import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import yfinance as yf

# Beginner Example 1: Load multi-ticker OHLCV data for 5 tech stocks
tickers = ['AAPL','MSFT','GOOGL','AMZN','TSLA']
ohlcv = yf.download(tickers, period='1y', auto_adjust=True, progress=False)
ohlcv = ohlcv.stack(future_stack=True).rename_axis(['Date','Ticker']).reset_index()
ohlcv.columns.name = None
print(ohlcv.shape)
print(ohlcv.head(3))

# Beginner Example 2: Pivot OHLCV data to see closing prices in wide format
close_pivot = ohlcv.pivot(index='Date', columns='Ticker', values='Close')
print(close_pivot.shape)
print(close_pivot.head(3))

# Beginner Example 3: Filter OHLCV for just Tesla (TSLA)
tsla_ohlcv = ohlcv[ohlcv['Ticker'] == 'TSLA']
print(tsla_ohlcv.head(3))

# Intermediate Example 1: Load fundamental data for 12 companies
tickers = ['AAPL','MSFT','GOOGL','AMZN','TSLA','NVDA','META','JPM','JNJ','XOM','WMT','V']
rows = []
for tk in tickers:
    info = yf.Ticker(tk).info
    rows.append({
        'ticker':       tk,
        'sector':       info.get('sector', 'Unknown'),
        'market_cap':   info.get('marketCap'),
        'pe_ratio':     info.get('trailingPE'),
        'eps':          info.get('trailingEps'),
        'profit_margin':info.get('profitMargins'),
        'debt_equity':  info.get('debtToEquity')
    })
fund_df = pd.DataFrame(rows)
print(fund_df.shape)
print(fund_df.head(3))

# Intermediate Example 2: Which companies have the highest P/E ratio?
print(fund_df[['ticker','pe_ratio']].sort_values('pe_ratio', ascending=False))

# Intermediate Example 3: Check sector counts in the fundamentals DataFrame
print(fund_df['sector'].value_counts())

# Intermediate Example 4: Find companies with missing debt-to-equity data
missing_de = fund_df[fund_df['debt_equity'].isnull()]
print(missing_de[['ticker','debt_equity']])

# Intermediate Example 5: Calculate average profit margin by sector
avg_profit_margin = fund_df.groupby('sector')['profit_margin'].mean()
print(avg_profit_margin)

# Intermediate Example 6: Screen for high earnings per share (EPS) companies
top_eps = fund_df[['ticker','eps']].sort_values('eps', ascending=False).head(5)
print(top_eps)

# Advanced Example 1: Load S&P 500 company list and join with fundamentals
url = 'https://raw.githubusercontent.com/datasets/s-and-p-500-companies/main/data/constituents.csv'
sp500 = pd.read_csv(url)
merged = pd.merge(sp500, fund_df, how='left', left_on='Symbol', right_on='ticker')
print(merged[['Symbol', 'Security', 'sector', 'market_cap']].head(5))

# Advanced Example 2: Select top 3 companies by market cap in each sector
top_by_sector = merged.sort_values(['sector', 'market_cap'], ascending=[True,False]).groupby('sector').head(3)
print(top_by_sector[['Symbol','sector','market_cap']])

# Advanced Example 3: Visualize the distribution of P/E ratios
import matplotlib.pyplot as plt
plt.hist(fund_df['pe_ratio'].dropna(), bins=8, color='skyblue', edgecolor='black')
plt.title('Distribution of Price-to-Earnings (P/E) Ratios')
plt.xlabel('P/E Ratio')
plt.ylabel('Frequency')
plt.show()

# Advanced Example 4: End-to-End - Portfolio gain/loss calculation
np.random.seed(42)
tickers = ['AAPL','MSFT','GOOGL','AMZN','TSLA','NVDA','META','NFLX','JPM','JNJ']
sector_map = {'AAPL':'Tech','MSFT':'Tech','GOOGL':'Tech','AMZN':'Consumer',
              'TSLA':'Auto','NVDA':'Tech','META':'Tech','NFLX':'Media',
              'JPM':'Finance','JNJ':'Health'}
data = yf.download(tickers, period='1y', auto_adjust=True, progress=False)['Close']
rows = []
for tk in tickers:
    buy = round(float(data[tk].iloc[0]), 2)
    cur = round(float(data[tk].iloc[-1]), 2)
    rows.append({'ticker': tk, 'shares': int(np.random.randint(5, 100)),
                 'buy_price': buy, 'cur_price': cur, 'sector': sector_map[tk]})
df = pd.DataFrame(rows)
df['gain_pct'] = np.round((df['cur_price'] - df['buy_price']) / df['buy_price'] * 100, 2)
print(df.head(5))

# Error Example 1: What happens if you assign columns directly to multi-ticker OHLCV data?
df = yf.download(['AAPL','MSFT'], period='1y', auto_adjust=True, progress=False)
try:
    df.columns = ['Date','AAPL_Close','MSFT_Close','AAPL_High','MSFT_High','AAPL_Low','MSFT_Low',
               'AAPL_Open','MSFT_Open','AAPL_Volume','MSFT_Volume']
except Exception as e:
    print('Error:', e)

# Error Example 2: Attempt to access a missing column in fundamentals
try:
    print(fund_df['dividend_yield'])
except KeyError as e:
    print('KeyError:', e)

# Error Example 3: Handling missing values in financial ratios
fund_df['profit_margin_filled'] = fund_df['profit_margin'].fillna(0)
print(fund_df[['ticker','profit_margin','profit_margin_filled']])

# Best Practice: Always check data types before analysis
print(fund_df.dtypes)

# Common Pattern: Combine price and fundamental data for enhanced screening
combo = pd.merge(ohlcv[ohlcv['Ticker']=='AAPL'][['Date','Close']],
                 fund_df[fund_df['ticker']=='AAPL'][['pe_ratio','eps']], how='cross')
print(combo.head(5))

# End-to-End Example: From raw fundamentals to sector ranking by average EPS
avg_sector_eps = fund_df.groupby('sector')['eps'].mean().sort_values(ascending=False)
print(avg_sector_eps)