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from flask import Flask, jsonify
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from flask_cors import CORS
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import yfinance as yf
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from datetime import date, datetime, timedelta
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import pandas as pd
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import numpy as np
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app = Flask(__name__)
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CORS(app)
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@app.route('/')
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def home():
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    return "Marcello Personal Portfolio Dashboard API"
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@app.route('/stock-data')
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def get_stock_data():
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    tickers = ['VHYL.L', 'MSFT', 'SGLN.L', 'IMEU.L', 'BABA', 'SAAA.L', 'XYZ']
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    # weights of the stocks
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    weights = np.array([0.2164, 0.1797, 0.1536, 0.1304, 0.1289, 0.1275, 0.0635])
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    today = date.today()
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    data = yf.download(tickers, start='2021-01-19', end="2025-02-21", interval="1mo", auto_adjust = False)['Adj Close']
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    # normalize the price
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    normalized_data = data / data.iloc[0]
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    # portfolio performance
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    portfolio_performance = (normalized_data * weights).sum(axis=1) #  weighted sum of all shares in the portfolio
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    # Calculate the percentage change
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    pct_change = (portfolio_performance.pct_change().fillna(0) * 100).tolist()
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      # Prepare JSON response
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    response_data = {
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        "labels": normalized_data.index.strftime("%Y-%m-%d").tolist(),
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        # add the percentage change to the response
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        "pct_change": pct_change, # percentage change
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        # add the weights to the response to show the weights of the stocks showing also the ticker of the stock
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        "portfolio_weights": {
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            "tickers": tickers,
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            "weights": weights.tolist()
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        }
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    }
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    return jsonify(response_data)
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PORTFOLIO = ['VHYL.L', 'MSFT', 'SGLN.L', 'IMEU.L', 'BABA', 'SAAA.L', 'XYZ']
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def calculate_performance(ticker_data, periods):
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    """Calculate performance for different time periods"""
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    results = {}
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    latest_price = ticker_data.iloc[-1]
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    for period_name, days in periods.items():
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        if days > len(ticker_data):
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            results[period_name] = None
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            continue
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        start_price = ticker_data.iloc[-days]
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        performance = ((latest_price - start_price) / start_price) * 100
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        results[period_name] = round(performance, 2)
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    return results
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@app.route('/portfolio-performance')
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def portfolio_performance():
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    today = datetime.now()
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    # Define time periods in trading days (approximations)
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    periods = {
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        '1_month': 21,
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        'ytd': max(1, (today - datetime(today.year, 1, 1)).days), # Year-to-date (from Jan 1st) excluding weekends
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        '1_year': 252,
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        '3_year': 756,
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        '5_year': 1260
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    }
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    # Calculate start date for data retrieval (add some buffer)
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    start_date = (today - timedelta(days=1900)).strftime('%Y-%m-%d')
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    end_date = today.strftime('%Y-%m-%d')
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    portfolio_data = []
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    for ticker in PORTFOLIO:
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        try:
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            # Get historical data
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            stock_data = yf.Ticker(ticker)
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            hist = stock_data.history(start=start_date, end=end_date)
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            # Skip if no data
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            if hist.empty:
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                continue
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            # Get adjusted close prices
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            adj_close = hist['Close']
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            # Calculate performance for different periods
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            performance = calculate_performance(adj_close, periods)
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            # Get current price
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            current_price = round(float(adj_close.iloc[-1]), 2)
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            # Get company name
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            info = stock_data.info
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            company_name = info.get('shortName', ticker)
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            portfolio_data.append({
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                'ticker': ticker,
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                'name': company_name,
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                'current_price': current_price,
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                'performance': performance
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            })
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        except Exception as e:
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            print(f"Error processing {ticker}: {e}")
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    return jsonify(portfolio_data)
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if __name__ == '__main__':
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    #app.run(debug=True) # run the app in debug mode locally
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    app.run() # run the app in production mode