Repeated values in prediction with sequential model

Question

The problem I got is with the result, I get the same value in the 'future' field in all the rows as follows.

          open        high         low       close    adjclose    volume   upperband  middleband   lowerband      future
12  185.979996  191.669998  182.899994  184.149994  184.149994  49946000  206.918629  193.606000  180.293370  208.573837
13  189.669998  200.369995  184.899994  197.820007  197.820007  57032700  204.412283  192.804001  181.195718  208.573837
14  194.020004  201.279999  185.169998  185.470001  185.470001  50001100  203.453190  190.868002  178.282814  208.573837
15  185.410004  195.740005  183.910004  195.330002  195.330002  57204900  201.037422  190.130002  179.222582  208.573837

The following is my model creation method:

def deep_learning_model_creation(X_Train, layers = 2, optimizer="rmsprop", units=256,  bidirectional = False, dropout=0.2, loss="mean_absolute_error"):
    model = Sequential()
    for i in range(layers):
        if i == 0:
            model.add(LSTM(units=units,return_sequences=True,input_shape=(X_Train.shape[1], 1)))
        elif i == layers - 1:            
            model.add(LSTM(units=units,return_sequences=False))
        else:
             model.add(LSTM(units=units,return_sequences=True))
        model.add(Dropout(dropout))
    model.add(Dense(1, activation="linear"))
    model.compile(optimizer=optimizer,loss=loss)
    return model

The following is how I set the field to predict, then create and fit the model.

df = df.assign(future=df['adjclose'].shift(-1))
df.dropna(subset=['future'], how='all', inplace=True)
df = df.drop(columns=['date'])
X_Train, X_Test, Y_Train, Y_Test = train_test_split(df.drop(['future'], axis=1), df['future'], test_size=0.2, random_state=1, shuffle = False)
    
model = deep_learning_model_creation(X_Train, optimizer = "adam", loss = "huber_loss", dropout=0.4)

model.fit(
   X_Train,
   Y_Train,
   epochs=100,
   batch_size=5,
   validation_data=(X_Test, Y_Test)
)

predicted_stock_price = model.predict(X_Test)
X_Test['future'] = predicted_stock_price
print(X_Test)

This is the dataframe:

          open        high         low       close    adjclose    volume   upperband  middleband   lowerband      future
0   222.139999  223.199997  216.600006  219.169998  219.169998  57520700  266.023308  239.164001  212.304695  215.039993
1   225.369995  227.250000  212.820007  215.039993  215.039993  66225800  253.735079  230.310001  206.884922  222.029999
2   217.279999  225.000000  214.550003  222.029999  222.029999  51694300  245.251131  225.901999  206.552867  212.580002
3   225.050003  227.770004  211.630005  212.580002  212.580002  56822500  232.953724  220.001999  207.050274  217.830002
4   212.000000  220.880005  210.789993  217.830002  217.830002  52570100  223.874138  217.329999  210.785860  221.979996
5   217.199997  223.729996  213.139999  221.979996  221.979996  51278100  225.385454  217.891998  210.398542  214.820007
6   225.169998  226.699997  212.000000  214.820007  214.820007  46897400  225.410190  217.848001  210.285812  201.830002
7   217.089996  223.919998  200.000000  201.830002  201.830002  65620900  227.342036  213.808002  200.273968  195.149994
8   203.029999  204.860001  195.000000  195.149994  195.149994  62471300  230.616134  210.322000  190.027866  199.020004
9   192.020004  199.449997  190.960007  199.020004  199.020004  64156600  226.865993  206.560001  186.254008  187.880005
10  197.179993  197.880005  186.699997  187.880005  187.880005  65314300  217.494343  199.740002  181.985662  184.149994
11  185.979996  191.669998  182.899994  184.149994  184.149994  49946000  206.918629  193.606000  180.293370  197.820007
12  189.669998  200.369995  184.899994  197.820007  197.820007  57032700  204.412283  192.804001  181.195718  185.470001
13  194.020004  201.279999  185.169998  185.470001  185.470001  50001100  203.453190  190.868002  178.282814  195.330002
14  185.410004  195.740005  183.910004  195.330002  195.330002  57204900  201.037422  190.130002  179.222582  196.020004
15  194.000000  198.250000  191.330002  196.020004  196.020004  47575100  203.250347  191.758002  180.265657  203.339996

EDIT

As @elbe suggested me scaling values before training and prediction makes the values not the same anymore:

        open      high       low     close  adjclose    volume  upperband  middleband  lowerband     future
12  0.215466  0.266205  0.071722  0.034847  0.034847  0.160577   0.045842    0.018367   0.000000  42.391476
13  0.000000  0.112743  0.031912  0.295143  0.295143  0.533283   0.000000    0.000000   0.028913  42.391270
14  0.214965  0.182271  0.266351  0.313358  0.313358  0.035062   0.041993    0.040518   0.061005  42.391472
15  0.345846  0.341551  0.145656  0.506600  0.506600  0.930656   0.116540    0.136038   0.176303  42.391541