CopyLSTMPredAPPL.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 26 12:23:25 2023

@author: jebin
"""

import numpy as np
import pandas as pd
import yfinance as yf
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

# Define the start and end dates for the data
start = '2010-01-01'
end = '2023-11-25'

# Fetch the stock data from Yahoo Finance
api_key = 'DSCL4PPQQKL33HQ1'  # Replace with your actual API key
yf.pdr_override()
df = yf.download('AAPL', start=start, end=end)

# Preprocess the data
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(df['Close'].values.reshape(-1, 1))

# Split the data into training and testing sets
train_size = int(len(scaled_data) * 0.8)
train_data = scaled_data[:train_size]
test_data = scaled_data[train_size:]

# Define the window size for the LSTM model
window_size = 60

# Create the training data
X_train = []
y_train = []
for i in range(window_size, len(train_data)):
    X_train.append(train_data[i-window_size:i, 0])
    y_train.append(train_data[i, 0])
X_train, y_train = np.array(X_train), np.array(y_train)

# Reshape the training data for LSTM input
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))

# Build the LSTM model
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
model.add(LSTM(units=50))
model.add(Dense(units=1))

# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')

# Train the model
model.fit(X_train, y_train, epochs=10, batch_size=32)

# Create the testing data
X_test = []
y_test = df['Close'].values[train_size+window_size:]
for i in range(window_size, len(test_data)):
    X_test.append(test_data[i-window_size:i, 0])
X_test = np.array(X_test)

# Reshape the testing data for LSTM input
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))

# Make predictions
predicted_data = model.predict(X_test)
predicted_prices = scaler.inverse_transform(predicted_data)

# Create a DataFrame to store the predicted prices with dates
predicted_df = pd.DataFrame({'Date': df.index[train_size+window_size:], 'Predicted Price': predicted_prices.flatten()})

# Set the dates as the index for the DataFrame
predicted_df.set_index('Date', inplace=True)

# Visualize the actual and predicted prices
plt.plot(df.index[train_size+window_size:], y_test, color='blue', label='Actual Price')
plt.plot(predicted_df.index, predicted_df['Predicted Price'], color='red', label='Predicted Price')
plt.xlabel('Date')
plt.ylabel('Stock Price')
plt.title('TESLA  Stock Price Prediction')
plt.xticks(rotation=45)  # Rotate x-axis dates by 45 degrees
plt.legend()
plt.show()

# Print the actual and predicted prices with date and time
for i in range(len(predicted_df)):
    date_str = predicted_df.index[i].strftime('%Y-%m-%d')
    actual_price = y_test[i]
    predicted_price = predicted_df['Predicted Price'].iloc[i]
    print(f"Date: {date_str}, Actual Price: {actual_price}, Predicted Price: {predicted_price}")