Stock Price Prediction with LSTM

# Section 1: Import needed libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from keras.callbacks import ModelCheckpoint
from tensorflow.keras.models import load_model
from keras.models import Sequential, load_model
from keras.layers import LSTM, Dropout, Dense
from sklearn.metrics import r2_score, mean_absolute_error, mean_absolute_percentage_error

# Section 2: Read and Preprocess Data
from matplotlib.dates import YearLocator, DateFormatter, MonthLocator

df['DATE'] = pd.to_datetime(df['DATE'], format='%d/%m/%Y')
df = df.sort_values(by='DATE')

df['CLOSE'] = df['CLOSE'].astype(str).str.replace(',', '').astype(float)
df['OPEN'] = df['OPEN'].astype(str).str.replace(',', '').astype(float)
df['HIGH'] = df['HIGH'].astype(str).str.replace(',', '').astype(float)
df['LOW'] = df['LOW'].astype(str).str.replace(',', '').astype(float)

df['YEAR'] = df['DATE'].dt.year

# Visualize closing price
plt.figure(figsize=(10, 5))
plt.plot(df['DATE'], df['CLOSE'], label='CLOSE', color='red')
plt.xlabel('YEAR')
plt.ylabel('CLOSE')
plt.title('The closing price chart over the years')
plt.legend(loc='best')

# Section 3: Normalize Data & Split Train/Test
scaler = MinMaxScaler()
close_scaled = scaler.fit_transform(df[['CLOSE']])

train_size = int(len(close_scaled) * 0.8)
train_data = close_scaled[:train_size]
test_data = close_scaled[train_size:]

# Section 4: Create Sequences for LSTM
def create_sequences(data, seq_length):
    X, y = [], []
    for i in range(len(data) - seq_length):
        X.append(data[i:i+seq_length])
        y.append(data[i+seq_length])
    return np.array(X), np.array(y)

seq_length = 60
X_train, y_train = create_sequences(train_data, seq_length)
X_test, y_test = create_sequences(test_data, seq_length)

# Section 5: Build LSTM Model
model = Sequential([
    LSTM(50, return_sequences=True, input_shape=(seq_length, 1)),
    Dropout(0.2),
    LSTM(50, return_sequences=True),
    Dropout(0.2),
    LSTM(50),
    Dropout(0.2),
    Dense(1)
])

model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.1)

# Section 6: Evaluate & Predict
predictions = model.predict(X_test)
predictions = scaler.inverse_transform(predictions)
y_test_actual = scaler.inverse_transform(y_test)

r2 = r2_score(y_test_actual, predictions)
mae = mean_absolute_error(y_test_actual, predictions)
mape = mean_absolute_percentage_error(y_test_actual, predictions)

print(f'R² Score: {r2:.4f}')
print(f'MAE: {mae:.4f}')
print(f'MAPE: {mape:.4f}%')