Navigating Financial Insights: Analyzing Stock Data with Python and Visualization

import yfinance as yf
import pandas as pd
import numpy as np

# Fetch historical stock data for Apple and Microsoft
symbols = ['AAPL', 'MSFT']
start_date = '2020-01-01'
end_date = '2023-01-01'

try:
    stock_data = yf.download(symbols, start=start_date, end=end_date, progress=False)

    # Check if data is available
    if stock_data.empty:
        raise Exception('No price data found for the specified stocks and date range.')

    # Display the first few rows of the dataset
    print(stock_data.head())

except Exception as e:
    print(f"Error: {e}")

import matplotlib.pyplot as plt

# Line plot for closing prices
plt.figure(figsize=(12, 6))
for symbol in symbols:
    plt.plot(stock_data['Close'][symbol], label=symbol, linewidth=2)
plt.title('Historical Closing Prices of Apple and Microsoft')
plt.xlabel('Date')
plt.ylabel('Closing Price (USD)')
plt.legend()
plt.show()

# Descriptive statistics
descriptive_stats = stock_data.describe()
print("\nDescriptive Statistics:")
print(descriptive_stats)

# Dispersion measures (Standard Deviation and Variance)
std_dev = stock_data.std()
variance = stock_data.var()
print("\nStandard Deviation:")
print(std_dev)
print("\nVariance:")
print(variance)

# Correlation
correlation_matrix = stock_data['Close'].corr()
print("\nCorrelation Matrix:")
print(correlation_matrix)

# Covariance Matrix
covariance_matrix = stock_data['Close'].cov()
print("\nCovariance Matrix:")
print(covariance_matrix)

import seaborn as sns

# Heatmap for correlation matrix
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f", linewidths=0.5)
plt.title('Correlation Heatmap of Closing Prices')
plt.show()