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

# 1. Download 3 years of Nifty 50 data
nifty = yf.Ticker("^NSEI")
df = nifty.history(period="3y")

# 2. Daily returns
df['Return'] = df['Close'].pct_change()
df = df.dropna()

# Plot histogram
plt.figure(figsize=(10, 5))
plt.hist(df['Return'] * 100, bins=50, edgecolor='black', color='#2563EB', alpha=0.7)
plt.title('Nifty 50 Daily Returns Distribution (3 Years)')
plt.xlabel('Daily Return (%)')
plt.ylabel('Frequency')
plt.axvline(x=df['Return'].mean()*100, color='red', linestyle='--', label=f'Mean: {df["Return"].mean()*100:.3f}%')
plt.legend()
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

# 3. Statistics
print("="*50)
print("NIFTY 50 RETURN STATISTICS")
print("="*50)
print(f"Mean Daily Return:    {df['Return'].mean()*100:.4f}%")
print(f"Std Dev (Daily):      {df['Return'].std()*100:.4f}%")
print(f"Skewness:             {df['Return'].skew():.4f}")
print(f"Kurtosis:             {df['Return'].kurtosis():.4f}")
print(f"Annualized Return:    {df['Return'].mean()*252*100:.2f}%")
print(f"Annualized Vol:       {df['Return'].std()*np.sqrt(252)*100:.2f}%")

# 4. Best and worst days
best_day = df.loc[df['Return'].idxmax()]
worst_day = df.loc[df['Return'].idxmin()]
print(f"\nBest Day:  {best_day.name.date()} → {best_day['Return']*100:+.2f}%")
print(f"Worst Day: {worst_day.name.date()} → {worst_day['Return']*100:+.2f}%")

# 5. Moving averages and crossovers
df['MA50'] = df['Close'].rolling(50).mean()
df['MA200'] = df['Close'].rolling(200).mean()
df = df.dropna()

# Golden Cross: MA50 crosses above MA200
df['Signal'] = np.where(df['MA50'] > df['MA200'], 1, -1)
df['Crossover'] = df['Signal'].diff()

golden_crosses = df[df['Crossover'] == 2]   # MA50 crosses above MA200
death_crosses = df[df['Crossover'] == -2]    # MA50 crosses below MA200

print(f"\nGolden Crosses: {len(golden_crosses)}")
for date in golden_crosses.index:
    print(f"  → {date.date()}")

print(f"\nDeath Crosses: {len(death_crosses)}")
for date in death_crosses.index:
    print(f"  → {date.date()}")

import pandas as pd
import yfinance as yf

# Define portfolio
portfolio = {
    'Stock': ['Reliance', 'TCS', 'HDFC Bank', 'Infosys', 'Bharti Airtel'],
    'Ticker': ['RELIANCE.NS', 'TCS.NS', 'HDFCBANK.NS', 'INFY.NS', 'BHARTIARTL.NS'],
    'Shares': [150, 80, 200, 120, 100],
    'Buy_Price': [2400, 3200, 1500, 1400, 1100]
}

df = pd.DataFrame(portfolio)

# 1. Fetch current prices
current_prices = {}
for ticker in df['Ticker']:
    stock = yf.Ticker(ticker)
    price = stock.history(period='1d')['Close'].iloc[-1]
    current_prices[ticker] = price

df['Current_Price'] = df['Ticker'].map(current_prices)

# 2. P&L calculations
df['Invested'] = df['Shares'] * df['Buy_Price']
df['Current_Value'] = df['Shares'] * df['Current_Price']
df['PnL'] = df['Current_Value'] - df['Invested']
df['PnL_Pct'] = (df['Current_Price'] / df['Buy_Price'] - 1) * 100

# 3. Portfolio weights
total_value = df['Current_Value'].sum()
df['Weight_Pct'] = df['Current_Value'] / total_value * 100

# 4. Fetch 1-year returns
for idx, row in df.iterrows():
    hist = yf.Ticker(row['Ticker']).history(period='1y')
    if len(hist) > 0:
        yr_return = (hist['Close'].iloc[-1] / hist['Close'].iloc[0] - 1) * 100
        df.at[idx, 'Y1_Return_Pct'] = yr_return

# Display
print("="*80)
print("PORTFOLIO SUMMARY")
print("="*80)
print(df[['Stock', 'Shares', 'Buy_Price', 'Current_Price', 'Invested', 'Current_Value', 'PnL', 'PnL_Pct']].to_string(index=False))
print(f"\nTotal Invested:  ₹{df['Invested'].sum():>12,.2f}")
print(f"Current Value:   ₹{df['Current_Value'].sum():>12,.2f}")
print(f"Total P&L:       ₹{df['PnL'].sum():>12,.2f}")
print(f"Overall Return:  {(df['Current_Value'].sum()/df['Invested'].sum()-1)*100:>11.2f}%")

# 5. Export to Excel
df.to_excel('portfolio_summary.xlsx', index=False)
print("\n✅ Exported to portfolio_summary.xlsx")

import numpy as np
import pandas as pd

def dcf_valuation(fcfs, wacc, terminal_growth, shares_outstanding=None, net_debt=0):
    """
    Perform DCF valuation.
    
    Parameters:
    - fcfs: list of projected free cash flows (₹ Cr)
    - wacc: weighted average cost of capital (decimal, e.g., 0.12 for 12%)
    - terminal_growth: perpetual growth rate (decimal, e.g., 0.03 for 3%)
    - shares_outstanding: in Cr (optional, for per-share value)
    - net_debt: net debt in ₹ Cr (for equity value)
    
    Returns: DataFrame with detailed calculations
    """
    n = len(fcfs)
    years = list(range(1, n + 1))
    
    # Present Value of each FCF
    pv_fcfs = [fcf / (1 + wacc) ** t for fcf, t in zip(fcfs, years)]
    
    # Terminal Value (Gordon Growth Model)
    terminal_fcf = fcfs[-1] * (1 + terminal_growth)
    terminal_value = terminal_fcf / (wacc - terminal_growth)
    pv_terminal = terminal_value / (1 + wacc) ** n
    
    # Enterprise Value
    ev = sum(pv_fcfs) + pv_terminal
    
    # Equity Value
    equity_value = ev - net_debt
    
    # Build results DataFrame
    results = pd.DataFrame({
        'Year': years,
        'Projected_FCF': fcfs,
        'Discount_Factor': [1/(1+wacc)**t for t in years],
        'PV_of_FCF': pv_fcfs
    })
    
    # Summary
    print("="*60)
    print("DCF VALUATION SUMMARY")
    print("="*60)
    print(f"WACC:                    {wacc*100:.1f}%")
    print(f"Terminal Growth Rate:    {terminal_growth*100:.1f}%")
    print(f"Projection Period:       {n} years")
    print(f"")
    print(f"Sum of PV of FCFs:       ₹{sum(pv_fcfs):>10,.2f} Cr")
    print(f"Terminal Value:          ₹{terminal_value:>10,.2f} Cr")
    print(f"PV of Terminal Value:    ₹{pv_terminal:>10,.2f} Cr")
    print(f"")
    print(f"Enterprise Value:        ₹{ev:>10,.2f} Cr")
    print(f"Net Debt:                ₹{net_debt:>10,.2f} Cr")
    print(f"Equity Value:            ₹{equity_value:>10,.2f} Cr")
    
    if shares_outstanding:
        price_per_share = equity_value / shares_outstanding
        print(f"Shares Outstanding:      {shares_outstanding:>10,.2f} Cr")
        print(f"Implied Share Price:     ₹{price_per_share:>10,.2f}")
    
    print("="*60)
    
    return results

# ========================================
# Test with given data
# ========================================
fcfs = [100, 120, 140, 160, 180]  # ₹ Cr
wacc = 0.12  # 12%
terminal_growth = 0.03  # 3%

results = dcf_valuation(
    fcfs=fcfs,
    wacc=wacc,
    terminal_growth=terminal_growth,
    shares_outstanding=10,  # 10 Cr shares
    net_debt=50  # ₹50 Cr net debt
)

print("\nDetailed Year-by-Year:")
print(results.round(2).to_string(index=False))

import pandas as pd
import yfinance as yf

# Define peer group
companies = {
    'TCS': 'TCS.NS',
    'Infosys': 'INFY.NS',
    'Wipro': 'WIPRO.NS',
    'HCL Tech': 'HCLTECH.NS',
    'Tech Mahindra': 'TECHM.NS'
}

# Fetch key metrics for each company
data = []
for name, ticker in companies.items():
    stock = yf.Ticker(ticker)
    info = stock.info
    
    data.append({
        'Company': name,
        'Ticker': ticker,
        'Market Cap (₹ Cr)': info.get('marketCap', 0) / 1e7,  # Convert to Cr
        'P/E Ratio': info.get('trailingPE', None),
        'P/B Ratio': info.get('priceToBook', None),
        'EV/EBITDA': info.get('enterpriseToEbitda', None),
        'Dividend Yield %': info.get('dividendYield', 0) * 100 if info.get('dividendYield') else None,
        'ROE %': info.get('returnOnEquity', 0) * 100 if info.get('returnOnEquity') else None,
        'Revenue Growth %': info.get('revenueGrowth', 0) * 100 if info.get('revenueGrowth') else None,
    })

df = pd.DataFrame(data)

# Display comparison
print("="*80)
print("INDIAN IT SECTOR — COMPARABLE COMPANY ANALYSIS")
print("="*80)
print(df.to_string(index=False))

# Summary statistics
print("\n" + "="*80)
print("SUMMARY STATISTICS")
print("="*80)
metrics = ['P/E Ratio', 'P/B Ratio', 'EV/EBITDA']
for metric in metrics:
    print(f"\n{metric}:")
    print(f"  Median: {df[metric].median():.2f}")
    print(f"  Mean:   {df[metric].mean():.2f}")
    cheapest = df.loc[df[metric].idxmin(), 'Company']
    print(f"  Cheapest: {cheapest} ({df[metric].min():.2f})")

# Export to Excel
df.to_excel('it_sector_comps.xlsx', index=False)
print("\n✅ Exported to it_sector_comps.xlsx")