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

# Dow Jones Index Ticker Symbols
dow_tickers = [
    "AAPL", "MSFT", "UNH", "JNJ", "V", "WMT", "PG", "NVDA", "HD", "JPM", "AMZN",
    "CVX", "MRK", "KO", "CSCO", "MCD", "DIS", "CRM", "NKE", "AMGN", "VZ",
    "IBM", "CAT", "GS", "BA", "TRV", "MMM", "AXP", "SHW", "HON"
]

# Fetch market cap and opening price data from Yahoo Finance
data = []
for ticker in dow_tickers:
    stock = yf.Ticker(ticker)
    info = stock.info
    market_cap = info.get("marketCap", 0)
    hist = stock.history(period="1d")
    opening_price = hist["Open"].iloc[0] if not hist.empty else 0
    data.append((ticker, market_cap, opening_price))

# Convert to DataFrame
market_caps = pd.DataFrame(data, columns=["Ticker", "MarketCap", "OpeningPrice"])

# Calculate Normalized Market Caps
market_caps = market_caps.sort_values(by="MarketCap", ascending=False).reset_index(drop=True)
market_caps["NormalizedMarketCap"] = market_caps["MarketCap"] / market_caps["MarketCap"].sum()

# Calculate Price Weighted Normalized Values
market_caps["NormalizedPriceWeight"] = market_caps["OpeningPrice"] / market_caps["OpeningPrice"].sum()

# Plot Normalized Market Caps
plt.figure(figsize=(12, 6))
plt.bar(market_caps["Ticker"], market_caps["NormalizedMarketCap"], color='lightblue')
plt.title("Normalized Market Caps of Dow Jones Stocks", fontsize=14)
plt.ylabel("Normalized Market Cap", fontsize=12)
plt.xlabel("Ticker", fontsize=12)
plt.xticks(rotation=45, fontsize=10)
plt.yticks(fontsize=10)
plt.grid(axis='y', visible=True)
plt.tight_layout()
plt.show()

# Plot Price Weighted Normalized Values
plt.figure(figsize=(12, 6))
plt.bar(market_caps["Ticker"], market_caps["NormalizedPriceWeight"], color='orange')
plt.title("Price Weighted Normalized Values of Dow Jones Stocks", fontsize=14)
plt.ylabel("Normalized Price Weight", fontsize=12)
plt.xlabel("Ticker", fontsize=12)
plt.xticks(rotation=45, fontsize=10)
plt.yticks(fontsize=10)
plt.grid(axis='y', visible=True)
plt.tight_layout()
plt.show()