import matplotlib.pyplot as plt
import yfinance as yf
import pandas as pd

# Download stock data
aapl = yf.download('AAPL', start='2023-01-01', end='2023-12-31')

# Create a price chart
plt.figure(figsize=(12, 6))
plt.plot(aapl.index, aapl['Adj Close'], label='AAPL')
plt.title('Apple Stock Price (2023)')
plt.xlabel('Date')
plt.ylabel('Price ($)')
plt.legend()
plt.grid(True)
plt.show()

# Create volume bars
plt.figure(figsize=(12, 6))
plt.bar(aapl.index, aapl['Volume'], alpha=0.5)
plt.title('Trading Volume')
plt.xlabel('Date')
plt.ylabel('Volume')
plt.show()

import seaborn as sns

# Calculate daily returns
returns = aapl['Adj Close'].pct_change()

# Create a distribution plot
plt.figure(figsize=(10, 6))
sns.histplot(returns.dropna(), kde=True)
plt.title('Distribution of Daily Returns')
plt.xlabel('Returns')
plt.show()

# Create a correlation heatmap
portfolio = yf.download(['AAPL', 'MSFT', 'GOOGL'], 
                       start='2023-01-01', 
                       end='2023-12-31')['Adj Close']
corr = portfolio.corr()
plt.figure(figsize=(8, 6))
sns.heatmap(corr, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix')
plt.show()

# Calculate moving averages
aapl['MA50'] = aapl['Adj Close'].rolling(window=50).mean()
aapl['MA200'] = aapl['Adj Close'].rolling(window=200).mean()

# Create technical analysis chart
plt.figure(figsize=(12, 6))
plt.plot(aapl.index, aapl['Adj Close'], label='Price')
plt.plot(aapl.index, aapl['MA50'], label='50-day MA')
plt.plot(aapl.index, aapl['MA200'], label='200-day MA')
plt.title('Technical Analysis Chart')
plt.xlabel('Date')
plt.ylabel('Price ($)')
plt.legend()
plt.grid(True)
plt.show()