Remove EMARSI param

Signed-off-by: Aadhitya A <[email protected]>

app.py

@@ -106,8 +106,8 @@ def objective(trial, X_train, y_train, X_test, y_test):
 def modelCNNLSTM(csv_file, prax):
     # Read the data
     df = csv_file
-    temp_data = df.iloc[0:3000, 1:24]
-    trek = df.iloc[3000:,1:24]
+    temp_data = df.iloc[0:3000, 1:23]
+    trek = df.iloc[3000:,1:23]
     #print(temp_data)
     data = temp_data
     sc = MinMaxScaler()
@@ -217,7 +217,7 @@ def modelCNNLSTM(csv_file, prax):
     #print(f'> Loss: {np.mean(loss_per_fold)}')
     #print('------------------------------------------------------------------------')
     
-    trek = df.iloc[0:len(df), 1:24]
+    trek = df.iloc[0:len(df), 1:23]
     Y = trek[0:len(trek)]
     YP = trek[1:len(trek)]
     Y1 = Y['Close']
@@ -474,7 +474,7 @@ def modelTFT(csv_file, prax):
         time_varying_known_reals=["time_idx"],
         time_varying_unknown_categoricals=[],
         time_varying_unknown_reals=[
-            'Open','High','Low','Close','OI','RSI14','RSI44','HHRSI','EMARSI','Rsi Weekly','LLCHHV','white','Vap44','Vap14','BV11','SV11','Ema5','Ema20','Ema50','Ema200'
+            'Open','High','Low','Close','OI','RSI14','RSI44','HHRSI','Rsi Weekly','LLCHHV','white','Vap44','Vap14','BV11','SV11','Ema5','Ema20','Ema50','Ema200'
         ],
         target_normalizer=GroupNormalizer(
             groups=['Ticker'], transformation="softplus"
@@ -507,11 +507,6 @@ def modelTFT(csv_file, prax):
 
     print(f"Median loss for naive prediction on validation: {sm.loss(actuals, baseline_predictions).mean(axis = 1).median().item()}")
 
-    """<a id ="4"></a><h3 style="background:#0554f2; border:0; border-radius: 4px; color:#f5f6f7">Training and Evaluation</h3>
-
-    I'm not particularly interested in performances, rather in showing how easy it is to use the library.
-    """
-
     early_stop_callback = EarlyStopping(monitor="train_loss", min_delta=1e-2, patience=PATIENCE, verbose=False, mode="min")
     lr_logger = LearningRateMonitor()  # log the learning rate
     logger = TensorBoardLogger("lightning_logs")  # logging results to a tensorboard
@@ -711,7 +706,7 @@ def modelTFT_OpenGap(csv_file, prax):
         time_varying_known_reals=["time_idx"],
         time_varying_unknown_categoricals=[],
         time_varying_unknown_reals=[
-            'Open','High','Low','Close','OI','RSI14','RSI44','HHRSI','EMARSI','Rsi Weekly','LLCHHV','white','Vap44','Vap14','BV11','SV11','Ema5','Ema20','Ema50','Ema200', 'O-C'
+            'Open','High','Low','Close','OI','RSI14','RSI44','HHRSI','Rsi Weekly','LLCHHV','white','Vap44','Vap14','BV11','SV11','Ema5','Ema20','Ema50','Ema200', 'O-C'
         ],
         target_normalizer=GroupNormalizer(
             groups=['Ticker'], transformation="softplus"
@@ -744,11 +739,6 @@ def modelTFT_OpenGap(csv_file, prax):
 
     print(f"Median loss for naive prediction on validation: {sm.loss(actuals, baseline_predictions).mean(axis = 1).median().item()}")
 
-    """<a id ="4"></a><h3 style="background:#0554f2; border:0; border-radius: 4px; color:#f5f6f7">Training and Evaluation</h3>
-
-    I'm not particularly interested in performances, rather in showing how easy it is to use the library.
-    """
-
     early_stop_callback = EarlyStopping(monitor="train_loss", min_delta=1e-2, patience=PATIENCE, verbose=False, mode="min")
     lr_logger = LearningRateMonitor()  # log the learning rate
     logger = TensorBoardLogger("lightning_logs")  # logging results to a tensorboard
@@ -911,7 +901,7 @@ def main(files):
         prax[0] = df['Ticker'][0]
         prax[1] = df['Close'][len(df)-1]
         print('------------------')
-        # df = df.drop(['Volume'], axis=1)
+        df = df.drop(['EMARSI'], axis=1)
         #df['Date/Time'] = pd.to_datetime(df['Date/Time'])
         for i in range(len(df)):
             x = guess_date(df['Date/Time'][i])