main.py

from dotenv import load_dotenv
from alpha_vantage.timeseries import TimeSeries
import os

load_dotenv()
api_key = os.getenv("ALPHA_VANTAGE_KEY")
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LinearRegression

ts = TimeSeries(key=api_key, output_format='pandas')
data, meta_data = ts.get_daily(symbol='AAPL', outputsize='compact')
data = data.rename(columns={
    '1. open': 'Open',
    '2. high': 'High',
    '3. low': 'Low',
    '4. close': 'Close',
    '5. volume': 'Volume'
})
data = data.sort_index()  # Ensure data is in time order (oldest first)
data = data[['Close']].dropna()

# Add time index for regression
data['TimeIndex'] = np.arange(len(data))
X = data[['TimeIndex']]
y = data['Close']

# Train Linear Regression model
model = LinearRegression()
model.fit(X, y)
data['Predicted_Close'] = model.predict(X)

# Plot results
plt.figure(figsize=(10, 5))
plt.plot(data['Close'], label='Actual Close Price')
plt.plot(data['Predicted_Close'], label='Predicted Trend', linestyle='--')
plt.title(f'{'AAPL'} Price Prediction using Linear Regression')
plt.xlabel('Days')
plt.ylabel('Price (USD)')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()