Keras: val_loss & val_accuracy are not changing

I am trying to build an LSTM model to predict whether a stock is going up or down the next day. As you can see, a simple classification task that got me stuck for a couple of days now. I am selecting 3 features only to feed into my network, below I am showing my pre-processing:

# pre-processing, last column has values of either 1 or zero
len(df.columns) # 32 columns
index_ = len(df.columns) - 1
x = df.iloc[:,:index_]
y = df.iloc[:,index_:].values.astype(int)

Removing any nan values:

def clean_dataset(df):
    assert isinstance(df, pd.DataFrame), "df needs to be a pd.DataFrame"
    df.dropna(inplace=True)
    indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf, 'NaN', 'nan']).any(1)
    return df[indices_to_keep].astype(np.float64)

df = clean_dataset(df)

Then I am taking the 3 selected features and showing the shape for X and Y

selected_features = ['feature1', 'feature2', 'feature3']
x = x[selected_features].values.astype(float)
# s.shape (44930, 3)
# y.shape (44930, 1)

Then I am splitting my dataset into 80/20

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=98 )

Here I am reshaping my data

x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], 1) 
x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], 1) 
y_train = y_train.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)

Here is the new shape of each one:

x_train.shape = (35944, 3, 1)
x_test.shape = (8986, 3, 1)
y_train.shape = (35944, 1)
y_test.shape = (8986, 1)

First sample of the x_train set Before reshaping

x_train[0] => array([8.05977145e-01, 4.92200000e+01, 1.23157152e+08])

First sample of the x_train set After reshaping

x_train[0] => array([[8.05977145e-01],
                     [4.92200000e+01],
                     [1.23157152e+08]
                      ])

Making sure no nan values in my training set both x_train, and y_train:

for main_index, xx in enumerate(x_train):
  for i, y in enumerate(xx):
    if type(x_train[main_index][i][0]) !=  np.float64:
      print("Something wrong here:" ,main_index, i)
else:
  print("done") # one done, got nothing wrong

Finally I am training here LSTM

def build_nn():
    model = Sequential()    
    model.add(Bidirectional(LSTM(32, return_sequences=True, input_shape = (x_train.shape[1], 1), name="one"))) #. input_shape = (None, *x_train.shape) , 
    model.add(Dropout(0.20))
    model.add(Bidirectional(LSTM(32, return_sequences=False, name="three")))
    model.add(Dropout(0.10))
    model.add(Dense(32, activation='relu'))
    model.add(Dropout(0.10))
    model.add(Dense(1, activation='sigmoid'))
    opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, decay=0.01)
    model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
    return model

filepath = "bilstmv1.h5"
chkp = ModelCheckpoint(monitor = 'val_accuracy', mode = 'auto', filepath=filepath, verbose = 1, save_best_only=True)


model = build_nn()
model.fit(x_train, y_train, epochs=15, batch_size=32, validation_split=0.1, callbacks=[chkp])

Here is CNN:

model.add(Conv1D(256, 3, input_shape = (x_train.shape[1], 1), activation='relu', padding="same"))
model.add(BatchNormalization())
model.add(Dropout(0.15))
model.add(Conv1D(128, 3, activation='relu', padding="same"))
model.add(BatchNormalization())
model.add(Dropout(0.15))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.15))
model.add(Dense(1))
model.add(Activation("sigmoid"))
# opt = Adam(lr=0.01, beta_1=0.9, beta_2=0.999, decay=0.01)
# opt = SGD(lr=0.01)
model.compile(loss='binary_crossentropy', optimizer='adamax', metrics=['accuracy'])

All seems good until I start training, both val_loss and val_accuracy are NOT changing when training

Epoch 1/15
1011/1011 [==============================] - 18s 10ms/step - loss: 0.6803 - accuracy: 0.5849 - val_loss: 0.6800 - val_accuracy: 0.5803

Epoch 00001: val_accuracy improved from -inf to 0.58025, saving model to bilstmv1.h5
Epoch 2/15
1011/1011 [==============================] - 9s 9ms/step - loss: 0.6782 - accuracy: 0.5877 - val_loss: 0.6799 - val_accuracy: 0.5803

Epoch 00002: val_accuracy did not improve from 0.58025
Epoch 3/15
1011/1011 [==============================] - 9s 8ms/step - loss: 0.6793 - accuracy: 0.5844 - val_loss: 0.6799 - val_accuracy: 0.5803

Epoch 00003: val_accuracy did not improve from 0.58025
Epoch 4/15
1011/1011 [==============================] - 9s 9ms/step - loss: 0.6784 - accuracy: 0.5861 - val_loss: 0.6799 - val_accuracy: 0.5803

Epoch 00004: val_accuracy did not improve from 0.58025
Epoch 5/15
1011/1011 [==============================] - 9s 9ms/step - loss: 0.6796 - accuracy: 0.5841 - val_loss: 0.6799 - val_accuracy: 0.5803

Epoch 00005: val_accuracy did not improve from 0.58025
Epoch 6/15
1011/1011 [==============================] - 8s 8ms/step - loss: 0.6792 - accuracy: 0.5842 - val_loss: 0.6798 - val_accuracy: 0.5803

Epoch 00006: val_accuracy did not improve from 0.58025
Epoch 7/15
1011/1011 [==============================] - 8s 8ms/step - loss: 0.6779 - accuracy: 0.5883 - val_loss: 0.6798 - val_accuracy: 0.5803

Epoch 00007: val_accuracy did not improve from 0.58025
Epoch 8/15
1011/1011 [==============================] - 8s 8ms/step - loss: 0.6797 - accuracy: 0.5830 - val_loss: 0.6798 - val_accuracy: 0.5803

Epoch 00008: val_accuracy did not improve from 0.58025

I tried to change every single thing i saw here and there and nothing worked, I am sure I have no nan values in my data as i did remove them in the pre-processing steps. I tried to run CNN to check if it is related to LSTM or not and got the same thing (neither one of the 2 things are changing). Also, after trying different optimizers, nothing has changed. Any help is really appreciated.

Here is a link of the dataset after doing all the pre-processing: https://drive.google.com/file/d/1punYl-f3dFbw1YWtw3M7hVwy5knhqU9Q/view?usp=sharing

Using Decision Tree I was able to get 85%

decesion_tree = DecisionTreeClassifier().fit(x_train, y_train)
dt_predictions = decesion_tree.predict(x_test)
score = metrics.accuracy_score(y_test, dt_predictions) # 85

Note: the predictions test has same values for all testing set (x_test), that tell us why the val_accuracy is not changing.

from Keras: val_loss & val_accuracy are not changing