2264 lines
94 KiB
Python
2264 lines
94 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, timedelta
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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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import re
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# Load environment variables
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load_dotenv(override=True) # Force reload of environment variables
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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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# Global settings
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USE_FMP_API = True # Default to using FMP API if available
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# FMP API configuration
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FMP_API_KEY = os.getenv('FMP_API_KEY')
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if not FMP_API_KEY:
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logger.warning("FMP_API_KEY not found in environment variables")
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logger.warning("Current environment variables: %s", dict(os.environ))
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logger.warning("Current working directory: %s", os.getcwd())
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logger.warning("Files in current directory: %s", os.listdir('.'))
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if os.path.exists('.env'):
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logger.warning(".env file exists")
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with open('.env', 'r') as f:
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logger.warning("Contents of .env file: %s", f.read())
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else:
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logger.warning(".env file does not exist")
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else:
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logger.info("FMP_API_KEY loaded successfully")
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# Mask the API key for security in logs
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masked_key = FMP_API_KEY[:4] + '*' * (len(FMP_API_KEY) - 8) + FMP_API_KEY[-4:]
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logger.info("FMP_API_KEY (masked): %s", masked_key)
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FMP_BASE_URL = "https://financialmodelingprep.com/api/v3"
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def test_fmp_data_fetching():
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"""Test FMP API data fetching with detailed logging."""
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try:
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logger.info("=== Starting FMP API Test ===")
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logger.info(f"FMP API Key available: {bool(FMP_API_KEY)}")
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logger.info(f"FMP API enabled: {USE_FMP_API}")
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# Test a few high-yield ETFs
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test_tickers = ["JEPI", "JEPQ", "QYLD"]
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for ticker in test_tickers:
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logger.info(f"\nTesting {ticker}:")
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data = fetch_etf_data_fmp(ticker)
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if data:
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logger.info(f"Successfully fetched data for {ticker}")
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logger.info(f"Data source: {data.get('data_source', 'Not specified')}")
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logger.info(f"Raw data: {data.get('raw_data', 'No raw data')}")
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else:
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logger.error(f"Failed to fetch data for {ticker}")
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logger.info("=== FMP API Test Complete ===")
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except Exception as e:
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logger.error(f"Error in FMP API test: {str(e)}")
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logger.error(traceback.format_exc())
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# High-yield ETFs reference data
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HIGH_YIELD_ETFS = {
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"MSTY": {"expected_yield": 125.0, "frequency": "Monthly"},
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"SMCY": {"expected_yield": 100.0, "frequency": "Monthly"},
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"TSLY": {"expected_yield": 85.0, "frequency": "Monthly"},
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"NVDY": {"expected_yield": 75.0, "frequency": "Monthly"},
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"ULTY": {"expected_yield": 70.0, "frequency": "Monthly"},
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"JEPQ": {"expected_yield": 9.5, "frequency": "Monthly"},
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"JEPI": {"expected_yield": 7.8, "frequency": "Monthly"},
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"XYLD": {"expected_yield": 12.0, "frequency": "Monthly"},
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"QYLD": {"expected_yield": 12.0, "frequency": "Monthly"},
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"RYLD": {"expected_yield": 12.0, "frequency": "Monthly"}
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}
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def calculate_etf_metrics(ticker: str, price_data: pd.DataFrame, dividend_data: pd.DataFrame) -> Dict[str, Any]:
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"""
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Calculate ETF metrics based on available data.
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Args:
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ticker: ETF ticker
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price_data: DataFrame with price history
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dividend_data: DataFrame with dividend history
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Returns:
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Dictionary with calculated metrics
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"""
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metrics = {
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"Ticker": ticker,
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"Yield (%)": 0.0,
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"Price": 0.0,
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"volatility": 0.0,
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"sharpe_ratio": 0.0,
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"sortino_ratio": 0.0,
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"correlation": 0.0,
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"payout_ratio": 0.0,
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"score": 0.0,
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"Risk Level": "Unknown",
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"missing_metrics": []
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}
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try:
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# Get current price from price data
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if not price_data.empty:
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metrics["Price"] = price_data["close"].iloc[-1]
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else:
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metrics["missing_metrics"].append("Price")
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# Calculate yield if dividend data is available
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if not dividend_data.empty and metrics["Price"] > 0:
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# Convert date column to datetime if it's not already
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dividend_data["date"] = pd.to_datetime(dividend_data["date"])
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# Get dividends from the last 12 months
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one_year_ago = pd.Timestamp.now() - pd.Timedelta(days=365)
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recent_dividends = dividend_data[dividend_data["date"] >= one_year_ago]
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if not recent_dividends.empty:
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# Calculate TTM dividend
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ttm_dividend = recent_dividends["dividend"].sum()
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# Calculate annual yield
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metrics["Yield (%)"] = (ttm_dividend / metrics["Price"]) * 100
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logger.info(f"Calculated yield for {ticker}: {metrics['Yield (%)']:.2f}% (TTM dividend: ${ttm_dividend:.2f}, Price: ${metrics['Price']:.2f})")
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else:
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logger.warning(f"No recent dividends found for {ticker}")
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metrics["missing_metrics"].append("Yield (%)")
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else:
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metrics["missing_metrics"].append("Yield (%)")
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# Calculate volatility if price data is available
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if len(price_data) > 1:
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returns = price_data["close"].pct_change().dropna()
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metrics["volatility"] = returns.std() * np.sqrt(252) * 100 # Annualized volatility
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else:
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metrics["missing_metrics"].append("volatility")
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# Calculate Sharpe ratio if we have returns and risk-free rate
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if len(price_data) > 1:
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risk_free_rate = 0.05 # Assuming 5% risk-free rate
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excess_returns = returns - (risk_free_rate / 252)
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if excess_returns.std() != 0:
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metrics["sharpe_ratio"] = (excess_returns.mean() / excess_returns.std()) * np.sqrt(252)
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else:
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metrics["missing_metrics"].append("sharpe_ratio")
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# Calculate Sortino ratio if we have returns
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if len(price_data) > 1:
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downside_returns = returns[returns < 0]
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if len(downside_returns) > 0 and downside_returns.std() != 0:
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metrics["sortino_ratio"] = (returns.mean() / downside_returns.std()) * np.sqrt(252)
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else:
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metrics["missing_metrics"].append("sortino_ratio")
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# Categorize risk based on available metrics
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metrics["Risk Level"] = categorize_etf_risk(metrics)
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# Calculate overall score
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metrics["score"] = calculate_etf_score(metrics)
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logger.info(f"Calculated metrics for {ticker}: {metrics}")
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return metrics
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except Exception as e:
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logger.error(f"Error calculating metrics for {ticker}: {str(e)}")
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logger.error(traceback.format_exc())
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return metrics
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def categorize_etf_risk(metrics: Dict[str, Any]) -> str:
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"""
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Categorize ETF risk based on available metrics.
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Args:
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metrics: Dictionary with ETF metrics
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Returns:
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Risk category: "Low", "Medium", or "High"
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"""
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try:
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# Initialize risk score
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risk_score = 0
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available_metrics = 0
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# Yield-based risk (higher yield = higher risk)
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if "Yield (%)" not in metrics["missing_metrics"]:
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if metrics["Yield (%)"] > 10:
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risk_score += 3
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elif metrics["Yield (%)"] > 6:
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risk_score += 2
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else:
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risk_score += 1
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available_metrics += 1
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# Volatility-based risk
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if "volatility" not in metrics["missing_metrics"]:
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if metrics["volatility"] > 20:
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risk_score += 3
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elif metrics["volatility"] > 15:
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risk_score += 2
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else:
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risk_score += 1
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available_metrics += 1
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# Sharpe ratio-based risk (lower Sharpe = higher risk)
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if "sharpe_ratio" not in metrics["missing_metrics"]:
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if metrics["sharpe_ratio"] < 0.5:
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risk_score += 3
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elif metrics["sharpe_ratio"] < 1.0:
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risk_score += 2
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else:
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risk_score += 1
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available_metrics += 1
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# Sortino ratio-based risk (lower Sortino = higher risk)
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if "sortino_ratio" not in metrics["missing_metrics"]:
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if metrics["sortino_ratio"] < 0.5:
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risk_score += 3
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elif metrics["sortino_ratio"] < 1.0:
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risk_score += 2
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else:
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risk_score += 1
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available_metrics += 1
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# Calculate average risk score
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if available_metrics > 0:
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avg_risk_score = risk_score / available_metrics
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if avg_risk_score > 2.5:
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return "High"
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elif avg_risk_score > 1.5:
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return "Medium"
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else:
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return "Low"
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# If no metrics available, use yield as fallback
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if metrics["Yield (%)"] > 10:
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return "High"
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elif metrics["Yield (%)"] > 6:
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return "Medium"
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else:
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return "Low"
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except Exception as e:
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logger.error(f"Error categorizing ETF risk: {str(e)}")
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return "Unknown"
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def calculate_etf_score(metrics: Dict[str, Any]) -> float:
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"""
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Calculate overall ETF score based on available metrics.
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Args:
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metrics: Dictionary with ETF metrics
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Returns:
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Overall score (0-100)
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"""
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try:
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score = 0
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available_metrics = 0
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# Yield score (0-25 points)
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if "Yield (%)" not in metrics["missing_metrics"]:
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if metrics["Yield (%)"] > 10:
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score += 25
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elif metrics["Yield (%)"] > 6:
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score += 20
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elif metrics["Yield (%)"] > 3:
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score += 15
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else:
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score += 10
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available_metrics += 1
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# Volatility score (0-25 points)
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if "volatility" not in metrics["missing_metrics"]:
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if metrics["volatility"] < 10:
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score += 25
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elif metrics["volatility"] < 15:
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score += 20
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elif metrics["volatility"] < 20:
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score += 15
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else:
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score += 10
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available_metrics += 1
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# Sharpe ratio score (0-25 points)
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if "sharpe_ratio" not in metrics["missing_metrics"]:
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if metrics["sharpe_ratio"] > 1.5:
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score += 25
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elif metrics["sharpe_ratio"] > 1.0:
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score += 20
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elif metrics["sharpe_ratio"] > 0.5:
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score += 15
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else:
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score += 10
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available_metrics += 1
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# Sortino ratio score (0-25 points)
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if "sortino_ratio" not in metrics["missing_metrics"]:
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if metrics["sortino_ratio"] > 1.5:
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score += 25
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elif metrics["sortino_ratio"] > 1.0:
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score += 20
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elif metrics["sortino_ratio"] > 0.5:
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score += 15
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else:
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score += 10
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available_metrics += 1
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# Calculate final score
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if available_metrics > 0:
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return score / available_metrics
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return 0
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except Exception as e:
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logger.error(f"Error calculating ETF score: {str(e)}")
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return 0
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def calculate_correlation_matrix(price_data_dict: Dict[str, pd.DataFrame]) -> pd.DataFrame:
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"""
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Calculate correlation matrix between ETFs.
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Args:
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price_data_dict: Dictionary of price DataFrames for each ETF
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||
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Returns:
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DataFrame with correlation matrix
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"""
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try:
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# Create a DataFrame with returns for all ETFs
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returns_df = pd.DataFrame()
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for ticker, price_data in price_data_dict.items():
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if len(price_data) > 1:
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returns = price_data["close"].pct_change().dropna()
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returns_df[ticker] = returns
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if returns_df.empty:
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logger.warning("No valid price data for correlation calculation")
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return pd.DataFrame()
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# Calculate correlation matrix
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corr_matrix = returns_df.corr()
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logger.info(f"Correlation matrix calculated:\n{corr_matrix}")
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return corr_matrix
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except Exception as e:
|
||
logger.error(f"Error calculating correlation matrix: {str(e)}")
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logger.error(traceback.format_exc())
|
||
return pd.DataFrame()
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||
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def calculate_etf_risk_score(etf: Dict[str, Any]) -> float:
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"""
|
||
Calculate a comprehensive risk score for an ETF based on multiple metrics.
|
||
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||
Args:
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||
etf: Dictionary containing ETF metrics
|
||
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||
Returns:
|
||
float: Risk score (0-100, higher means higher risk)
|
||
"""
|
||
try:
|
||
score = 0
|
||
metrics_used = 0
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||
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# Primary Metrics (60% of total score)
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||
# 1. Volatility (20%)
|
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if 'volatility' in etf:
|
||
volatility = etf['volatility']
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if volatility < 10:
|
||
score += 20
|
||
elif volatility < 15:
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score += 15
|
||
elif volatility < 20:
|
||
score += 10
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||
else:
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||
score += 5
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||
metrics_used += 1
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||
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# 2. Yield (20%)
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if 'yield' in etf:
|
||
yield_value = etf['yield']
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if yield_value < 3:
|
||
score += 5
|
||
elif yield_value < 6:
|
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score += 10
|
||
elif yield_value < 10:
|
||
score += 15
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else:
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score += 20
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metrics_used += 1
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||
|
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# 3. Sharpe/Sortino Ratio (20%)
|
||
if 'sharpe_ratio' in etf:
|
||
sharpe = etf['sharpe_ratio']
|
||
if sharpe > 1.5:
|
||
score += 5
|
||
elif sharpe > 1.0:
|
||
score += 10
|
||
elif sharpe > 0.8:
|
||
score += 15
|
||
else:
|
||
score += 20
|
||
metrics_used += 1
|
||
|
||
# Secondary Metrics (40% of total score)
|
||
# 1. Dividend Growth (10%)
|
||
if 'dividend_growth' in etf:
|
||
growth = etf['dividend_growth']
|
||
if growth > 10:
|
||
score += 5
|
||
elif growth > 5:
|
||
score += 7
|
||
elif growth > 0:
|
||
score += 10
|
||
else:
|
||
score += 15
|
||
metrics_used += 1
|
||
|
||
# 2. Payout Ratio (10%)
|
||
if 'payout_ratio' in etf:
|
||
ratio = etf['payout_ratio']
|
||
if ratio < 40:
|
||
score += 5
|
||
elif ratio < 60:
|
||
score += 7
|
||
elif ratio < 80:
|
||
score += 10
|
||
else:
|
||
score += 15
|
||
metrics_used += 1
|
||
|
||
# 3. Expense Ratio (10%)
|
||
if 'expense_ratio' in etf:
|
||
ratio = etf['expense_ratio']
|
||
if ratio < 0.2:
|
||
score += 5
|
||
elif ratio < 0.4:
|
||
score += 7
|
||
elif ratio < 0.6:
|
||
score += 10
|
||
else:
|
||
score += 15
|
||
metrics_used += 1
|
||
|
||
# 4. AUM/Volume (10%)
|
||
if 'aum' in etf:
|
||
aum = etf['aum']
|
||
if aum > 5e9: # > $5B
|
||
score += 5
|
||
elif aum > 1e9: # > $1B
|
||
score += 7
|
||
elif aum > 500e6: # > $500M
|
||
score += 10
|
||
else:
|
||
score += 15
|
||
metrics_used += 1
|
||
|
||
# Normalize score based on available metrics
|
||
if metrics_used > 0:
|
||
return score / metrics_used
|
||
return 50 # Default middle score if no metrics available
|
||
|
||
except Exception as e:
|
||
logger.error(f"Error calculating ETF risk score: {str(e)}")
|
||
return 50
|
||
|
||
def optimize_portfolio_allocation(etf_metrics: List[Dict[str, Any]], risk_tolerance: str, correlation_matrix: pd.DataFrame) -> List[Dict[str, Any]]:
|
||
"""
|
||
Optimize portfolio allocation based on risk tolerance and ETF metrics.
|
||
|
||
Args:
|
||
etf_metrics: List of ETF metrics dictionaries
|
||
risk_tolerance: Risk tolerance level ("Conservative", "Moderate", "Aggressive")
|
||
correlation_matrix: Correlation matrix between ETFs
|
||
|
||
Returns:
|
||
List of dictionaries with ETF tickers and their allocations
|
||
"""
|
||
try:
|
||
logger.info(f"Optimizing portfolio allocation for {risk_tolerance} risk tolerance")
|
||
logger.info(f"ETF metrics: {etf_metrics}")
|
||
|
||
# Sort ETFs by yield (higher yield = higher risk)
|
||
sorted_etfs = sorted(etf_metrics, key=lambda x: x.get('Yield (%)', 0), reverse=True)
|
||
logger.info(f"Sorted ETFs by yield: {[etf['Ticker'] for etf in sorted_etfs]}")
|
||
|
||
# Calculate base allocations based on risk tolerance
|
||
num_etfs = len(sorted_etfs)
|
||
if num_etfs == 0:
|
||
return []
|
||
|
||
if risk_tolerance == "Conservative":
|
||
# For conservative, allocate more to lower yielding ETFs
|
||
# This naturally requires more capital for the same income
|
||
base_allocations = []
|
||
remaining_alloc = 100
|
||
for i in range(num_etfs):
|
||
if i < num_etfs - 1:
|
||
# Allocate more to lower yielding ETFs
|
||
alloc = remaining_alloc * 0.4 # 40% of remaining
|
||
base_allocations.append(alloc)
|
||
remaining_alloc -= alloc
|
||
else:
|
||
# Last ETF gets remaining allocation
|
||
base_allocations.append(remaining_alloc)
|
||
|
||
elif risk_tolerance == "Moderate":
|
||
# For moderate, allocate more to middle yielding ETFs
|
||
# This naturally requires medium capital for the same income
|
||
base_allocations = []
|
||
remaining_alloc = 100
|
||
for i in range(num_etfs):
|
||
if i < num_etfs - 1:
|
||
# Allocate more to middle yielding ETFs
|
||
alloc = remaining_alloc * 0.5 # 50% of remaining
|
||
base_allocations.append(alloc)
|
||
remaining_alloc -= alloc
|
||
else:
|
||
# Last ETF gets remaining allocation
|
||
base_allocations.append(remaining_alloc)
|
||
|
||
else: # Aggressive
|
||
# For aggressive, allocate more to higher yielding ETFs
|
||
# This naturally requires less capital for the same income
|
||
base_allocations = []
|
||
remaining_alloc = 100
|
||
for i in range(num_etfs):
|
||
if i < num_etfs - 1:
|
||
# Allocate more to higher yielding ETFs
|
||
alloc = remaining_alloc * 0.6 # 60% of remaining
|
||
base_allocations.append(alloc)
|
||
remaining_alloc -= alloc
|
||
else:
|
||
# Last ETF gets remaining allocation
|
||
base_allocations.append(remaining_alloc)
|
||
|
||
# Create final allocation list
|
||
final_allocations = []
|
||
for etf, allocation in zip(sorted_etfs, base_allocations):
|
||
final_allocations.append({
|
||
"ticker": etf["Ticker"],
|
||
"allocation": allocation # Already in percentage
|
||
})
|
||
|
||
logger.info(f"Final allocations: {final_allocations}")
|
||
return final_allocations
|
||
|
||
except Exception as e:
|
||
logger.error(f"Error in optimize_portfolio_allocation: {str(e)}")
|
||
logger.error(traceback.format_exc())
|
||
return []
|
||
|
||
def adjust_allocations_for_correlation(
|
||
allocations: Dict[str, float],
|
||
correlation_matrix: pd.DataFrame
|
||
) -> Dict[str, float]:
|
||
"""
|
||
Adjust allocations to reduce correlation between ETFs.
|
||
|
||
Args:
|
||
allocations: Dictionary with current allocations
|
||
correlation_matrix: Correlation matrix between ETFs
|
||
|
||
Returns:
|
||
Dictionary with adjusted allocations
|
||
"""
|
||
try:
|
||
adjusted_allocations = allocations.copy()
|
||
|
||
# Get highly correlated pairs (correlation > 0.7)
|
||
high_corr_pairs = []
|
||
for i in range(len(correlation_matrix.columns)):
|
||
for j in range(i + 1, len(correlation_matrix.columns)):
|
||
ticker1 = correlation_matrix.columns[i]
|
||
ticker2 = correlation_matrix.columns[j]
|
||
if abs(correlation_matrix.iloc[i, j]) > 0.7:
|
||
high_corr_pairs.append((ticker1, ticker2))
|
||
|
||
# Adjust allocations for highly correlated pairs
|
||
for ticker1, ticker2 in high_corr_pairs:
|
||
if ticker1 in adjusted_allocations and ticker2 in adjusted_allocations:
|
||
# Reduce allocation to the ETF with lower score
|
||
if adjusted_allocations[ticker1] > adjusted_allocations[ticker2]:
|
||
reduction = adjusted_allocations[ticker1] * 0.1 # Reduce by 10%
|
||
adjusted_allocations[ticker1] -= reduction
|
||
adjusted_allocations[ticker2] += reduction
|
||
else:
|
||
reduction = adjusted_allocations[ticker2] * 0.1 # Reduce by 10%
|
||
adjusted_allocations[ticker2] -= reduction
|
||
adjusted_allocations[ticker1] += reduction
|
||
|
||
logger.info(f"Adjusted allocations for correlation: {adjusted_allocations}")
|
||
return adjusted_allocations
|
||
|
||
except Exception as e:
|
||
logger.error(f"Error adjusting allocations for correlation: {str(e)}")
|
||
logger.error(traceback.format_exc())
|
||
return allocations
|
||
|
||
def get_fmp_session():
|
||
"""Create a session with retry logic for FMP API calls."""
|
||
session = requests.Session()
|
||
retries = Retry(total=3, backoff_factor=0.5)
|
||
session.mount('https://', HTTPAdapter(max_retries=retries))
|
||
return session
|
||
|
||
def fetch_etf_data_fmp(ticker: str) -> Optional[Dict[str, Any]]:
|
||
"""
|
||
Fetch ETF data from Financial Modeling Prep API.
|
||
|
||
Args:
|
||
ticker: ETF ticker symbol
|
||
|
||
Returns:
|
||
Dictionary with ETF data or None if failed
|
||
"""
|
||
try:
|
||
if not FMP_API_KEY:
|
||
logger.warning("FMP API key not configured in environment variables")
|
||
st.warning("FMP API key not found in environment variables. Some features may be limited.")
|
||
return None
|
||
|
||
session = get_fmp_session()
|
||
|
||
# Get profile data for current price
|
||
profile_url = f"{FMP_BASE_URL}/profile/{ticker}?apikey={FMP_API_KEY}"
|
||
logger.info(f"[FMP API] Making profile request to: {profile_url}")
|
||
profile_response = session.get(profile_url)
|
||
st.session_state.api_calls += 1
|
||
logger.info(f"[FMP API] Profile response status: {profile_response.status_code}")
|
||
logger.info(f"[FMP API] Profile response content: {profile_response.text[:500]}...") # Log first 500 chars
|
||
|
||
if profile_response.status_code != 200:
|
||
logger.error(f"[FMP API] Error for {ticker}: {profile_response.status_code}")
|
||
logger.error(f"[FMP API] Response content: {profile_response.text}")
|
||
return None
|
||
|
||
profile_data = profile_response.json()
|
||
logger.info(f"[FMP API] Profile data for {ticker}: {profile_data}")
|
||
|
||
if not profile_data or not isinstance(profile_data, list) or len(profile_data) == 0:
|
||
logger.warning(f"[FMP API] No profile data found for {ticker}")
|
||
return None
|
||
|
||
profile = profile_data[0]
|
||
current_price = float(profile.get('price', 0))
|
||
if current_price <= 0:
|
||
logger.error(f"[FMP API] Invalid price for {ticker}: {current_price}")
|
||
return None
|
||
|
||
# Get dividend history
|
||
dividend_url = f"{FMP_BASE_URL}/historical-price-full/stock_dividend/{ticker}?apikey={FMP_API_KEY}"
|
||
logger.info(f"[FMP API] Making dividend request to: {dividend_url}")
|
||
dividend_response = session.get(dividend_url)
|
||
st.session_state.api_calls += 1
|
||
logger.info(f"[FMP API] Dividend response status: {dividend_response.status_code}")
|
||
logger.info(f"[FMP API] Dividend response content: {dividend_response.text[:500]}...") # Log first 500 chars
|
||
|
||
if dividend_response.status_code != 200:
|
||
logger.error(f"[FMP API] Error for dividend data: {dividend_response.status_code}")
|
||
logger.error(f"[FMP API] Response content: {dividend_response.text}")
|
||
return None
|
||
|
||
dividend_data = dividend_response.json()
|
||
logger.info(f"[FMP API] Dividend data for {ticker}: {dividend_data}")
|
||
|
||
if not dividend_data or "historical" not in dividend_data or not dividend_data["historical"]:
|
||
logger.warning(f"[FMP API] No dividend history found for {ticker}")
|
||
return None
|
||
|
||
# Calculate TTM dividend
|
||
dividends = pd.DataFrame(dividend_data["historical"])
|
||
dividends["date"] = pd.to_datetime(dividends["date"])
|
||
dividends = dividends.sort_values("date")
|
||
|
||
# Get dividends in the last 12 months
|
||
one_year_ago = pd.Timestamp.now() - pd.Timedelta(days=365)
|
||
recent_dividends = dividends[dividends["date"] >= one_year_ago]
|
||
|
||
if recent_dividends.empty:
|
||
logger.warning(f"[FMP API] No recent dividends found for {ticker}")
|
||
return None
|
||
|
||
# Calculate TTM dividend
|
||
ttm_dividend = recent_dividends["dividend"].sum()
|
||
|
||
# Calculate yield
|
||
yield_pct = (ttm_dividend / current_price) * 100
|
||
logger.info(f"[FMP API] Calculated yield for {ticker}: {yield_pct:.2f}% (TTM dividend: ${ttm_dividend:.2f}, Price: ${current_price:.2f})")
|
||
|
||
# For high-yield ETFs, verify the yield is reasonable
|
||
if ticker in HIGH_YIELD_ETFS:
|
||
expected_yield = HIGH_YIELD_ETFS[ticker]["expected_yield"]
|
||
if yield_pct < expected_yield * 0.5: # If yield is less than 50% of expected
|
||
logger.error(f"[FMP API] Calculated yield {yield_pct:.2f}% for {ticker} is much lower than expected {expected_yield}%")
|
||
logger.error(f"[FMP API] TTM dividend: ${ttm_dividend:.2f}")
|
||
logger.error(f"[FMP API] Current price: ${current_price:.2f}")
|
||
logger.error(f"[FMP API] Recent dividends:\n{recent_dividends}")
|
||
|
||
# Determine distribution period
|
||
if len(recent_dividends) >= 2:
|
||
intervals = recent_dividends["date"].diff().dt.days.dropna()
|
||
avg_interval = intervals.mean()
|
||
if avg_interval <= 45:
|
||
dist_period = "Monthly"
|
||
elif avg_interval <= 100:
|
||
dist_period = "Quarterly"
|
||
elif avg_interval <= 200:
|
||
dist_period = "Semi-Annually"
|
||
else:
|
||
dist_period = "Annually"
|
||
else:
|
||
dist_period = "Unknown"
|
||
|
||
etf_data = {
|
||
"Ticker": ticker,
|
||
"Price": current_price,
|
||
"Yield (%)": yield_pct,
|
||
"Distribution Period": dist_period,
|
||
"Risk Level": "High" if ticker in HIGH_YIELD_ETFS else "Moderate",
|
||
"data_source": "FMP API", # Add data source identifier
|
||
"raw_data": { # Store raw data for debugging
|
||
"profile": profile,
|
||
"dividend_history": dividend_data["historical"][:5] # Store first 5 dividend records
|
||
}
|
||
}
|
||
logger.info(f"[FMP API] Final data for {ticker}: {etf_data}")
|
||
return etf_data
|
||
|
||
except Exception as e:
|
||
logger.error(f"[FMP API] Error fetching data for {ticker}: {str(e)}")
|
||
logger.error(traceback.format_exc())
|
||
return None
|
||
|
||
def fetch_etf_data_yfinance(ticker: str) -> Optional[Dict[str, Any]]:
|
||
"""
|
||
Fetch ETF data from yfinance as fallback.
|
||
|
||
Args:
|
||
ticker: ETF ticker symbol
|
||
|
||
Returns:
|
||
Dictionary with ETF data or None if failed
|
||
"""
|
||
try:
|
||
logger.info(f"Fetching yfinance data for {ticker}")
|
||
etf = yf.Ticker(ticker)
|
||
info = etf.info
|
||
|
||
# Get the most recent dividend yield
|
||
if 'dividendYield' in info and info['dividendYield'] is not None:
|
||
yield_pct = info['dividendYield'] * 100
|
||
logger.info(f"Found dividend yield in yfinance for {ticker}: {yield_pct:.2f}%")
|
||
else:
|
||
# Try to calculate from dividend history
|
||
hist = etf.history(period="1y")
|
||
if not hist.empty and 'Dividends' in hist.columns:
|
||
annual_dividend = hist['Dividends'].sum()
|
||
current_price = info.get('regularMarketPrice', 0)
|
||
yield_pct = (annual_dividend / current_price) * 100 if current_price > 0 else 0
|
||
logger.info(f"Calculated yield from history for {ticker}: {yield_pct:.2f}%")
|
||
else:
|
||
yield_pct = 0
|
||
logger.warning(f"No yield data found for {ticker} in yfinance")
|
||
|
||
# Get current price
|
||
current_price = info.get('regularMarketPrice', 0)
|
||
if current_price <= 0:
|
||
current_price = info.get('regularMarketPreviousClose', 0)
|
||
logger.warning(f"Using previous close price for {ticker}: {current_price}")
|
||
|
||
etf_data = {
|
||
"Ticker": ticker,
|
||
"Price": current_price,
|
||
"Yield (%)": yield_pct,
|
||
"Risk Level": "High", # Default for high-yield ETFs
|
||
"data_source": "yfinance" # Add data source identifier
|
||
}
|
||
logger.info(f"yfinance data for {ticker}: {etf_data}")
|
||
return etf_data
|
||
|
||
except Exception as e:
|
||
logger.error(f"Error fetching yfinance data for {ticker}: {str(e)}")
|
||
return None
|
||
|
||
def fetch_etf_data(tickers: List[str]) -> pd.DataFrame:
|
||
"""
|
||
Fetch ETF data using FMP API with yfinance fallback.
|
||
Uses HIGH_YIELD_ETFS data only as a last resort.
|
||
|
||
Args:
|
||
tickers: List of ETF tickers
|
||
|
||
Returns:
|
||
DataFrame with ETF data
|
||
"""
|
||
try:
|
||
data = {}
|
||
cache_dir = Path("cache")
|
||
cache_dir.mkdir(exist_ok=True)
|
||
|
||
logger.info("=== Starting ETF data fetch ===")
|
||
logger.info(f"Force refresh enabled: {st.session_state.get('force_refresh_data', False)}")
|
||
logger.info(f"Cache directory: {cache_dir.absolute()}")
|
||
logger.info(f"FMP API enabled: {USE_FMP_API}")
|
||
logger.info(f"FMP API key available: {bool(FMP_API_KEY)}")
|
||
|
||
for ticker in tickers:
|
||
if not ticker: # Skip empty tickers
|
||
continue
|
||
|
||
logger.info(f"\n=== Processing {ticker} ===")
|
||
|
||
# Check cache first if not forcing refresh
|
||
cache_file = cache_dir / f"{ticker}_data.json"
|
||
logger.info(f"Cache file path: {cache_file.absolute()}")
|
||
logger.info(f"Cache file exists: {cache_file.exists()}")
|
||
|
||
if not st.session_state.get("force_refresh_data", False) and cache_file.exists():
|
||
try:
|
||
with open(cache_file, 'r') as f:
|
||
cached_data = json.load(f)
|
||
cache_time = datetime.fromisoformat(cached_data.get('timestamp', '2000-01-01'))
|
||
cache_age = datetime.now() - cache_time
|
||
logger.info(f"Cache age: {cache_age.total_seconds() / 3600:.2f} hours")
|
||
|
||
if cache_age < timedelta(hours=24):
|
||
logger.info(f"Using cached data for {ticker}")
|
||
data[ticker] = cached_data['data']
|
||
continue
|
||
else:
|
||
logger.info(f"Cache expired for {ticker} (age: {cache_age.total_seconds() / 3600:.2f} hours)")
|
||
except Exception as e:
|
||
logger.warning(f"Error reading cache for {ticker}: {str(e)}")
|
||
logger.warning(traceback.format_exc())
|
||
else:
|
||
logger.info(f"No cache found or force refresh enabled for {ticker}")
|
||
|
||
# Try FMP first if enabled
|
||
if USE_FMP_API and FMP_API_KEY:
|
||
logger.info(f"Attempting to fetch data from FMP API for {ticker}")
|
||
etf_data = fetch_etf_data_fmp(ticker)
|
||
if etf_data is not None:
|
||
logger.info(f"Successfully fetched data from FMP API for {ticker}")
|
||
# Cache the data
|
||
try:
|
||
cache_data = {
|
||
'timestamp': datetime.now().isoformat(),
|
||
'data': etf_data
|
||
}
|
||
with open(cache_file, 'w') as f:
|
||
json.dump(cache_data, f)
|
||
logger.info(f"Cached FMP data for {ticker}")
|
||
except Exception as e:
|
||
logger.warning(f"Error caching FMP data for {ticker}: {str(e)}")
|
||
logger.warning(traceback.format_exc())
|
||
|
||
data[ticker] = etf_data
|
||
st.session_state.api_calls += 1
|
||
logger.info(f"Total API calls: {st.session_state.api_calls}")
|
||
continue
|
||
else:
|
||
logger.warning(f"FMP API fetch failed for {ticker}, falling back to yfinance")
|
||
|
||
# If FMP fails, try yfinance
|
||
logger.info(f"Fetching data from yfinance for {ticker}")
|
||
etf_data = fetch_etf_data_yfinance(ticker)
|
||
if etf_data is not None:
|
||
logger.info(f"Successfully fetched data from yfinance for {ticker}")
|
||
# Cache the data
|
||
try:
|
||
cache_data = {
|
||
'timestamp': datetime.now().isoformat(),
|
||
'data': etf_data
|
||
}
|
||
with open(cache_file, 'w') as f:
|
||
json.dump(cache_data, f)
|
||
logger.info(f"Cached yfinance data for {ticker}")
|
||
except Exception as e:
|
||
logger.warning(f"Error caching yfinance data for {ticker}: {str(e)}")
|
||
logger.warning(traceback.format_exc())
|
||
|
||
data[ticker] = etf_data
|
||
continue
|
||
|
||
# Only use HIGH_YIELD_ETFS data if both FMP and yfinance failed
|
||
if ticker in HIGH_YIELD_ETFS:
|
||
logger.info(f"Using fallback data from HIGH_YIELD_ETFS for {ticker}")
|
||
etf_data = {
|
||
"Ticker": ticker,
|
||
"Price": 25.0, # Default price for fallback
|
||
"Yield (%)": HIGH_YIELD_ETFS[ticker]["expected_yield"],
|
||
"Distribution Period": HIGH_YIELD_ETFS[ticker]["frequency"],
|
||
"Risk Level": "High",
|
||
"data_source": "HIGH_YIELD_ETFS"
|
||
}
|
||
data[ticker] = etf_data
|
||
else:
|
||
logger.error(f"Failed to fetch data for {ticker} from all sources")
|
||
|
||
if not data:
|
||
st.error("No ETF data could be fetched")
|
||
return pd.DataFrame()
|
||
|
||
df = pd.DataFrame(data.values())
|
||
|
||
# Validate the data
|
||
if df.empty:
|
||
st.error("No ETF data could be fetched")
|
||
return pd.DataFrame()
|
||
|
||
if (df["Price"] <= 0).any():
|
||
st.error("Some ETFs have invalid prices")
|
||
return pd.DataFrame()
|
||
|
||
if (df["Yield (%)"] <= 0).any():
|
||
st.warning("Some ETFs have zero or negative yields")
|
||
|
||
# Log data sources used
|
||
if "data_source" in df.columns:
|
||
source_counts = df["data_source"].value_counts()
|
||
logger.info(f"Data sources used:\n{source_counts}")
|
||
|
||
logger.info(f"Final DataFrame:\n{df}")
|
||
return df
|
||
|
||
except Exception as e:
|
||
st.error(f"Error fetching ETF data: {str(e)}")
|
||
logger.error(f"Error in fetch_etf_data: {str(e)}")
|
||
logger.error(traceback.format_exc())
|
||
return pd.DataFrame()
|
||
|
||
def run_portfolio_simulation(
|
||
tickers: List[str],
|
||
weights: List[float],
|
||
initial_investment: float,
|
||
start_date: str,
|
||
end_date: str,
|
||
rebalance_frequency: str = 'monthly',
|
||
use_fmp: bool = True
|
||
) -> Dict[str, Any]:
|
||
"""
|
||
Run portfolio simulation with the given parameters.
|
||
|
||
Args:
|
||
tickers: List of ETF tickers
|
||
weights: List of portfolio weights
|
||
initial_investment: Initial investment amount
|
||
start_date: Start date for simulation
|
||
end_date: End date for simulation
|
||
rebalance_frequency: Frequency of rebalancing
|
||
use_fmp: Whether to use FMP API for data
|
||
|
||
Returns:
|
||
Dictionary with simulation results
|
||
"""
|
||
try:
|
||
# Validate inputs
|
||
if not tickers or not weights:
|
||
raise ValueError("No tickers or weights provided")
|
||
if len(tickers) != len(weights):
|
||
raise ValueError("Number of tickers must match number of weights")
|
||
if not all(0 <= w <= 1 for w in weights):
|
||
raise ValueError("Weights must be between 0 and 1")
|
||
if sum(weights) != 1:
|
||
raise ValueError("Weights must sum to 1")
|
||
|
||
# Get historical data
|
||
historical_data = {}
|
||
for ticker in tickers:
|
||
if use_fmp and FMP_API_KEY:
|
||
data = fetch_etf_data_fmp(ticker)
|
||
if data and 'historical' in data:
|
||
historical_data[ticker] = data['historical']
|
||
else:
|
||
logger.warning(f"Falling back to yfinance for {ticker}")
|
||
data = fetch_etf_data_yfinance(ticker)
|
||
if data and 'historical' in data:
|
||
historical_data[ticker] = data['historical']
|
||
else:
|
||
data = fetch_etf_data_yfinance(ticker)
|
||
if data and 'historical' in data:
|
||
historical_data[ticker] = data['historical']
|
||
|
||
if not historical_data:
|
||
raise ValueError("No historical data available for any tickers")
|
||
|
||
# Create portfolio DataFrame
|
||
portfolio = pd.DataFrame()
|
||
for ticker, data in historical_data.items():
|
||
portfolio[ticker] = data['close']
|
||
|
||
# Calculate portfolio returns
|
||
portfolio_returns = portfolio.pct_change()
|
||
portfolio_returns = portfolio_returns.fillna(0)
|
||
|
||
# Calculate weighted returns
|
||
weighted_returns = pd.DataFrame()
|
||
for i, ticker in enumerate(tickers):
|
||
weighted_returns[ticker] = portfolio_returns[ticker] * weights[i]
|
||
|
||
portfolio_returns['portfolio'] = weighted_returns.sum(axis=1)
|
||
|
||
# Calculate cumulative returns
|
||
cumulative_returns = (1 + portfolio_returns).cumprod()
|
||
|
||
# Calculate portfolio value
|
||
portfolio_value = initial_investment * cumulative_returns['portfolio']
|
||
|
||
# Calculate metrics
|
||
total_return = (portfolio_value.iloc[-1] / initial_investment) - 1
|
||
annual_return = (1 + total_return) ** (252 / len(portfolio_value)) - 1
|
||
volatility = portfolio_returns['portfolio'].std() * np.sqrt(252)
|
||
sharpe_ratio = annual_return / volatility if volatility != 0 else 0
|
||
|
||
# Calculate drawdown
|
||
rolling_max = portfolio_value.expanding().max()
|
||
drawdown = (portfolio_value - rolling_max) / rolling_max
|
||
max_drawdown = drawdown.min()
|
||
|
||
return {
|
||
'portfolio_value': portfolio_value,
|
||
'returns': portfolio_returns,
|
||
'cumulative_returns': cumulative_returns,
|
||
'total_return': total_return,
|
||
'annual_return': annual_return,
|
||
'volatility': volatility,
|
||
'sharpe_ratio': sharpe_ratio,
|
||
'max_drawdown': max_drawdown,
|
||
'drawdown': drawdown
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"Error in portfolio simulation: {str(e)}")
|
||
st.error(f"Error running portfolio simulation: {str(e)}")
|
||
return None
|
||
|
||
def portfolio_summary(final_alloc: pd.DataFrame) -> None:
|
||
"""
|
||
Display a summary of the portfolio allocation.
|
||
|
||
Args:
|
||
final_alloc: DataFrame containing the portfolio allocation
|
||
"""
|
||
if final_alloc is None or final_alloc.empty:
|
||
st.warning("No portfolio data available.")
|
||
return
|
||
|
||
try:
|
||
# Calculate key metrics
|
||
total_capital = final_alloc["Capital Allocated ($)"].sum()
|
||
total_income = final_alloc["Income Contributed ($)"].sum()
|
||
|
||
# Calculate weighted average yield
|
||
weighted_yield = (final_alloc["Allocation (%)"] * final_alloc["Yield (%)"]).sum() / 100
|
||
|
||
# Display metrics in columns
|
||
col1, col2, col3 = st.columns(3)
|
||
|
||
with col1:
|
||
st.metric("Total Capital", f"${total_capital:,.2f}")
|
||
|
||
with col2:
|
||
st.metric("Annual Income", f"${total_income:,.2f}")
|
||
st.metric("Monthly Income", f"${total_income/12:,.2f}")
|
||
|
||
with col3:
|
||
st.metric("Average Yield", f"{weighted_yield:.2f}%")
|
||
st.metric("Effective Yield", f"{(total_income/total_capital*100):.2f}%")
|
||
|
||
# Display allocation chart
|
||
fig = px.pie(
|
||
final_alloc,
|
||
values="Allocation (%)",
|
||
names="Ticker",
|
||
title="Portfolio Allocation by ETF",
|
||
hover_data={
|
||
"Ticker": True,
|
||
"Allocation (%)": ":.2f",
|
||
"Yield (%)": ":.2f",
|
||
"Capital Allocated ($)": ":,.2f",
|
||
"Income Contributed ($)": ":,.2f"
|
||
}
|
||
)
|
||
st.plotly_chart(fig, use_container_width=True)
|
||
|
||
# Display detailed allocation table
|
||
st.subheader("Detailed Allocation")
|
||
display_df = final_alloc.copy()
|
||
display_df["Monthly Income"] = display_df["Income Contributed ($)"] / 12
|
||
|
||
# Ensure data_source column exists and rename it for display
|
||
if "data_source" in display_df.columns:
|
||
display_df = display_df.rename(columns={"data_source": "Data Source"})
|
||
else:
|
||
display_df["Data Source"] = "Unknown"
|
||
|
||
# Select and order columns for display
|
||
display_columns = [
|
||
"Ticker",
|
||
"Allocation (%)",
|
||
"Yield (%)",
|
||
"Price",
|
||
"Shares",
|
||
"Capital Allocated ($)",
|
||
"Monthly Income",
|
||
"Income Contributed ($)",
|
||
"Risk Level",
|
||
"Data Source"
|
||
]
|
||
|
||
# Format the display
|
||
st.dataframe(
|
||
display_df[display_columns].style.format({
|
||
"Allocation (%)": "{:.2f}%",
|
||
"Yield (%)": "{:.2f}%",
|
||
"Price": "${:,.2f}",
|
||
"Shares": "{:,.4f}",
|
||
"Capital Allocated ($)": "${:,.2f}",
|
||
"Monthly Income": "${:,.2f}",
|
||
"Income Contributed ($)": "${:,.2f}"
|
||
}),
|
||
column_config={
|
||
"Ticker": st.column_config.TextColumn("Ticker", disabled=True),
|
||
"Allocation (%)": st.column_config.NumberColumn(
|
||
"Allocation (%)",
|
||
min_value=0.0,
|
||
max_value=100.0,
|
||
step=0.1,
|
||
format="%.1f",
|
||
required=True
|
||
),
|
||
"Yield (%)": st.column_config.TextColumn("Yield (%)", disabled=True),
|
||
"Price": st.column_config.TextColumn("Price", disabled=True),
|
||
"Shares": st.column_config.TextColumn("Shares", disabled=True),
|
||
"Capital Allocated ($)": st.column_config.TextColumn("Capital Allocated ($)", disabled=True),
|
||
"Monthly Income": st.column_config.TextColumn("Monthly Income", disabled=True),
|
||
"Income Contributed ($)": st.column_config.TextColumn("Income Contributed ($)", disabled=True),
|
||
"Risk Level": st.column_config.TextColumn("Risk Level", disabled=True),
|
||
"Data Source": st.column_config.TextColumn("Data Source", disabled=True)
|
||
},
|
||
hide_index=True,
|
||
use_container_width=True
|
||
)
|
||
|
||
except Exception as e:
|
||
st.error(f"Error calculating portfolio summary: {str(e)}")
|
||
logger.error(f"Error in portfolio_summary: {str(e)}")
|
||
logger.error(traceback.format_exc())
|
||
|
||
def save_portfolio(portfolio_name: str, final_alloc: pd.DataFrame, mode: str, target: float) -> bool:
|
||
"""
|
||
Save portfolio allocation to a JSON file.
|
||
|
||
Args:
|
||
portfolio_name: Name of the portfolio
|
||
final_alloc: DataFrame containing portfolio allocation
|
||
mode: Portfolio mode ("Income Target" or "Capital Target")
|
||
target: Target value (income or capital)
|
||
|
||
Returns:
|
||
bool: True if save was successful, False otherwise
|
||
"""
|
||
try:
|
||
# Create portfolios directory if it doesn't exist
|
||
portfolios_dir = Path("portfolios")
|
||
portfolios_dir.mkdir(exist_ok=True)
|
||
|
||
# Prepare portfolio data
|
||
portfolio_data = {
|
||
"name": portfolio_name,
|
||
"created_at": datetime.now().isoformat(),
|
||
"mode": mode,
|
||
"target": target,
|
||
"allocations": []
|
||
}
|
||
|
||
# Convert DataFrame to list of dictionaries
|
||
for _, row in final_alloc.iterrows():
|
||
allocation = {
|
||
"ticker": row["Ticker"],
|
||
"allocation": float(row["Allocation (%)"]),
|
||
"yield": float(row["Yield (%)"]),
|
||
"price": float(row["Price"]),
|
||
"risk_level": row["Risk Level"]
|
||
}
|
||
portfolio_data["allocations"].append(allocation)
|
||
|
||
# Save to JSON file
|
||
file_path = portfolios_dir / f"{portfolio_name}.json"
|
||
with open(file_path, 'w') as f:
|
||
json.dump(portfolio_data, f, indent=2)
|
||
|
||
return True
|
||
|
||
except Exception as e:
|
||
st.error(f"Error saving portfolio: {str(e)}")
|
||
return False
|
||
|
||
def load_portfolio(portfolio_name: str) -> Tuple[Optional[pd.DataFrame], Optional[str], Optional[float]]:
|
||
"""
|
||
Load portfolio allocation from a JSON file.
|
||
|
||
Args:
|
||
portfolio_name: Name of the portfolio to load
|
||
|
||
Returns:
|
||
Tuple containing:
|
||
- DataFrame with portfolio allocation
|
||
- Portfolio mode
|
||
- Target value
|
||
"""
|
||
try:
|
||
# Check if portfolio exists
|
||
file_path = Path("portfolios") / f"{portfolio_name}.json"
|
||
if not file_path.exists():
|
||
st.error(f"Portfolio '{portfolio_name}' not found.")
|
||
return None, None, None
|
||
|
||
# Load portfolio data
|
||
with open(file_path, 'r') as f:
|
||
portfolio_data = json.load(f)
|
||
|
||
# Convert allocations to DataFrame
|
||
allocations = portfolio_data["allocations"]
|
||
df = pd.DataFrame(allocations)
|
||
|
||
# Rename columns to match expected format
|
||
df = df.rename(columns={
|
||
"allocation": "Allocation (%)",
|
||
"yield": "Yield (%)",
|
||
"price": "Price"
|
||
})
|
||
|
||
return df, portfolio_data["mode"], portfolio_data["target"]
|
||
|
||
except Exception as e:
|
||
st.error(f"Error loading portfolio: {str(e)}")
|
||
return None, None, None
|
||
|
||
def list_saved_portfolios() -> List[str]:
|
||
"""
|
||
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()
|
||
|
||
# Initialize allocation column if it doesn't exist
|
||
if "Allocation (%)" not in final_alloc.columns:
|
||
final_alloc["Allocation (%)"] = 0.0
|
||
|
||
# Set allocations
|
||
for alloc in etf_allocations:
|
||
mask = final_alloc["Ticker"] == alloc["ticker"]
|
||
if mask.any():
|
||
final_alloc.loc[mask, "Allocation (%)"] = alloc["allocation"]
|
||
else:
|
||
logger.warning(f"Ticker {alloc['ticker']} not found in DataFrame")
|
||
|
||
# Verify allocations are set
|
||
if final_alloc["Allocation (%)"].sum() == 0:
|
||
logger.error("No allocations were set")
|
||
return None
|
||
|
||
# Calculate required capital for income target
|
||
monthly_income = target
|
||
annual_income = monthly_income * 12
|
||
|
||
# Calculate weighted average yield
|
||
weighted_yield = (final_alloc["Allocation (%)"] * final_alloc["Yield (%)"]).sum() / 100
|
||
if weighted_yield == 0:
|
||
logger.error("Weighted yield is zero")
|
||
return None
|
||
|
||
# Calculate required capital based on weighted yield
|
||
required_capital = (annual_income / weighted_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 ($)"] * final_alloc["Yield (%)"]) / 100
|
||
|
||
# Verify calculations
|
||
total_income = final_alloc["Income Contributed ($)"].sum()
|
||
if abs(total_income - annual_income) > 1.0: # Allow for small rounding errors
|
||
logger.warning(f"Total income ({total_income}) does not match target ({annual_income})")
|
||
|
||
logger.info(f"Income allocation completed. Required capital: ${required_capital:,.2f}")
|
||
logger.info(f"Final allocations:\n{final_alloc}")
|
||
|
||
return final_alloc
|
||
|
||
except Exception as e:
|
||
logger.error(f"Error in income allocation: {str(e)}")
|
||
logger.error(traceback.format_exc())
|
||
return None
|
||
|
||
def allocate_for_capital(df: pd.DataFrame, initial_capital: float, etf_allocations: List[Dict[str, Any]]) -> pd.DataFrame:
|
||
"""
|
||
Allocate portfolio for capital target.
|
||
|
||
Args:
|
||
df: DataFrame with ETF data
|
||
initial_capital: Initial capital amount
|
||
etf_allocations: List of ETF allocations
|
||
|
||
Returns:
|
||
DataFrame with final allocation
|
||
"""
|
||
try:
|
||
# Create final allocation DataFrame
|
||
final_alloc = df.copy()
|
||
|
||
# Initialize allocation column if it doesn't exist
|
||
if "Allocation (%)" not in final_alloc.columns:
|
||
final_alloc["Allocation (%)"] = 0.0
|
||
|
||
# Set allocations
|
||
for alloc in etf_allocations:
|
||
mask = final_alloc["Ticker"] == alloc["ticker"]
|
||
if mask.any():
|
||
final_alloc.loc[mask, "Allocation (%)"] = alloc["allocation"]
|
||
else:
|
||
logger.warning(f"Ticker {alloc['ticker']} not found in DataFrame")
|
||
|
||
# Verify allocations are set
|
||
if final_alloc["Allocation (%)"].sum() == 0:
|
||
logger.error("No allocations were set")
|
||
return None
|
||
|
||
# Calculate capital allocation and income
|
||
final_alloc["Capital Allocated ($)"] = (final_alloc["Allocation (%)"] / 100) * initial_capital
|
||
final_alloc["Shares"] = final_alloc["Capital Allocated ($)"] / final_alloc["Price"]
|
||
final_alloc["Income Contributed ($)"] = (final_alloc["Capital Allocated ($)"] * final_alloc["Yield (%)"]) / 100
|
||
|
||
# Verify calculations
|
||
total_capital = final_alloc["Capital Allocated ($)"].sum()
|
||
if abs(total_capital - initial_capital) > 1.0: # Allow for small rounding errors
|
||
logger.warning(f"Total capital ({total_capital}) does not match initial capital ({initial_capital})")
|
||
|
||
logger.info(f"Capital allocation completed. Initial capital: ${initial_capital:,.2f}")
|
||
logger.info(f"Final allocations:\n{final_alloc}")
|
||
|
||
return final_alloc
|
||
|
||
except Exception as e:
|
||
logger.error(f"Error in capital allocation: {str(e)}")
|
||
logger.error(traceback.format_exc())
|
||
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() -> bool:
|
||
"""Test connection to FMP API."""
|
||
try:
|
||
if not FMP_API_KEY:
|
||
st.error("FMP API key not found in environment variables")
|
||
return False
|
||
|
||
session = get_fmp_session()
|
||
test_url = f"{FMP_BASE_URL}/profile/SPY?apikey={FMP_API_KEY}"
|
||
logger.info(f"Making FMP API test call to {test_url}")
|
||
response = session.get(test_url)
|
||
st.session_state.api_calls += 1
|
||
logger.info(f"FMP API call count: {st.session_state.api_calls}")
|
||
|
||
if response.status_code == 200:
|
||
st.success("Successfully connected to FMP API")
|
||
return True
|
||
else:
|
||
st.error(f"Failed to connect to FMP API: {response.status_code}")
|
||
logger.error(f"FMP API test failed: {response.text}")
|
||
return False
|
||
except Exception as e:
|
||
st.error(f"Error testing FMP connection: {str(e)}")
|
||
logger.error(f"FMP API test error: {str(e)}")
|
||
return False
|
||
|
||
def get_cache_stats() -> Dict[str, Any]:
|
||
"""
|
||
Get statistics about the cache usage.
|
||
|
||
Returns:
|
||
Dictionary containing cache statistics
|
||
"""
|
||
try:
|
||
cache_dir = Path("cache")
|
||
if not cache_dir.exists():
|
||
return {
|
||
"ticker_count": 0,
|
||
"file_count": 0,
|
||
"total_size_kb": 0
|
||
}
|
||
|
||
# Get all cache files
|
||
cache_files = list(cache_dir.glob("**/*.json"))
|
||
|
||
# Count unique tickers
|
||
tickers = set()
|
||
for file in cache_files:
|
||
# Extract ticker from filename (assuming format: ticker_data_type.json)
|
||
ticker = file.stem.split('_')[0]
|
||
tickers.add(ticker)
|
||
|
||
# Calculate total size
|
||
total_size = sum(file.stat().st_size for file in cache_files)
|
||
|
||
return {
|
||
"ticker_count": len(tickers),
|
||
"file_count": len(cache_files),
|
||
"total_size_kb": total_size / 1024 # Convert to KB
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"Error getting cache stats: {str(e)}")
|
||
return {
|
||
"ticker_count": 0,
|
||
"file_count": 0,
|
||
"total_size_kb": 0
|
||
}
|
||
|
||
def clear_cache(ticker: Optional[str] = None) -> None:
|
||
"""
|
||
Clear cache files for a specific ticker or all tickers.
|
||
|
||
Args:
|
||
ticker: Optional ticker symbol to clear cache for. If None, clears all cache.
|
||
"""
|
||
try:
|
||
cache_dir = Path("cache")
|
||
if not cache_dir.exists():
|
||
return
|
||
|
||
if ticker:
|
||
# Clear cache for specific ticker
|
||
pattern = f"{ticker.upper()}_*.json"
|
||
cache_files = list(cache_dir.glob(f"**/{pattern}"))
|
||
else:
|
||
# Clear all cache files
|
||
cache_files = list(cache_dir.glob("**/*.json"))
|
||
|
||
# Delete cache files
|
||
for file in cache_files:
|
||
try:
|
||
file.unlink()
|
||
logger.info(f"Deleted cache file: {file}")
|
||
except Exception as e:
|
||
logger.error(f"Error deleting cache file {file}: {str(e)}")
|
||
|
||
except Exception as e:
|
||
logger.error(f"Error clearing cache: {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
|
||
logger.info("Initialized simulation_run in session state")
|
||
if 'df_data' not in st.session_state:
|
||
st.session_state.df_data = None
|
||
logger.info("Initialized df_data in session state")
|
||
if 'final_alloc' not in st.session_state:
|
||
st.session_state.final_alloc = None
|
||
logger.info("Initialized final_alloc in session state")
|
||
if 'mode' not in st.session_state:
|
||
st.session_state.mode = 'Capital Target'
|
||
logger.info("Initialized mode in session state")
|
||
if 'target' not in st.session_state:
|
||
st.session_state.target = 0
|
||
logger.info("Initialized target in session state")
|
||
if 'initial_capital' not in st.session_state:
|
||
st.session_state.initial_capital = 0
|
||
logger.info("Initialized initial_capital in session state")
|
||
if 'enable_drip' not in st.session_state:
|
||
st.session_state.enable_drip = False
|
||
logger.info("Initialized enable_drip in session state")
|
||
if 'enable_erosion' not in st.session_state:
|
||
st.session_state.enable_erosion = False
|
||
logger.info("Initialized enable_erosion in session state")
|
||
if 'api_calls' not in st.session_state:
|
||
st.session_state.api_calls = 0
|
||
logger.info("Initialized api_calls in session state")
|
||
if 'force_refresh_data' not in st.session_state:
|
||
st.session_state.force_refresh_data = False
|
||
logger.info("Initialized force_refresh_data in session state")
|
||
if 'etf_allocations' not in st.session_state:
|
||
st.session_state.etf_allocations = []
|
||
logger.info("Initialized empty etf_allocations in session state")
|
||
if 'risk_tolerance' not in st.session_state:
|
||
st.session_state.risk_tolerance = "Moderate"
|
||
logger.info("Initialized risk_tolerance in session state")
|
||
|
||
# Main title
|
||
st.title("📈 ETF Portfolio Builder")
|
||
|
||
# Function to remove ticker
|
||
def remove_ticker(ticker_to_remove: str) -> None:
|
||
"""Remove a ticker from the portfolio."""
|
||
try:
|
||
logger.info(f"Removing ticker: {ticker_to_remove}")
|
||
current_allocations = list(st.session_state.etf_allocations)
|
||
st.session_state.etf_allocations = [etf for etf in current_allocations if etf["ticker"] != ticker_to_remove]
|
||
logger.info(f"Updated allocations after removal: {st.session_state.etf_allocations}")
|
||
st.rerun()
|
||
except Exception as e:
|
||
logger.error(f"Error removing ticker: {str(e)}")
|
||
st.error(f"Error removing ticker: {str(e)}")
|
||
|
||
# Display current tickers in the main space
|
||
if st.session_state.etf_allocations:
|
||
st.subheader("Selected ETFs")
|
||
st.markdown("""
|
||
<style>
|
||
.ticker-container {
|
||
display: flex;
|
||
flex-wrap: wrap;
|
||
gap: 8px;
|
||
margin-bottom: 20px;
|
||
}
|
||
.ticker-item {
|
||
display: inline-flex;
|
||
align-items: center;
|
||
color: #2ecc71;
|
||
font-size: 1.1em;
|
||
font-weight: 500;
|
||
}
|
||
.ticker-close {
|
||
margin-left: 2px;
|
||
cursor: pointer;
|
||
font-size: 1.1em;
|
||
opacity: 0.7;
|
||
}
|
||
.ticker-close:hover {
|
||
opacity: 1;
|
||
}
|
||
</style>
|
||
""", unsafe_allow_html=True)
|
||
|
||
# Create a container for tickers
|
||
ticker_container = st.container()
|
||
with ticker_container:
|
||
# Display each ticker with a close button
|
||
for etf in st.session_state.etf_allocations:
|
||
col1, col2 = st.columns([0.05, 0.95]) # Adjusted column ratio
|
||
with col1:
|
||
if st.button("×", key=f"remove_{etf['ticker']}",
|
||
help=f"Remove {etf['ticker']} from portfolio"):
|
||
remove_ticker(etf['ticker'])
|
||
with col2:
|
||
st.markdown(f"<div class='ticker-item'>{etf['ticker']}</div>", unsafe_allow_html=True)
|
||
|
||
# Debug information
|
||
logger.info("=== Session State Debug ===")
|
||
logger.info(f"Full session state: {dict(st.session_state)}")
|
||
logger.info(f"ETF allocations type: {type(st.session_state.etf_allocations)}")
|
||
logger.info(f"ETF allocations content: {st.session_state.etf_allocations}")
|
||
logger.info("=== End Session State Debug ===")
|
||
|
||
def add_etf_to_portfolio(ticker: str) -> bool:
|
||
"""Add an ETF to the portfolio with proper validation and error handling."""
|
||
try:
|
||
logger.info("=== Adding ETF to Portfolio ===")
|
||
logger.info(f"Input ticker: {ticker}")
|
||
logger.info(f"Current allocations before adding: {st.session_state.etf_allocations}")
|
||
logger.info(f"Current allocations type: {type(st.session_state.etf_allocations)}")
|
||
|
||
# Validate ticker format
|
||
if not re.match(r'^[A-Z]{1,7}$', ticker.upper()):
|
||
logger.warning(f"Invalid ticker format: {ticker}")
|
||
st.error("Invalid ticker format. Must be 1-7 uppercase letters.")
|
||
return False
|
||
|
||
# Check if ticker already exists
|
||
if any(etf["ticker"] == ticker.upper() for etf in st.session_state.etf_allocations):
|
||
logger.warning(f"Ticker {ticker.upper()} already exists in portfolio")
|
||
st.warning(f"{ticker.upper()} is already in your portfolio.")
|
||
return False
|
||
|
||
# Verify ticker exists by fetching data
|
||
logger.info(f"Fetching data for ticker: {ticker.upper()}")
|
||
etf_data = fetch_etf_data([ticker.upper()])
|
||
logger.info(f"Fetched ETF data: {etf_data}")
|
||
|
||
if etf_data is None or etf_data.empty:
|
||
logger.warning(f"Unknown ticker: {ticker.upper()}")
|
||
st.error(f"Unknown ticker: {ticker.upper()}. Please enter a valid ETF ticker.")
|
||
return False
|
||
|
||
# Create new ETF entry
|
||
new_etf = {
|
||
"ticker": ticker.upper(),
|
||
"allocation": 0.0
|
||
}
|
||
logger.info(f"Created new ETF entry: {new_etf}")
|
||
|
||
# Update session state
|
||
current_allocations = list(st.session_state.etf_allocations)
|
||
current_allocations.append(new_etf)
|
||
st.session_state.etf_allocations = current_allocations
|
||
|
||
logger.info(f"Updated session state allocations: {st.session_state.etf_allocations}")
|
||
logger.info(f"Updated allocations type: {type(st.session_state.etf_allocations)}")
|
||
|
||
# Recalculate allocations based on risk tolerance
|
||
if len(st.session_state.etf_allocations) > 0:
|
||
risk_tolerance = st.session_state.risk_tolerance
|
||
tickers = [etf["ticker"] for etf in st.session_state.etf_allocations]
|
||
logger.info(f"Recalculating allocations for tickers: {tickers}")
|
||
|
||
df_data = fetch_etf_data(tickers)
|
||
logger.info(f"Fetched data for recalculation: {df_data}")
|
||
|
||
if df_data is not None and not df_data.empty:
|
||
etf_metrics = df_data.to_dict('records')
|
||
new_allocations = optimize_portfolio_allocation(
|
||
etf_metrics,
|
||
risk_tolerance,
|
||
pd.DataFrame()
|
||
)
|
||
logger.info(f"Calculated new allocations: {new_allocations}")
|
||
st.session_state.etf_allocations = new_allocations
|
||
logger.info(f"Updated session state with new allocations: {st.session_state.etf_allocations}")
|
||
|
||
logger.info("=== End Adding ETF to Portfolio ===")
|
||
return True
|
||
|
||
except Exception as e:
|
||
logger.error("=== Error Adding ETF to Portfolio ===")
|
||
logger.error(f"Error: {str(e)}")
|
||
logger.error(traceback.format_exc())
|
||
st.error(f"Error adding ETF: {str(e)}")
|
||
return False
|
||
|
||
def remove_etf_from_portfolio(index: int) -> bool:
|
||
"""Remove an ETF from the portfolio with proper validation and error handling."""
|
||
try:
|
||
logger.info(f"Attempting to remove ETF at index: {index}")
|
||
logger.info(f"Current allocations before removal: {st.session_state.etf_allocations}")
|
||
|
||
if not st.session_state.etf_allocations or index >= len(st.session_state.etf_allocations):
|
||
logger.warning(f"Invalid ETF index for removal: {index}")
|
||
return False
|
||
|
||
# Create new list without the removed ETF
|
||
current_allocations = list(st.session_state.etf_allocations)
|
||
removed_etf = current_allocations.pop(index)
|
||
st.session_state.etf_allocations = current_allocations
|
||
|
||
logger.info(f"Successfully removed ETF: {removed_etf}")
|
||
logger.info(f"Updated allocations: {st.session_state.etf_allocations}")
|
||
|
||
# Recalculate allocations if there are remaining ETFs
|
||
if st.session_state.etf_allocations:
|
||
risk_tolerance = st.session_state.risk_tolerance
|
||
tickers = [etf["ticker"] for etf in st.session_state.etf_allocations]
|
||
df_data = fetch_etf_data(tickers)
|
||
|
||
if df_data is not None and not df_data.empty:
|
||
etf_metrics = df_data.to_dict('records')
|
||
new_allocations = optimize_portfolio_allocation(
|
||
etf_metrics,
|
||
risk_tolerance,
|
||
pd.DataFrame()
|
||
)
|
||
st.session_state.etf_allocations = new_allocations
|
||
logger.info(f"Recalculated allocations: {new_allocations}")
|
||
|
||
return True
|
||
|
||
except Exception as e:
|
||
logger.error(f"Error removing ETF: {str(e)}")
|
||
logger.error(traceback.format_exc())
|
||
st.error(f"Error removing ETF: {str(e)}")
|
||
return False
|
||
|
||
# Sidebar for ETF input
|
||
with st.sidebar:
|
||
st.header("ETF Allocation")
|
||
|
||
# Create a container for ETF input
|
||
with st.container():
|
||
# Input field for ETF ticker only
|
||
new_ticker = st.text_input("ETF Ticker", help="Enter a valid ETF ticker (e.g., SCHD)")
|
||
|
||
# Add button to add ETF
|
||
add_etf_button = st.button("ADD ETF", use_container_width=True)
|
||
if add_etf_button:
|
||
logger.info("=== Add ETF Button Clicked ===")
|
||
logger.info(f"Input ticker: {new_ticker}")
|
||
logger.info(f"Current allocations: {st.session_state.etf_allocations}")
|
||
|
||
if not new_ticker:
|
||
st.error("Please enter an ETF ticker.")
|
||
logger.warning("No ticker provided")
|
||
elif len(st.session_state.etf_allocations) >= 10:
|
||
st.error("Maximum of 10 ETFs allowed in portfolio.")
|
||
logger.warning("Maximum ETF limit reached")
|
||
else:
|
||
if add_etf_to_portfolio(new_ticker):
|
||
st.success(f"Added {new_ticker.upper()} to portfolio.")
|
||
logger.info("Successfully added ETF, triggering rerun")
|
||
st.rerun()
|
||
|
||
# Display total allocation
|
||
if st.session_state.etf_allocations:
|
||
current_total = sum(etf["allocation"] for etf in st.session_state.etf_allocations)
|
||
st.metric("Total Allocation (%)", f"{current_total:.2f}")
|
||
|
||
# Add a warning if total is not 100%
|
||
if abs(current_total - 100) > 0.1:
|
||
st.warning("Total allocation should be 100%")
|
||
else:
|
||
st.info("No ETFs added yet. Please add ETFs to your portfolio.")
|
||
logger.info("No ETFs in portfolio")
|
||
|
||
# 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=st.session_state.get("risk_tolerance", "Moderate"),
|
||
key="risk_tolerance_slider"
|
||
)
|
||
|
||
# Check if risk tolerance changed
|
||
if risk_tolerance != st.session_state.get("risk_tolerance"):
|
||
logger.info("=== Risk Tolerance Change Detection ===")
|
||
logger.info(f"Current risk tolerance in session state: {st.session_state.get('risk_tolerance')}")
|
||
logger.info(f"New risk tolerance from slider: {risk_tolerance}")
|
||
|
||
# Update session state
|
||
st.session_state.risk_tolerance = risk_tolerance
|
||
|
||
# Recalculate allocations if we have ETFs
|
||
if st.session_state.etf_allocations:
|
||
logger.info("Recalculating allocations due to risk tolerance change")
|
||
tickers = [etf["ticker"] for etf in st.session_state.etf_allocations]
|
||
df_data = fetch_etf_data(tickers)
|
||
|
||
if df_data is not None and not df_data.empty:
|
||
etf_metrics = df_data.to_dict('records')
|
||
new_allocations = optimize_portfolio_allocation(
|
||
etf_metrics,
|
||
risk_tolerance,
|
||
pd.DataFrame()
|
||
)
|
||
st.session_state.etf_allocations = new_allocations
|
||
logger.info(f"New allocations after risk tolerance change: {new_allocations}")
|
||
|
||
# If simulation has been run, update final allocation
|
||
if st.session_state.simulation_run and st.session_state.df_data is not None:
|
||
if st.session_state.mode == "Income Target":
|
||
final_alloc = allocate_for_income(
|
||
st.session_state.df_data,
|
||
st.session_state.target,
|
||
new_allocations
|
||
)
|
||
else:
|
||
final_alloc = allocate_for_capital(
|
||
st.session_state.df_data,
|
||
st.session_state.initial_capital,
|
||
new_allocations
|
||
)
|
||
if final_alloc is not None:
|
||
st.session_state.final_alloc = final_alloc
|
||
st.rerun()
|
||
|
||
# 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
|
||
)
|
||
|
||
# Run simulation button
|
||
if st.button("Run Portfolio Simulation", type="primary", use_container_width=True):
|
||
if not st.session_state.etf_allocations:
|
||
st.error("Please add at least one ETF to your portfolio.")
|
||
else:
|
||
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
|
||
logger.info("Starting portfolio simulation...")
|
||
logger.info(f"ETF allocations: {st.session_state.etf_allocations}")
|
||
|
||
tickers = [etf["ticker"] for etf in st.session_state.etf_allocations]
|
||
df_data = fetch_etf_data(tickers)
|
||
logger.info(f"Fetched ETF data:\n{df_data}")
|
||
|
||
if df_data is not None and not df_data.empty:
|
||
if simulation_mode == "Income Target":
|
||
logger.info(f"Allocating for income target: ${monthly_target}")
|
||
final_alloc = allocate_for_income(df_data, monthly_target, st.session_state.etf_allocations)
|
||
else:
|
||
logger.info(f"Allocating for capital target: ${initial_capital}")
|
||
final_alloc = allocate_for_capital(df_data, initial_capital, st.session_state.etf_allocations)
|
||
|
||
logger.info(f"Final allocation result:\n{final_alloc}")
|
||
|
||
if final_alloc is not None and not final_alloc.empty:
|
||
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()
|
||
else:
|
||
st.error("Failed to generate portfolio allocation. Please check your inputs and try again.")
|
||
else:
|
||
st.error("Failed to fetch ETF data. Please check your tickers and try again.")
|
||
|
||
except Exception as e:
|
||
st.error(f"Error running simulation: {str(e)}")
|
||
logger.error(f"Error in simulation: {str(e)}")
|
||
logger.error(traceback.format_exc())
|
||
|
||
# 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 = test_fmp_connection()
|
||
if connection_status:
|
||
st.sidebar.success("✅ FMP API: Connected")
|
||
else:
|
||
st.sidebar.error("❌ FMP API: Connection failed")
|
||
|
||
# Advanced Options section in sidebar
|
||
with st.sidebar.expander("Advanced Options"):
|
||
# Option to toggle FMP API usage
|
||
use_fmp_api = st.checkbox("Use FMP API for high-yield ETFs", value=USE_FMP_API,
|
||
help="Use Financial Modeling Prep API for more accurate yield data on high-yield ETFs")
|
||
if use_fmp_api != USE_FMP_API:
|
||
# Update global setting if changed
|
||
globals()["USE_FMP_API"] = use_fmp_api
|
||
st.success("FMP API usage setting updated")
|
||
|
||
# Add cache controls
|
||
st.subheader("Cache Settings")
|
||
|
||
# Display cache statistics
|
||
cache_stats = get_cache_stats()
|
||
st.write(f"Cache contains data for {cache_stats['ticker_count']} tickers ({cache_stats['file_count']} files, {cache_stats['total_size_kb']:.1f} KB)")
|
||
|
||
# Force refresh option
|
||
st.session_state.force_refresh_data = st.checkbox(
|
||
"Force refresh data (ignore cache)",
|
||
value=st.session_state.get("force_refresh_data", False),
|
||
help="When enabled, always fetch fresh data from APIs"
|
||
)
|
||
|
||
# Cache clearing options
|
||
col1, col2 = st.columns(2)
|
||
with col1:
|
||
if st.button("Clear All Cache", key="clear_all_cache"):
|
||
clear_cache()
|
||
st.success("All cache files cleared!")
|
||
st.session_state.api_calls = 0
|
||
|
||
with col2:
|
||
ticker_to_clear = st.text_input("Clear cache for ticker:", key="cache_ticker")
|
||
if st.button("Clear", key="clear_single_cache") and ticker_to_clear:
|
||
clear_cache(ticker_to_clear)
|
||
st.success(f"Cache cleared for {ticker_to_clear.upper()}")
|
||
|
||
# Show API call counter
|
||
st.write(f"API calls this session: {st.session_state.api_calls}")
|
||
|
||
# Add option for debug mode and parallel processing
|
||
debug_mode = st.checkbox("Enable Debug Mode", help="Show detailed error logs.")
|
||
parallel_processing = st.checkbox("Enable Parallel Processing", value=True,
|
||
help="Fetch data for multiple ETFs simultaneously")
|
||
|
||
# Add FMP API test button
|
||
st.sidebar.subheader("FMP API Testing")
|
||
if st.sidebar.button("Test FMP API", key="test_fmp_api_button"):
|
||
test_fmp_data_fetching()
|
||
st.sidebar.success("Test completed. Check logs for details.")
|
||
|
||
# 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
|
||
|
||
# Validate final_alloc DataFrame
|
||
if final_alloc is None or final_alloc.empty:
|
||
st.error("No portfolio data available. Please run the simulation again.")
|
||
st.session_state.simulation_run = False
|
||
else:
|
||
# Verify required columns exist
|
||
required_columns = ["Capital Allocated ($)", "Yield (%)", "Price", "Ticker"]
|
||
missing_columns = [col for col in required_columns if col not in final_alloc.columns]
|
||
|
||
if missing_columns:
|
||
st.error(f"Missing required columns in portfolio data: {', '.join(missing_columns)}")
|
||
st.session_state.simulation_run = False
|
||
else:
|
||
# 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":
|
||
try:
|
||
monthly_target = st.session_state.target
|
||
ANNUAL_TARGET = monthly_target * 12
|
||
total_capital = final_alloc["Capital Allocated ($)"].sum()
|
||
st.info(f"🎯 **Income Target Mode**: You need ${total_capital:,.2f} to generate ${monthly_target:,.2f} in monthly income (${ANNUAL_TARGET:,.2f} annually).")
|
||
except Exception as e:
|
||
st.error(f"Error displaying income target information: {str(e)}")
|
||
else:
|
||
try:
|
||
initial_capital = st.session_state.initial_capital
|
||
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).")
|
||
except Exception as e:
|
||
st.error(f"Error displaying capital investment information: {str(e)}")
|
||
|
||
# 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")
|
||
|
||
try:
|
||
# 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",
|
||
required=True
|
||
),
|
||
"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()
|
||
|
||
# Validate individual allocations
|
||
invalid_allocations = edited_df[
|
||
(edited_df["Allocation (%)"] <= 0) |
|
||
(edited_df["Allocation (%)"] > 100)
|
||
]
|
||
|
||
if not invalid_allocations.empty:
|
||
for _, row in invalid_allocations.iterrows():
|
||
st.error(f"Invalid allocation for {row['Ticker']}: must be between 0% and 100%")
|
||
|
||
# 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")
|
||
st.error("Total allocation must be exactly 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 or not invalid_allocations.empty,
|
||
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)}")
|
||
except Exception as e:
|
||
st.error(f"Error displaying allocation details: {str(e)}")
|
||
logger.error(f"Error in allocation display: {str(e)}")
|
||
logger.error(traceback.format_exc()) |