LSTM predict stock market is hard to converge in Keras - tensorflow

Question

I am working on a stock prediction project and I just want to predict the gain and drop labels from the LSTM net. It is a binary classification problem.
However, my LSTM net is hard to converge even I reduce the training set a lot. Technically, it should overfit easily. But my prediction accuracy is still only 60% and loss is around 0.7 even I just feed 90 samples for training. So, I was thinking I probably made some mistakes in building the neural net. However, due to my limited ability, I cannot find the reason. Therefore, I really hope someone can take a look at my code and point out the reason! I will appreciate a lot!
My code is given below.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import collections
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers.advanced_activations import LeakyReLU
from keras.layers import Dense, LSTM, Dropout, Activation, Flatten ,BatchNormalization
from keras.utils import to_categorical, np_utils
from keras.optimizers import SGD
data = pd.read_csv("EURUSD_M5_201910210000_201910251140.csv", sep="\t")
train_cut = int(data.shape[0] * 0.8)
dataset_train = data[0:train_cut]
training_set = dataset_train["<OPEN>"].values
sc = MinMaxScaler(feature_range=(0, 1))
train_sec_scaled = sc.fit_transform(training_set.reshape(-1, 1))
X_train = []
y_train = []
step_size = 60
predic_days = 1
for i in range(step_size, len(train_sec_scaled) - predic_days):
X_train.append(train_sec_scaled[i - step_size : i, 0])
y_value = train_sec_scaled[i : i + predic_days, 0]
last_day_value = train_sec_scaled[i - 1, 0]
# 1 ==> up, 0 ==> down
if y_value[0] > last_day_value:
y_train.append([1])
else:
y_train.append([0])
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], 1)
y_train = y_train.reshape(y_train.shape[0], y_train.shape[1])
y_train = np_utils.to_categorical(y_train, 2)
print(y_train)
print("train data generated!")
print(X_train.shape, y_train.shape)
def train():
model = Sequential()
model.add(Dropout(0.2))
model.add(LSTM(40))
model.add(BatchNormalization())
model.add(LeakyReLU(alpha=0.02))
model.add(Dropout(0.2))
model.add(Dense(30, kernel_initializer='glorot_normal'))
model.add(BatchNormalization())
model.add(LeakyReLU(alpha=0.02))
model.add(Dropout(0.2))
model.add(Dense(2, activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-4, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
model.fit(X_train,
y_train,batch_size=32,epochs=10000)
model.save("trend_analysis.h5")
print("model saved!")
if __name__ == "__main__":
train()
Also, here is the dataset I used:
https://drive.google.com/open?id=1r_0Ko1F6i0F1pToTSsQF1xGt_FTtpUux
Thanks in advance!