Keras after test prediction values steadily decreases and goes down - machine-learning

Question

Issue:
I'm trying to predict the future stock price of Google using the LSTM model in Keras. I'm able to train the model successfully and the test prediction also goes well, but the after test/future prediction is bad. It forms a steadily decreasing curve which is not an actual future data.
Some Explanation
I'm training the model with two inputs and expecting a single output from it.
# Feature Scaling
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range = (0, 1))
training_set_scaled = sc.fit_transform(training_set)
# Creating a data structure with 60 timesteps and 1 output
X_train = []
y_train = []
for i in range(2, 999):
X_train.append(training_set_scaled[i-2:i, 0])
y_train.append(training_set_scaled[i, 0])
X_train, y_train = np.array(X_train), np.array(y_train)
# Reshaping
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
# Part 2 - Building the RNN
# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
# Initialising the RNN
regressor = Sequential()
# Adding the first LSTM layer and some Dropout regularisation
regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))
regressor.add(Dropout(0.2))
# Adding a second LSTM layer and some Dropout regularisation
regressor.add(LSTM(units = 50, return_sequences = True))
regressor.add(Dropout(0.2))
# Adding a third LSTM layer and some Dropout regularisation
regressor.add(LSTM(units = 50, return_sequences = True))
regressor.add(Dropout(0.2))
# Adding a fourth LSTM layer and some Dropout regularisation
regressor.add(LSTM(units = 50))
regressor.add(Dropout(0.2))
# Adding the output layer
regressor.add(Dense(units = 1))
# Compiling the RNN
regressor.compile(optimizer = 'rmsprop', loss = 'mean_squared_error')
# Fitting the RNN to the Training set
regressor.fit(X_train, y_train, epochs = 500, batch_size = 50)
Testing the predicted model
dataset_test = pd.read_csv('/media/vinothubuntu/Ubuntu Storage/Downloads/Test - Test.csv')
real_stock_price = dataset_test.iloc[:, 2:3].values
# Getting the predicted stock price of 2017
dataset_total = pd.concat((dataset_train['data'], dataset_test['data']), axis = 0)
inputs = dataset_total[len(dataset_total) - len(dataset_test) -0:].values
inputs = inputs.reshape(-1,1)
inputs = sc.transform(inputs)
X_test = []
test_var = []
for i in range(0, 28):
X_test.append(inputs[i:i+2, 0])
test_var.append(inputs[i, 0])
X_test_pred = np.array(X_test)
X_test_pred = np.reshape(X_test_pred, (X_test_pred.shape[0], X_test_pred.shape[1], 1))
predicted_stock_price = regressor.predict(X_test_pred)
This part goes very well, the test prediction give a perfect result.
After test/future prediction:
for x in range(0,30):
X_test_length = X_test[len(X_test)-1] # get the last array of X_test list
future=[]
Prev_4 = X_test_length[1:2] # get the last four value of the X_test_length
Last_pred = predicted_stock_price.flat[-1] # get the last value from prediction
merger = np.append(Prev_4,Last_pred)
X_test.append(merger) #append the new array to X_test
future.append(merger) #append the new array to future array
one_time_pred=np.array(future)
one_time_pred = np.reshape(one_time_pred, (one_time_pred.shape[0], one_time_pred.shape[1], 1))
future_prediction = regressor.predict(one_time_pred) #predict future - gives one new prediction
predicted_stock_price = np.append(predicted_stock_price, future_prediction, axis=0) #put the new predicction on predicted_stock_price array
Here comes the actual problem, I'm getting the last value from the test prediction and predicting a single output and creating a loop on the new precited value. [Please suggest me a better way, if you feel this is a bad idea]
My output:
Expected Result: Actual future data, which is definitely not a decreasing curve.