticer import upload_predict
 from ai.training_data_builder import TrainingDataBuilder
 from api import DataSync
 
 # 截止日期
-# max_date = None
-max_date = '2023-11-24'
+max_date = None
+# max_date = '2023-12-01'
 
 # 待预测指数
 # PREDICT_LIST = [67]
@@ -33,7 +34,8 @@ def predictionFromMoel(the_model, scaledX_forecast, predict_item):
         predictionStr = 'UP'
     content = f"""\n On day {forecastDay.strftime("%m/%d/%Y")}, the model predicts {predict_item} to be {predictionStr} in {str(numForecastDays)} business days. \n"""
     print(content)
-    # upload_predict(predict_item, forecastDay, predictionStr)
+    # if predict_item == 'SPX':
+    #     upload_predict(f'{predict_item} Index', forecastDay, predictionStr)
     # send(content)
     return prediction
 
@@ -41,7 +43,7 @@ def predictionFromMoel(the_model, scaledX_forecast, predict_item):
 ########################################
 if __name__ == '__main__':
     sync()
-    toForecast = True  # False means test, True means forecast
+    toForecast = False  # False means test, True means forecast
     # define some parameters
     win1W = 5  # 1 week
     win1M = 21  # 1 Month

ai/training_data_builder.py

@@ -122,6 +122,7 @@ class TrainingDataBuilder(ABC):
     def build_train_test(self, pid, indexData, vixData, indexOtherData, cpiData, FDTRData, NAPMPMIData):
         ###### Merge Data to one table
         predictData = self.build_predict_data(indexData, pid)
+        forecastDay = None
         if (self._toForecast):
             forecastDay = predictData['date'].iloc[-1]
         DataAll = pd.merge(predictData, vixData, how='outer', on='date')
@@ -177,7 +178,7 @@ class TrainingDataBuilder(ABC):
         # scaledX = scaler.fit_transform(X)
         DataScaler = scaler.fit(X)
         scaledX = DataScaler.transform(X)
-
+        scaledX_forecast = None
         if (self._toForecast):
             scaledX_forecast = DataScaler.transform(X_forecast)