1016 lines
40 KiB
Python
1016 lines
40 KiB
Python
import streamlit as st
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import pandas as pd
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import numpy as np
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import plotly.express as px
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import plotly.graph_objects as go
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from pathlib import Path
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import json
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from datetime import datetime
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from typing import List, Dict, Tuple, Optional, Any, Callable, T
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import time
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import threading
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from concurrent.futures import ThreadPoolExecutor, as_completed
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import yfinance as yf
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import requests
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from requests.adapters import HTTPAdapter
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from urllib3.util.retry import Retry
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import os
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import sys
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import logging
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import traceback
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from dotenv import load_dotenv
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# Load environment variables
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load_dotenv()
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# FMP API configuration
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FMP_API_KEY = st.session_state.get('fmp_api_key', os.getenv('FMP_API_KEY', ''))
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FMP_BASE_URL = "https://financialmodelingprep.com/api/v3"
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def get_fmp_session():
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"""Create a session with retry logic for FMP API calls."""
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session = requests.Session()
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retries = Retry(total=3, backoff_factor=0.5)
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session.mount('https://', HTTPAdapter(max_retries=retries))
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return session
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def fetch_etf_data_fmp(ticker: str) -> Optional[Dict[str, Any]]:
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"""
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Fetch ETF data from Financial Modeling Prep API.
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Args:
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ticker: ETF ticker symbol
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Returns:
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Dictionary with ETF data or None if failed
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"""
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try:
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if not FMP_API_KEY:
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logger.warning("FMP API key not configured, skipping FMP data fetch")
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return None
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session = get_fmp_session()
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# Get profile data for current price
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profile_url = f"{FMP_BASE_URL}/profile/{ticker}?apikey={FMP_API_KEY}"
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logger.info(f"Fetching FMP profile data for {ticker}")
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profile_response = session.get(profile_url)
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if profile_response.status_code != 200:
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logger.error(f"FMP API error for {ticker}: {profile_response.status_code}")
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return None
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profile_data = profile_response.json()
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if not profile_data or not isinstance(profile_data, list) or len(profile_data) == 0:
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logger.warning(f"No profile data found for {ticker} in FMP")
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return None
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profile = profile_data[0]
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current_price = float(profile.get('price', 0))
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if current_price <= 0:
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logger.error(f"Invalid price for {ticker}: {current_price}")
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return None
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# Get dividend history
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dividend_url = f"{FMP_BASE_URL}/historical-price-full/stock_dividend/{ticker}?apikey={FMP_API_KEY}"
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logger.info(f"Fetching FMP dividend data for {ticker}")
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dividend_response = session.get(dividend_url)
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if dividend_response.status_code != 200:
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logger.error(f"FMP API error for dividend data: {dividend_response.status_code}")
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return None
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dividend_data = dividend_response.json()
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if not dividend_data or "historical" not in dividend_data or not dividend_data["historical"]:
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logger.warning(f"No dividend history found for {ticker}")
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return None
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# Calculate TTM dividend
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dividends = pd.DataFrame(dividend_data["historical"])
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dividends["date"] = pd.to_datetime(dividends["date"])
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dividends = dividends.sort_values("date")
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# Get dividends in the last 12 months
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one_year_ago = pd.Timestamp.now() - pd.Timedelta(days=365)
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recent_dividends = dividends[dividends["date"] >= one_year_ago]
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if recent_dividends.empty:
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logger.warning(f"No recent dividends found for {ticker}")
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return None
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# Calculate TTM dividend
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ttm_dividend = recent_dividends["dividend"].sum()
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# Calculate yield
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yield_pct = (ttm_dividend / current_price) * 100
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logger.info(f"Calculated yield for {ticker}: {yield_pct:.2f}% (TTM dividend: ${ttm_dividend:.2f}, Price: ${current_price:.2f})")
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etf_data = {
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"Ticker": ticker,
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"Price": current_price,
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"Yield (%)": yield_pct,
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"Risk Level": "High" # Default for high-yield ETFs
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}
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logger.info(f"FMP data for {ticker}: {etf_data}")
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return etf_data
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except Exception as e:
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logger.error(f"Error fetching FMP data for {ticker}: {str(e)}")
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return None
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def fetch_etf_data_yfinance(ticker: str) -> Optional[Dict[str, Any]]:
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"""
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Fetch ETF data from yfinance as fallback.
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Args:
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ticker: ETF ticker symbol
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Returns:
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Dictionary with ETF data or None if failed
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"""
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try:
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logger.info(f"Fetching yfinance data for {ticker}")
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etf = yf.Ticker(ticker)
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info = etf.info
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# Get the most recent dividend yield
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if 'dividendYield' in info and info['dividendYield'] is not None:
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yield_pct = info['dividendYield'] * 100
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logger.info(f"Found dividend yield in yfinance for {ticker}: {yield_pct:.2f}%")
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else:
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# Try to calculate from dividend history
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hist = etf.history(period="1y")
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if not hist.empty and 'Dividends' in hist.columns:
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annual_dividend = hist['Dividends'].sum()
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current_price = info.get('regularMarketPrice', 0)
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yield_pct = (annual_dividend / current_price) * 100 if current_price > 0 else 0
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logger.info(f"Calculated yield from history for {ticker}: {yield_pct:.2f}%")
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else:
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yield_pct = 0
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logger.warning(f"No yield data found for {ticker} in yfinance")
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# Get current price
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current_price = info.get('regularMarketPrice', 0)
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if current_price <= 0:
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current_price = info.get('regularMarketPreviousClose', 0)
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logger.warning(f"Using previous close price for {ticker}: {current_price}")
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etf_data = {
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"Ticker": ticker,
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"Price": current_price,
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"Yield (%)": yield_pct,
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"Risk Level": "High" # Default for high-yield ETFs
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}
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logger.info(f"yfinance data for {ticker}: {etf_data}")
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return etf_data
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except Exception as e:
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logger.error(f"Error fetching yfinance data for {ticker}: {str(e)}")
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return None
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def fetch_etf_data(tickers: List[str]) -> pd.DataFrame:
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"""
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Fetch ETF data using FMP API with yfinance fallback.
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Args:
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tickers: List of ETF tickers
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Returns:
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DataFrame with ETF data
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"""
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try:
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data = {}
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for ticker in tickers:
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if not ticker: # Skip empty tickers
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continue
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logger.info(f"Processing {ticker}")
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# Try FMP first
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etf_data = fetch_etf_data_fmp(ticker)
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# If FMP fails, try yfinance
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if etf_data is None:
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logger.info(f"Falling back to yfinance for {ticker}")
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etf_data = fetch_etf_data_yfinance(ticker)
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if etf_data is not None:
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# Validate and cap yield at a reasonable maximum (e.g., 30%)
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etf_data["Yield (%)"] = min(etf_data["Yield (%)"], 30.0)
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data[ticker] = etf_data
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logger.info(f"Final data for {ticker}: {etf_data}")
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else:
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logger.error(f"Failed to fetch data for {ticker} from both sources")
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if not data:
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st.error("No ETF data could be fetched")
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return pd.DataFrame()
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df = pd.DataFrame(data.values())
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# Validate the data
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if df.empty:
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st.error("No ETF data could be fetched")
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return pd.DataFrame()
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if (df["Price"] <= 0).any():
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st.error("Some ETFs have invalid prices")
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return pd.DataFrame()
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if (df["Yield (%)"] <= 0).any():
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st.warning("Some ETFs have zero or negative yields")
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logger.info(f"Final DataFrame:\n{df}")
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return df
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except Exception as e:
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st.error(f"Error fetching ETF data: {str(e)}")
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logger.error(f"Error in fetch_etf_data: {str(e)}")
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logger.error(traceback.format_exc())
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return pd.DataFrame()
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def run_portfolio_simulation(
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mode: str,
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target: float,
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risk_tolerance: str,
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etf_inputs: List[Dict[str, str]],
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enable_drip: bool,
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enable_erosion: bool
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) -> Tuple[pd.DataFrame, pd.DataFrame]:
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"""
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Run the portfolio simulation.
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Args:
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mode: Simulation mode ("income_target" or "capital_target")
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target: Target value (monthly income or initial capital)
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risk_tolerance: Risk tolerance level
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etf_inputs: List of ETF inputs
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enable_drip: Whether to enable dividend reinvestment
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enable_erosion: Whether to enable NAV & yield erosion
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Returns:
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Tuple of (ETF data DataFrame, Final allocation DataFrame)
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"""
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try:
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# Fetch real ETF data
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tickers = [input["ticker"] for input in etf_inputs]
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etf_data = fetch_etf_data(tickers)
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if etf_data is None or etf_data.empty:
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st.error("Failed to fetch ETF data")
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return pd.DataFrame(), pd.DataFrame()
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# Calculate allocations based on risk tolerance
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if risk_tolerance == "Conservative":
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# Higher allocation to lower yield ETFs
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sorted_data = etf_data.sort_values("Yield (%)")
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allocations = [40.0, 40.0, 20.0] # More to lower yield
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elif risk_tolerance == "Moderate":
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# Balanced allocation
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allocations = [33.33, 33.34, 33.33]
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else: # Aggressive
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# Higher allocation to higher yield ETFs
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sorted_data = etf_data.sort_values("Yield (%)", ascending=False)
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allocations = [20.0, 30.0, 50.0] # More to higher yield
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# Create final allocation DataFrame
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final_alloc = etf_data.copy()
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final_alloc["Allocation (%)"] = allocations
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if mode == "income_target":
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# Calculate required capital for income target
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monthly_income = target
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annual_income = monthly_income * 12
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# Calculate weighted average yield
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weighted_yield = (final_alloc["Allocation (%)"] * final_alloc["Yield (%)"]).sum() / 100
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# Validate weighted yield
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if weighted_yield <= 0 or weighted_yield > 30:
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st.error(f"Invalid weighted yield calculated: {weighted_yield:.2f}%")
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return pd.DataFrame(), pd.DataFrame()
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# Calculate required capital based on weighted yield
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required_capital = (annual_income / weighted_yield) * 100
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else:
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required_capital = target
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# Calculate capital allocation and income
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final_alloc["Capital Allocated ($)"] = (final_alloc["Allocation (%)"] / 100) * required_capital
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final_alloc["Shares"] = final_alloc["Capital Allocated ($)"] / final_alloc["Price"]
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final_alloc["Income Contributed ($)"] = (final_alloc["Capital Allocated ($)"] * final_alloc["Yield (%)"]) / 100
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# Apply erosion if enabled
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if enable_erosion:
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# Apply a small erosion factor to yield and price
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erosion_factor = 0.98 # 2% erosion per year
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final_alloc["Yield (%)"] = final_alloc["Yield (%)"] * erosion_factor
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final_alloc["Price"] = final_alloc["Price"] * erosion_factor
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final_alloc["Income Contributed ($)"] = (final_alloc["Capital Allocated ($)"] * final_alloc["Yield (%)"]) / 100
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# Validate final calculations
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total_capital = final_alloc["Capital Allocated ($)"].sum()
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total_income = final_alloc["Income Contributed ($)"].sum()
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effective_yield = (total_income / total_capital) * 100
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if effective_yield <= 0 or effective_yield > 30:
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st.error(f"Invalid effective yield calculated: {effective_yield:.2f}%")
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return pd.DataFrame(), pd.DataFrame()
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return etf_data, final_alloc
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except Exception as e:
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st.error(f"Error in portfolio simulation: {str(e)}")
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logger.error(f"Error in run_portfolio_simulation: {str(e)}")
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logger.error(traceback.format_exc())
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return pd.DataFrame(), pd.DataFrame()
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def portfolio_summary(final_alloc: pd.DataFrame) -> None:
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"""
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Display a summary of the portfolio allocation.
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Args:
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final_alloc: DataFrame containing the portfolio allocation
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"""
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if final_alloc is None or final_alloc.empty:
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st.warning("No portfolio data available.")
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return
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try:
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# Calculate key metrics
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total_capital = final_alloc["Capital Allocated ($)"].sum()
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total_income = final_alloc["Income Contributed ($)"].sum()
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# Calculate weighted average yield
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weighted_yield = (final_alloc["Allocation (%)"] * final_alloc["Yield (%)"]).sum() / 100
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# Display metrics in columns
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col1, col2, col3 = st.columns(3)
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with col1:
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st.metric("Total Capital", f"${total_capital:,.2f}")
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with col2:
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st.metric("Annual Income", f"${total_income:,.2f}")
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st.metric("Monthly Income", f"${total_income/12:,.2f}")
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with col3:
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st.metric("Average Yield", f"{weighted_yield:.2f}%")
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st.metric("Effective Yield", f"{(total_income/total_capital*100):.2f}%")
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# Display allocation chart
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fig = px.pie(
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final_alloc,
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values="Allocation (%)",
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names="Ticker",
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title="Portfolio Allocation by ETF",
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hover_data={
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"Ticker": True,
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"Allocation (%)": ":.2f",
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"Yield (%)": ":.2f",
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"Capital Allocated ($)": ":,.2f",
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"Income Contributed ($)": ":,.2f"
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}
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)
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st.plotly_chart(fig, use_container_width=True)
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# Display detailed allocation table
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st.subheader("Detailed Allocation")
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display_df = final_alloc.copy()
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display_df["Monthly Income"] = display_df["Income Contributed ($)"] / 12
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display_df = display_df[[
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"Ticker",
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"Allocation (%)",
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"Yield (%)",
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"Price",
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"Shares",
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"Capital Allocated ($)",
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"Monthly Income",
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"Income Contributed ($)",
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"Risk Level"
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]]
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# Format the display
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st.dataframe(
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display_df.style.format({
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"Allocation (%)": "{:.2f}%",
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"Yield (%)": "{:.2f}%",
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"Price": "${:,.2f}",
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"Shares": "{:,.4f}",
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"Capital Allocated ($)": "${:,.2f}",
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"Monthly Income": "${:,.2f}",
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"Income Contributed ($)": "${:,.2f}"
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}),
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use_container_width=True
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)
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except Exception as e:
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st.error(f"Error calculating portfolio summary: {str(e)}")
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logger.error(f"Error in portfolio_summary: {str(e)}")
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logger.error(traceback.format_exc())
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def save_portfolio(portfolio_name: str, final_alloc: pd.DataFrame, mode: str, target: float) -> bool:
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"""
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Save portfolio allocation to a JSON file.
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Args:
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portfolio_name: Name of the portfolio
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final_alloc: DataFrame containing portfolio allocation
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mode: Portfolio mode ("Income Target" or "Capital Target")
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target: Target value (income or capital)
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Returns:
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bool: True if save was successful, False otherwise
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"""
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try:
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# Create portfolios directory if it doesn't exist
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portfolios_dir = Path("portfolios")
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portfolios_dir.mkdir(exist_ok=True)
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# Prepare portfolio data
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portfolio_data = {
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"name": portfolio_name,
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"created_at": datetime.now().isoformat(),
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"mode": mode,
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"target": target,
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"allocations": []
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}
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# Convert DataFrame to list of dictionaries
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for _, row in final_alloc.iterrows():
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allocation = {
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"ticker": row["Ticker"],
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"allocation": float(row["Allocation (%)"]),
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"yield": float(row["Yield (%)"]),
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"price": float(row["Price"]),
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"risk_level": row["Risk Level"]
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}
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portfolio_data["allocations"].append(allocation)
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# Save to JSON file
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file_path = portfolios_dir / f"{portfolio_name}.json"
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with open(file_path, 'w') as f:
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json.dump(portfolio_data, f, indent=2)
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return True
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except Exception as e:
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st.error(f"Error saving portfolio: {str(e)}")
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return False
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def load_portfolio(portfolio_name: str) -> Tuple[Optional[pd.DataFrame], Optional[str], Optional[float]]:
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"""
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Load portfolio allocation from a JSON file.
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Args:
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portfolio_name: Name of the portfolio to load
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Returns:
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Tuple containing:
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- DataFrame with portfolio allocation
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- Portfolio mode
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- Target value
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"""
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try:
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# Check if portfolio exists
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file_path = Path("portfolios") / f"{portfolio_name}.json"
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if not file_path.exists():
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st.error(f"Portfolio '{portfolio_name}' not found.")
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return None, None, None
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# Load portfolio data
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with open(file_path, 'r') as f:
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portfolio_data = json.load(f)
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# Convert allocations to DataFrame
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allocations = portfolio_data["allocations"]
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df = pd.DataFrame(allocations)
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# Rename columns to match expected format
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df = df.rename(columns={
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"allocation": "Allocation (%)",
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"yield": "Yield (%)",
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"price": "Price"
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})
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return df, portfolio_data["mode"], portfolio_data["target"]
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except Exception as e:
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st.error(f"Error loading portfolio: {str(e)}")
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return None, None, None
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def list_saved_portfolios() -> List[str]:
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|
"""
|
|
List all saved portfolios.
|
|
|
|
Returns:
|
|
List of portfolio names
|
|
"""
|
|
try:
|
|
portfolios_dir = Path("portfolios")
|
|
if not portfolios_dir.exists():
|
|
return []
|
|
|
|
# Get all JSON files in the portfolios directory
|
|
portfolio_files = list(portfolios_dir.glob("*.json"))
|
|
|
|
# Extract portfolio names from filenames
|
|
portfolio_names = [f.stem for f in portfolio_files]
|
|
|
|
return sorted(portfolio_names)
|
|
|
|
except Exception as e:
|
|
st.error(f"Error listing portfolios: {str(e)}")
|
|
return []
|
|
|
|
def allocate_for_income(df: pd.DataFrame, target: float, etf_allocations: List[Dict[str, Any]]) -> pd.DataFrame:
|
|
"""
|
|
Allocate portfolio for income target.
|
|
|
|
Args:
|
|
df: DataFrame with ETF data
|
|
target: Monthly income target
|
|
etf_allocations: List of ETF allocations
|
|
|
|
Returns:
|
|
DataFrame with final allocation
|
|
"""
|
|
try:
|
|
# Create final allocation DataFrame
|
|
final_alloc = df.copy()
|
|
|
|
# Set allocations
|
|
for alloc in etf_allocations:
|
|
mask = final_alloc["Ticker"] == alloc["ticker"]
|
|
final_alloc.loc[mask, "Allocation (%)"] = alloc["allocation"]
|
|
|
|
# Calculate required capital for income target
|
|
monthly_income = target
|
|
annual_income = monthly_income * 12
|
|
avg_yield = final_alloc["Yield (%)"].mean()
|
|
required_capital = (annual_income / avg_yield) * 100
|
|
|
|
# Calculate capital allocation and income
|
|
final_alloc["Capital Allocated ($)"] = (final_alloc["Allocation (%)"] / 100) * required_capital
|
|
final_alloc["Shares"] = final_alloc["Capital Allocated ($)"] / final_alloc["Price"]
|
|
final_alloc["Income Contributed ($)"] = (final_alloc["Capital Allocated ($)"] * final_alloc["Yield (%)"]) / 100
|
|
|
|
return final_alloc
|
|
except Exception as e:
|
|
st.error(f"Error in income allocation: {str(e)}")
|
|
return None
|
|
|
|
def allocate_for_capital(df: pd.DataFrame, initial_capital: float, etf_allocations: List[Dict[str, Any]]) -> pd.DataFrame:
|
|
"""
|
|
Allocate portfolio for capital target.
|
|
|
|
Args:
|
|
df: DataFrame with ETF data
|
|
initial_capital: Initial capital amount
|
|
etf_allocations: List of ETF allocations
|
|
|
|
Returns:
|
|
DataFrame with final allocation
|
|
"""
|
|
try:
|
|
# Create final allocation DataFrame
|
|
final_alloc = df.copy()
|
|
|
|
# Set allocations
|
|
for alloc in etf_allocations:
|
|
mask = final_alloc["Ticker"] == alloc["ticker"]
|
|
final_alloc.loc[mask, "Allocation (%)"] = alloc["allocation"]
|
|
|
|
# Calculate capital allocation and income
|
|
final_alloc["Capital Allocated ($)"] = (final_alloc["Allocation (%)"] / 100) * initial_capital
|
|
final_alloc["Shares"] = final_alloc["Capital Allocated ($)"] / final_alloc["Price"]
|
|
final_alloc["Income Contributed ($)"] = (final_alloc["Capital Allocated ($)"] * final_alloc["Yield (%)"]) / 100
|
|
|
|
return final_alloc
|
|
except Exception as e:
|
|
st.error(f"Error in capital allocation: {str(e)}")
|
|
return None
|
|
|
|
def reset_simulation():
|
|
"""Reset all simulation data and state."""
|
|
st.session_state.simulation_run = False
|
|
st.session_state.df_data = None
|
|
st.session_state.final_alloc = None
|
|
st.session_state.mode = 'Capital Target'
|
|
st.session_state.target = 0
|
|
st.session_state.initial_capital = 0
|
|
st.session_state.enable_drip = False
|
|
st.session_state.enable_erosion = False
|
|
st.rerun()
|
|
|
|
def test_fmp_connection():
|
|
"""Test the FMP API connection and display status."""
|
|
try:
|
|
if not FMP_API_KEY:
|
|
return False, "No API key found"
|
|
|
|
session = get_fmp_session()
|
|
test_url = f"{FMP_BASE_URL}/profile/AAPL?apikey={FMP_API_KEY}"
|
|
response = session.get(test_url)
|
|
|
|
if response.status_code == 200:
|
|
data = response.json()
|
|
if data and isinstance(data, list) and len(data) > 0:
|
|
return True, "Connected"
|
|
return False, f"Error: {response.status_code}"
|
|
except Exception as e:
|
|
return False, f"Error: {str(e)}"
|
|
|
|
# Set page config
|
|
st.set_page_config(
|
|
page_title="ETF Portfolio Builder",
|
|
page_icon="📈",
|
|
layout="wide",
|
|
initial_sidebar_state="expanded"
|
|
)
|
|
|
|
# Initialize session state variables
|
|
if 'simulation_run' not in st.session_state:
|
|
st.session_state.simulation_run = False
|
|
if 'df_data' not in st.session_state:
|
|
st.session_state.df_data = None
|
|
if 'final_alloc' not in st.session_state:
|
|
st.session_state.final_alloc = None
|
|
if 'mode' not in st.session_state:
|
|
st.session_state.mode = 'Capital Target'
|
|
if 'target' not in st.session_state:
|
|
st.session_state.target = 0
|
|
if 'initial_capital' not in st.session_state:
|
|
st.session_state.initial_capital = 0
|
|
if 'enable_drip' not in st.session_state:
|
|
st.session_state.enable_drip = False
|
|
if 'enable_erosion' not in st.session_state:
|
|
st.session_state.enable_erosion = False
|
|
|
|
# Main title
|
|
st.title("📈 ETF Portfolio Builder")
|
|
|
|
# Sidebar for simulation parameters
|
|
with st.sidebar:
|
|
st.header("Simulation Parameters")
|
|
|
|
# Add refresh data button at the top
|
|
if st.button("🔄 Refresh Data", use_container_width=True):
|
|
st.info("Refreshing ETF data...")
|
|
# Add your data refresh logic here
|
|
st.success("Data refreshed successfully!")
|
|
|
|
# Mode selection
|
|
simulation_mode = st.radio(
|
|
"Select Simulation Mode",
|
|
["Capital Target", "Income Target"]
|
|
)
|
|
|
|
if simulation_mode == "Income Target":
|
|
monthly_target = st.number_input(
|
|
"Monthly Income Target ($)",
|
|
min_value=100.0,
|
|
max_value=100000.0,
|
|
value=1000.0,
|
|
step=100.0
|
|
)
|
|
ANNUAL_TARGET = monthly_target * 12
|
|
else:
|
|
initial_capital = st.number_input(
|
|
"Initial Capital ($)",
|
|
min_value=1000.0,
|
|
max_value=1000000.0,
|
|
value=100000.0,
|
|
step=1000.0
|
|
)
|
|
|
|
# Risk tolerance
|
|
risk_tolerance = st.select_slider(
|
|
"Risk Tolerance",
|
|
options=["Conservative", "Moderate", "Aggressive"],
|
|
value="Moderate"
|
|
)
|
|
|
|
# Additional options
|
|
st.subheader("Additional Options")
|
|
|
|
# DRIP option
|
|
enable_drip = st.radio(
|
|
"Enable Dividend Reinvestment (DRIP)",
|
|
["Yes", "No"],
|
|
index=1
|
|
)
|
|
|
|
# Erosion options
|
|
enable_erosion = st.radio(
|
|
"Enable NAV & Yield Erosion",
|
|
["Yes", "No"],
|
|
index=1
|
|
)
|
|
|
|
# ETF Selection
|
|
st.subheader("ETF Selection")
|
|
|
|
# Create a form for ETF selection
|
|
with st.form("etf_selection_form"):
|
|
# Number of ETFs
|
|
num_etfs = st.number_input("Number of ETFs", min_value=1, max_value=10, value=3, step=1)
|
|
|
|
# Create columns for ETF inputs
|
|
etf_inputs = []
|
|
for i in range(num_etfs):
|
|
ticker = st.text_input(f"ETF {i+1} Ticker", key=f"ticker_{i}")
|
|
etf_inputs.append({"ticker": ticker})
|
|
|
|
# Submit button
|
|
submitted = st.form_submit_button("Run Portfolio Simulation", type="primary")
|
|
|
|
if submitted:
|
|
try:
|
|
# Store parameters in session state
|
|
st.session_state.mode = simulation_mode
|
|
st.session_state.enable_drip = enable_drip == "Yes"
|
|
st.session_state.enable_erosion = enable_erosion == "Yes"
|
|
|
|
if simulation_mode == "Income Target":
|
|
st.session_state.target = monthly_target
|
|
else:
|
|
st.session_state.target = initial_capital
|
|
st.session_state.initial_capital = initial_capital
|
|
|
|
# Run simulation
|
|
df_data, final_alloc = run_portfolio_simulation(
|
|
simulation_mode.lower().replace(" ", "_"),
|
|
st.session_state.target,
|
|
risk_tolerance,
|
|
etf_inputs,
|
|
st.session_state.enable_drip,
|
|
st.session_state.enable_erosion
|
|
)
|
|
|
|
# Store results in session state
|
|
st.session_state.simulation_run = True
|
|
st.session_state.df_data = df_data
|
|
st.session_state.final_alloc = final_alloc
|
|
|
|
st.success("Portfolio simulation completed!")
|
|
st.rerun()
|
|
|
|
except Exception as e:
|
|
st.error(f"Error running simulation: {str(e)}")
|
|
|
|
# Add reset simulation button at the bottom of sidebar
|
|
if st.button("🔄 Reset Simulation", use_container_width=True, type="secondary"):
|
|
reset_simulation()
|
|
|
|
# Add FMP connection status to the navigation bar
|
|
st.sidebar.markdown("---")
|
|
st.sidebar.subheader("FMP API Status")
|
|
connection_status, message = test_fmp_connection()
|
|
if connection_status:
|
|
st.sidebar.success(f"✅ FMP API: {message}")
|
|
else:
|
|
st.sidebar.error(f"❌ FMP API: {message}")
|
|
|
|
# Display results and interactive allocation adjustment UI after simulation is run
|
|
if st.session_state.simulation_run and st.session_state.df_data is not None:
|
|
df = st.session_state.df_data
|
|
final_alloc = st.session_state.final_alloc if hasattr(st.session_state, 'final_alloc') else None
|
|
|
|
# Create tabs for better organization
|
|
tab1, tab2, tab3, tab4, tab5 = st.tabs(["📈 Portfolio Overview", "📊 DRIP Forecast", "📉 Erosion Risk Assessment", "🤖 AI Suggestions", "📊 ETF Details"])
|
|
|
|
with tab1:
|
|
st.subheader("💰 Portfolio Summary")
|
|
portfolio_summary(final_alloc)
|
|
|
|
# Display mode-specific information
|
|
if st.session_state.mode == "Income Target":
|
|
st.info(f"🎯 **Income Target Mode**: You need ${final_alloc['Capital Allocated ($)'].sum():,.2f} to generate ${monthly_target:,.2f} in monthly income (${ANNUAL_TARGET:,.2f} annually).")
|
|
else:
|
|
annual_income = final_alloc["Income Contributed ($)"].sum()
|
|
monthly_income = annual_income / 12
|
|
st.info(f"💲 **Capital Investment Mode**: Your ${initial_capital:,.2f} investment generates ${monthly_income:,.2f} in monthly income (${annual_income:,.2f} annually).")
|
|
|
|
# Add save/load section
|
|
st.subheader("💾 Save/Load Portfolio")
|
|
|
|
# Create two columns for save and load
|
|
save_col, load_col = st.columns(2)
|
|
|
|
with save_col:
|
|
st.write("Save current portfolio")
|
|
portfolio_name = st.text_input("Portfolio Name", key="save_portfolio_name")
|
|
if st.button("Save Portfolio", key="save_portfolio"):
|
|
if portfolio_name:
|
|
if save_portfolio(portfolio_name, final_alloc,
|
|
st.session_state.mode,
|
|
st.session_state.target):
|
|
st.success(f"Portfolio '{portfolio_name}' saved successfully!")
|
|
else:
|
|
st.warning("Please enter a portfolio name.")
|
|
|
|
with load_col:
|
|
st.write("Load saved portfolio")
|
|
if st.button("Show Saved Portfolios", key="show_portfolios"):
|
|
saved_portfolios = list_saved_portfolios()
|
|
if saved_portfolios:
|
|
selected_portfolio = st.selectbox("Select Portfolio", saved_portfolios, key="load_portfolio")
|
|
if st.button("Load Portfolio", key="load_portfolio_btn"):
|
|
loaded_df, loaded_mode, loaded_target = load_portfolio(selected_portfolio)
|
|
if loaded_df is not None:
|
|
st.session_state.final_alloc = loaded_df
|
|
st.session_state.mode = loaded_mode
|
|
st.session_state.target = loaded_target
|
|
st.success(f"Portfolio '{selected_portfolio}' loaded successfully!")
|
|
st.rerun()
|
|
else:
|
|
st.info("No saved portfolios found.")
|
|
|
|
# Display full detailed allocation table
|
|
st.subheader("📊 Capital Allocation Details")
|
|
|
|
# Format currencies for better readability
|
|
display_df = final_alloc.copy()
|
|
# Calculate shares for each ETF
|
|
display_df["Shares"] = display_df["Capital Allocated ($)"] / display_df["Price"]
|
|
display_df["Price Per Share"] = display_df["Price"].apply(lambda x: f"${x:,.2f}")
|
|
display_df["Capital Allocated ($)"] = display_df["Capital Allocated ($)"].apply(lambda x: f"${x:,.2f}")
|
|
display_df["Income Contributed ($)"] = display_df["Income Contributed ($)"].apply(lambda x: f"${x:,.2f}")
|
|
display_df["Yield (%)"] = display_df["Yield (%)"].apply(lambda x: f"{x:.2f}%")
|
|
display_df["Shares"] = display_df["Shares"].apply(lambda x: f"{x:,.4f}")
|
|
|
|
# Create a form for the allocation table
|
|
with st.form("allocation_form"):
|
|
# Create an editable DataFrame
|
|
edited_df = st.data_editor(
|
|
display_df[["Ticker", "Allocation (%)", "Yield (%)", "Price Per Share", "Risk Level"]],
|
|
column_config={
|
|
"Ticker": st.column_config.TextColumn("Ticker", disabled=True),
|
|
"Allocation (%)": st.column_config.NumberColumn(
|
|
"Allocation (%)",
|
|
min_value=0.0,
|
|
max_value=100.0,
|
|
step=0.1,
|
|
format="%.1f"
|
|
),
|
|
"Yield (%)": st.column_config.TextColumn("Yield (%)", disabled=True),
|
|
"Price Per Share": st.column_config.TextColumn("Price Per Share", disabled=True),
|
|
"Risk Level": st.column_config.TextColumn("Risk Level", disabled=True)
|
|
},
|
|
hide_index=True,
|
|
use_container_width=True
|
|
)
|
|
|
|
# Calculate total allocation
|
|
total_alloc = edited_df["Allocation (%)"].sum()
|
|
|
|
# Display total allocation with color coding
|
|
if abs(total_alloc - 100) <= 0.1:
|
|
st.metric("Total Allocation (%)", f"{total_alloc:.2f}", delta=None)
|
|
else:
|
|
st.metric("Total Allocation (%)", f"{total_alloc:.2f}",
|
|
delta=f"{total_alloc - 100:.2f}",
|
|
delta_color="off")
|
|
|
|
if abs(total_alloc - 100) > 0.1:
|
|
st.warning("Total allocation should be 100%")
|
|
|
|
# Create columns for quick actions
|
|
col1, col2, col3 = st.columns(3)
|
|
|
|
with col1:
|
|
equal_weight = st.form_submit_button("Equal Weight", use_container_width=True)
|
|
|
|
with col2:
|
|
focus_income = st.form_submit_button("Focus on Income", use_container_width=True)
|
|
|
|
with col3:
|
|
focus_capital = st.form_submit_button("Focus on Capital", use_container_width=True)
|
|
|
|
# Submit button for manual edits
|
|
submitted = st.form_submit_button("Update Allocations",
|
|
disabled=abs(total_alloc - 100) > 0.1,
|
|
type="primary",
|
|
use_container_width=True)
|
|
|
|
# Handle form submission
|
|
if submitted:
|
|
try:
|
|
# Convert the edited allocations to a dictionary
|
|
new_allocations = {row["Ticker"]: float(row["Allocation (%)"]) for _, row in edited_df.iterrows()}
|
|
|
|
# Convert to the format expected by allocation functions
|
|
etf_allocations = [{"ticker": ticker, "allocation": alloc} for ticker, alloc in new_allocations.items()]
|
|
|
|
# Get the mode and target from session state
|
|
mode = st.session_state.mode
|
|
target = st.session_state.target
|
|
initial_capital = st.session_state.initial_capital
|
|
|
|
# Use the same allocation functions as the main navigation
|
|
if mode == "Income Target":
|
|
final_alloc = allocate_for_income(df, target, etf_allocations)
|
|
else: # Capital Target
|
|
final_alloc = allocate_for_capital(df, initial_capital, etf_allocations)
|
|
|
|
if final_alloc is not None:
|
|
st.session_state.final_alloc = final_alloc
|
|
st.success("Portfolio updated with new allocations!")
|
|
st.rerun()
|
|
else:
|
|
st.error("Failed to update portfolio. Please try again.")
|
|
except Exception as e:
|
|
st.error(f"Error updating allocations: {str(e)}")
|
|
|
|
# Handle quick actions
|
|
if equal_weight:
|
|
try:
|
|
# Calculate equal weight allocation
|
|
num_etfs = len(edited_df)
|
|
equal_allocation = 100 / num_etfs
|
|
|
|
# Create new allocations in the format expected by allocation functions
|
|
etf_allocations = [{"ticker": row["Ticker"], "allocation": equal_allocation} for _, row in edited_df.iterrows()]
|
|
|
|
# Get the mode and target from session state
|
|
mode = st.session_state.mode
|
|
target = st.session_state.target
|
|
initial_capital = st.session_state.initial_capital
|
|
|
|
# Use the same allocation functions as the main navigation
|
|
if mode == "Income Target":
|
|
final_alloc = allocate_for_income(df, target, etf_allocations)
|
|
else: # Capital Target
|
|
final_alloc = allocate_for_capital(df, initial_capital, etf_allocations)
|
|
|
|
if final_alloc is not None:
|
|
st.session_state.final_alloc = final_alloc
|
|
st.success("Portfolio adjusted to equal weight!")
|
|
st.rerun()
|
|
except Exception as e:
|
|
st.error(f"Error applying equal weight: {str(e)}")
|
|
|
|
elif focus_income:
|
|
try:
|
|
# Sort by yield and adjust allocations
|
|
sorted_alloc = edited_df.sort_values("Yield (%)", ascending=False)
|
|
total_yield = sorted_alloc["Yield (%)"].str.rstrip('%').astype('float').sum()
|
|
|
|
# Calculate new allocations based on yield
|
|
etf_allocations = []
|
|
for _, row in sorted_alloc.iterrows():
|
|
yield_val = float(row["Yield (%)"].rstrip('%'))
|
|
allocation = (yield_val / total_yield) * 100
|
|
etf_allocations.append({"ticker": row["Ticker"], "allocation": allocation})
|
|
|
|
# Get the mode and target from session state
|
|
mode = st.session_state.mode
|
|
target = st.session_state.target
|
|
initial_capital = st.session_state.initial_capital
|
|
|
|
# Use the same allocation functions as the main navigation
|
|
if mode == "Income Target":
|
|
final_alloc = allocate_for_income(df, target, etf_allocations)
|
|
else: # Capital Target
|
|
final_alloc = allocate_for_capital(df, initial_capital, etf_allocations)
|
|
|
|
if final_alloc is not None:
|
|
st.session_state.final_alloc = final_alloc
|
|
st.success("Portfolio adjusted to focus on income!")
|
|
st.rerun()
|
|
except Exception as e:
|
|
st.error(f"Error focusing on income: {str(e)}")
|
|
|
|
elif focus_capital:
|
|
try:
|
|
# Calculate equal weight allocation (same as equal weight)
|
|
num_etfs = len(edited_df)
|
|
equal_allocation = 100 / num_etfs
|
|
|
|
# Create new allocations in the format expected by allocation functions
|
|
etf_allocations = [{"ticker": row["Ticker"], "allocation": equal_allocation} for _, row in edited_df.iterrows()]
|
|
|
|
# Get the mode and target from session state
|
|
mode = st.session_state.mode
|
|
target = st.session_state.target
|
|
initial_capital = st.session_state.initial_capital
|
|
|
|
# Use the same allocation functions as the main navigation
|
|
if mode == "Income Target":
|
|
final_alloc = allocate_for_income(df, target, etf_allocations)
|
|
else: # Capital Target
|
|
final_alloc = allocate_for_capital(df, initial_capital, etf_allocations)
|
|
|
|
if final_alloc is not None:
|
|
st.session_state.final_alloc = final_alloc
|
|
st.success("Portfolio adjusted to focus on capital!")
|
|
st.rerun()
|
|
except Exception as e:
|
|
st.error(f"Error focusing on capital: {str(e)}") |