LSTM keras: how to predict the value on multiple features with time series?

I need to predict the value of the column Close (stock price) with 3 inputs: Close, Open and Volume. Example of a dataset:

             Close    Open   Volume
Date                               
2019-09-20  5489.0  5389.0  1578781
2019-09-23  5420.0  5460.0   622325
2019-09-24  5337.5  5424.0   688395
2019-09-25  5343.5  5326.5   628849
2019-09-26  5387.5  5345.0   619344
...            ...     ...      ...
2020-03-30  4459.0  4355.0  1725236
2020-03-31  4715.0  4550.0  2433310
2020-04-01  4674.5  4596.0  1919728
2020-04-02  5050.0  4865.0  3860103
2020-04-03  5204.5  5050.0  3133078

[134 rows x 3 columns]

Info:

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 134 entries, 2019-09-20 to 2020-04-03
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Close   134 non-null    float64
 1   Open    134 non-null    float64
 2   Volume  134 non-null    int64  
dtypes: float64(2), int64(1)

My question is what is wrong in the code below for predicting the value for the last 10 days. The result I have is that it is obviously wrong:

Epoch 1/1
64/64 [==============================] - 6s 88ms/step - loss: 37135470.9219
[[32.588608]
 [32.587284]
 [32.586754]
 [32.587196]
 [32.58649 ]
 [32.58663 ]
 [32.586098]
 [32.58682 ]
 [32.586452]
 [32.588108]]
rmse: 4625.457010985681

The problem still remains even if I remove fit_transform (for y_train I also do not do scale, I do not know if it is necessary). Code:

from math import sqrt
from numpy import concatenate
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import Dense, Dropout, Embedding
from keras.layers import LSTM
import numpy as np
from datetime import datetime, timedelta
import yfinance as yf

start = (datetime.now() - timedelta(days=200)).strftime("%Y-%m-%d")
end = (datetime.now() - timedelta(days=1)).strftime("%Y-%m-%d")
df = yf.download(tickers="LKOH.ME", start=start, end=end, interval="1d")
dataset = df.loc[start:end].filter(['Close', 'Open', 'Volume']).values
scaler = MinMaxScaler(feature_range=(0,1))

training_data_len = len(dataset) - 10 # last 10 days to test
train_data = dataset[0:int(training_data_len), :]
x_train = []
y_train = []

for i in range(60, len(train_data)):
    x_train.append(train_data[i-60:i, :]) # получаем 3 features
    y_train.append(train_data[i, 0]) # 0 значит предсказываем Close

x_train, y_train = np.array(x_train), np.array(y_train)
x_train = x_train.reshape((x_train.shape[0], x_train.shape[1]*x_train.shape[2])) # convert to 2d for fit_transform()
x_train = scaler.fit_transform(x_train)
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))

model = Sequential()
# здесь нужно поменять input_shape=(x_train.shape[1], 3) в силу 3-х features?
model.add(LSTM(50, return_sequences=True, input_shape=(x_train.shape[1], 1)))
model.add(LSTM(50))
model.add(Dense(25))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(x_train, y_train, batch_size=1, epochs=1)

test_data = dataset[training_data_len - 60:, :]
x_test = []
y_test = dataset[training_data_len:, 0]
for i in range(60, len(test_data)):
    x_test.append(test_data[i-60:i, :])

x_test = np.array(x_test)
x_test = x_test.reshape((x_test.shape[0], x_test.shape[1]*x_test.shape[2]))
x_test = scaler.fit_transform(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))

predictions = model.predict(x_test)
print(predictions)
print('rmse:', np.sqrt(np.mean(((predictions - y_test) ** 2))))