LTSM_model.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pandas_datareader import data as pdr
import yfinance as yf
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from math import sqrt
from keras.models import Sequential
from keras.layers import Dense, LSTM
import warnings

# Ignore warnings
warnings.filterwarnings("ignore")

# Configure Yahoo Finance data source
yf.pdr_override()

# Download historical stock data
ticker = "AAPL"
start_date = "2020-01-01"
end_date = "2021-01-01"
stock_data = pdr.get_data_yahoo(ticker, start=start_date, end=end_date)

# Select the closing prices
closing_prices = stock_data["Close"]

# Preprocessing the data
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(closing_prices.values.reshape(-1, 1))

# Split the data into training and testing sets
train_size = int(len(scaled_data) * 0.8)
train_data, test_data = scaled_data[0:train_size, :], scaled_data[train_size:, :]
test_data_index = closing_prices[train_size:].index

# Function to create time series dataset for LSTM
def create_dataset(dataset, window_size=1):
    data_x, data_y = [], []
    for i in range(len(dataset) - window_size - 1):
        data_x.append(dataset[i:(i + window_size), 0])
        data_y.append(dataset[i + window_size, 0])
    return np.array(data_x), np.array(data_y)

# Create the time series dataset for LSTM
window_size = 5
train_x, train_y = create_dataset(train_data, window_size)
test_x, test_y = create_dataset(test_data, window_size)

# Reshape the input to be [samples, time steps, features]
train_x = np.reshape(train_x, (train_x.shape[0], 1, train_x.shape[1]))
test_x = np.reshape(test_x, (test_x.shape[0], 1, test_x.shape[1]))

# Build the LSTM model
print("BUILDING LSTM MODEL...")
model = Sequential()
model.add(LSTM(50, input_shape=(1, window_size)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')

# Train the LSTM model
print("TRAINING LSTM MODEL...")
model.fit(train_x, train_y, epochs=100, batch_size=1, verbose=0)

# Make predictions using the LSTM model
print("MAKING PREDICTIONS...")
train_predict = model.predict(train_x)
test_predict = model.predict(test_x)

# Invert the predictions
train_predict = scaler.inverse_transform(train_predict)
train_y = scaler.inverse_transform([train_y])
test_predict = scaler.inverse_transform(test_predict)
test_y = scaler.inverse_transform([test_y])

# Calculate the root mean squared error (RMSE) for LSTM
test_rmse = sqrt(mean_squared_error(test_y[0], test_predict[:, 0]))
print("LSTM Test RMSE: ", test_rmse)

# Plot the actual vs. predicted closing prices
plt.figure(figsize=(12, 6))
plt.plot(closing_prices[:-len(test_data_index)], label="Training data Closing prices")
plt.plot(closing_prices[-len(test_data_index):], label="Actual closing prices")
plt.plot(test_data_index[7:], test_predict[:-1], label="LSTM Predicted closing prices")
plt.xlabel("Date")
plt.ylabel("Closing Price")
plt.title(f"{ticker} Stock Closing Price Prediction using LSTM")
plt.legend()
plt.show()