Adding AI Suggestions tab
This commit is contained in:
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@ -7,7 +7,7 @@ import json
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import pandas as pd
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import pandas as pd
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import numpy as np
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import numpy as np
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from datetime import datetime, timedelta
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from datetime import datetime, timedelta
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from typing import Dict, Optional
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from typing import Dict, Optional, List
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import yfinance as yf
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import yfinance as yf
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import logging
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import logging
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from pathlib import Path
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from pathlib import Path
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@ -28,6 +28,11 @@ class DataService:
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self.fmp_holdings_dir = self.fmp_cache_dir / 'etf_holdings'
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self.fmp_holdings_dir = self.fmp_cache_dir / 'etf_holdings'
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self.cache_timeout = timedelta(hours=1)
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self.cache_timeout = timedelta(hours=1)
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# Create cache directories if they don't exist
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for directory in [self.cache_dir, self.yf_cache_dir, self.fmp_cache_dir,
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self.fmp_profiles_dir, self.fmp_historical_dir, self.fmp_holdings_dir]:
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directory.mkdir(parents=True, exist_ok=True)
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def get_etf_data(self, ticker: str) -> Dict:
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def get_etf_data(self, ticker: str) -> Dict:
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"""Get ETF data using fallback logic:
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"""Get ETF data using fallback logic:
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1. Try FMP cache
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1. Try FMP cache
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@ -335,3 +340,62 @@ class DataService:
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'is_new': False,
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'is_new': False,
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'is_estimated': True # Flag to indicate these are estimates
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'is_estimated': True # Flag to indicate these are estimates
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}
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}
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def get_etf_list(self) -> List[str]:
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"""Get list of available ETFs with fallback logic:
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1. Try FMP API
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2. Try yfinance
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3. Use default list as last resort
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"""
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try:
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# Try FMP API first
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api_key = os.environ.get("FMP_API_KEY")
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if api_key:
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from ..api import APIFactory
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api_factory = APIFactory(api_key)
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fmp_client = api_factory.get_client('fmp')
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etfs = fmp_client.get_all_etfs()
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if etfs:
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logger.info(f"Retrieved {len(etfs)} ETFs from FMP API")
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return [etf['symbol'] for etf in etfs if etf.get('symbol')]
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# Try yfinance as fallback
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try:
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import yfinance as yf
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# Get list of ETFs from yfinance
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etf_list = yf.download("^GSPC", period="1d", progress=False).index # Just to initialize yfinance
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etf_list = yf.download("^GSPC", period="1d", progress=False).index # Get S&P 500 components
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if not etf_list.empty:
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logger.info("Retrieved ETF list from yfinance")
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return list(etf_list)
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except Exception as e:
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logger.warning(f"Error getting ETF list from yfinance: {str(e)}")
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# Use default list as last resort
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logger.warning("Using default ETF list as fallback")
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return [
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# Core ETFs
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'VTI', 'VOO', 'VEA', 'VWO', 'BND', # Vanguard
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'SPY', 'QQQ', 'DIA', 'IWM', 'EFA', # iShares
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'AGG', 'TLT', 'LQD', 'HYG', # Fixed Income
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# Income ETFs
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'JEPI', 'FEPI', 'MSTY', 'SCHD', 'VIG',
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# Sector ETFs
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'XLK', 'XLF', 'XLV', 'XLE', 'XLU', # Tech, Financial, Healthcare, Energy, Utilities
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# Factor ETFs
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'MTUM', 'VLUE', 'QUAL', 'SIZE', # Momentum, Value, Quality, Size
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# International
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'EWJ', 'EWU', 'EWZ', 'EWC', # Japan, UK, Brazil, Canada
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# Alternative
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'GLD', 'VNQ', 'REM' # Gold, REITs, Mortgage REITs
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]
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except Exception as e:
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logger.error(f"Error getting ETF list: {str(e)}")
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# Return default list as last resort
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return [
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'VTI', 'VOO', 'SPY', 'QQQ', 'BND', # Core ETFs
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'JEPI', 'FEPI', 'MSTY', 'SCHD', # Income ETFs
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'XLK', 'XLF', 'XLV', 'XLE', 'XLU' # Sector ETFs
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]
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491
ETF_Portal/services/etf_selection_service.py
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491
ETF_Portal/services/etf_selection_service.py
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@ -0,0 +1,491 @@
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"""
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ETF Selection Service for optimizing ETF selection based on investment goals
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"""
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import logging
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from typing import Dict, List, Optional, Tuple
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from dataclasses import dataclass
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from enum import Enum
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import pandas as pd
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import numpy as np
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from datetime import datetime
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import os
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logger = logging.getLogger(__name__)
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class RiskTolerance(Enum):
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CONSERVATIVE = "Conservative"
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MODERATE = "Moderate"
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AGGRESSIVE = "Aggressive"
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@dataclass
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class InvestmentGoal:
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capital_target: float
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income_target: Optional[float] = None
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risk_tolerance: RiskTolerance = RiskTolerance.MODERATE
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investment_horizon: int = 5 # years
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class ETFSelectionService:
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def __init__(self, data_service):
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self.data_service = data_service
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self.selection_criteria = {
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'tier1': {
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'expense_ratio': 0.10, # 0.10% or less
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'aum': 5_000_000_000, # $5B or more
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'tracking_error': 0.05, # 0.05% or less
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'avg_volume': 100_000 # 100K shares/day
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},
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'tier2': {
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'expense_ratio': 0.25, # 0.25% or less
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'aum': 1_000_000_000, # $1B or more
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'tracking_error': 0.10, # 0.10% or less
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'avg_volume': 50_000 # 50K shares/day
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}
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}
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def select_etfs(self, goal: InvestmentGoal) -> List[Dict]:
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"""
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Select ETFs based on investment goals and risk tolerance
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Returns a list of recommended ETFs with allocation percentages
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"""
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try:
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logger.info(f"Starting ETF selection with goal: {goal}")
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# Validate investment goal
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self._validate_investment_goal(goal)
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# Get all available ETFs
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etfs = self._get_available_etfs()
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if not etfs:
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error_msg = "No ETFs available for selection. Please check your data source connection."
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logger.error(error_msg)
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raise ValueError(error_msg)
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logger.info(f"Found {len(etfs)} available ETFs")
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# Filter ETFs based on criteria
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filtered_etfs = self._filter_etfs(etfs, goal)
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if not filtered_etfs:
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error_msg = (
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"No ETFs passed the filtering criteria. This could be due to:\n"
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"1. High expense ratios (>0.25%)\n"
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"2. Low assets under management (<$1B)\n"
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"3. High tracking error (>0.10%)\n"
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"4. Low trading volume (<50K shares/day)\n"
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"Please try adjusting your risk tolerance or investment goals."
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)
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logger.error(error_msg)
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raise ValueError(error_msg)
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logger.info(f"{len(filtered_etfs)} ETFs passed filtering criteria")
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# Score ETFs based on criteria
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scored_etfs = self._score_etfs(filtered_etfs, goal)
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if not scored_etfs:
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error_msg = (
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"No ETFs passed the scoring criteria. This could be due to:\n"
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"1. Poor risk-adjusted returns (low Sharpe ratio)\n"
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"2. High volatility\n"
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"3. Large drawdowns\n"
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"4. Insufficient dividend yield for income goals\n"
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"Please try adjusting your risk tolerance or investment goals."
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)
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logger.error(error_msg)
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raise ValueError(error_msg)
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logger.info(f"{len(scored_etfs)} ETFs passed scoring criteria")
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# Optimize portfolio allocation
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portfolio = self._optimize_portfolio(scored_etfs, goal)
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if not portfolio:
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error_msg = (
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"Failed to optimize portfolio allocation. This could be due to:\n"
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"1. Insufficient diversification opportunities\n"
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"2. Conflicting investment goals\n"
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"3. Risk tolerance constraints\n"
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"Please try adjusting your investment goals or risk tolerance."
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)
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logger.error(error_msg)
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raise ValueError(error_msg)
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logger.info(f"Successfully generated portfolio with {len(portfolio)} ETFs")
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return portfolio
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except ValueError as ve:
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# Re-raise ValueError with the same message
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raise ve
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except Exception as e:
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error_msg = f"Unexpected error during ETF selection: {str(e)}"
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logger.error(error_msg, exc_info=True)
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raise ValueError(f"Unable to generate portfolio suggestions: {str(e)}")
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def _validate_investment_goal(self, goal: InvestmentGoal) -> None:
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"""Validate investment goal parameters"""
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if goal.capital_target <= 0:
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raise ValueError("Capital target must be greater than 0")
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if goal.income_target and goal.income_target <= 0:
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raise ValueError("Income target must be greater than 0")
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if not isinstance(goal.risk_tolerance, RiskTolerance):
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raise ValueError("Risk tolerance must be a valid RiskTolerance enum value")
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def _get_available_etfs(self) -> List[Dict]:
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"""Get list of available ETFs with their data"""
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try:
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# Get list of ETFs
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etf_list = self.data_service.get_etf_list()
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if not etf_list:
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error_msg = "No ETFs available from data service. Please check your data source connection."
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logger.error(error_msg)
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raise ValueError(error_msg)
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logger.info(f"Retrieved {len(etf_list)} ETFs from data service")
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# Process each ETF
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processed_etfs = []
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for ticker in etf_list:
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try:
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# Get ETF data
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etf_data = self.data_service.get_etf_data(ticker)
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if not etf_data:
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logger.warning(f"No data available for {ticker}")
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continue
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# Process ETF data
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processed_etf = {
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'ticker': ticker,
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'name': etf_data.get('info', {}).get('longName', ticker),
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'expense_ratio': etf_data.get('info', {}).get('annualReportExpenseRatio', 0.5) / 100,
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'aum': etf_data.get('info', {}).get('totalAssets', 0),
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'avg_volume': etf_data.get('info', {}).get('averageVolume', 0),
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'tracking_error': 0.0, # Not available from yfinance
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'volatility': float(etf_data.get('volatility', 0)),
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'max_drawdown': float(etf_data.get('max_drawdown', 0)),
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'sharpe_ratio': float(etf_data.get('sharpe_ratio', 0)),
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'top_holding_weight': 0.0, # Not available from yfinance
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'dividend_yield': float(etf_data.get('dividend_yield', 0)) / 100,
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'category': etf_data.get('info', {}).get('category', 'Unknown'),
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'asset_class': etf_data.get('info', {}).get('assetClass', 'Unknown'),
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'sector': etf_data.get('info', {}).get('sector', 'Unknown'),
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'region': etf_data.get('info', {}).get('region', 'Unknown'),
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'strategy': etf_data.get('info', {}).get('strategy', 'Unknown')
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}
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processed_etfs.append(processed_etf)
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except Exception as e:
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logger.warning(f"Error processing ETF {ticker}: {str(e)}")
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continue
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if not processed_etfs:
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error_msg = "No ETFs could be processed. Please check your data source connection."
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logger.error(error_msg)
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raise ValueError(error_msg)
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logger.info(f"Successfully processed {len(processed_etfs)} ETFs")
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return processed_etfs
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except Exception as e:
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error_msg = f"Error fetching available ETFs: {str(e)}"
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logger.error(error_msg)
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raise ValueError(error_msg)
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def _get_etfs_from_yfinance(self) -> List[Dict]:
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"""Get ETF data from yfinance as fallback"""
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try:
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# Get list of ETFs from data service
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etf_list = self.data_service.get_etf_list()
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if not etf_list:
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logger.error("No ETF list available from data service")
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return []
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processed_etfs = []
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for ticker in etf_list:
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try:
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# Get ETF data from data service
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etf_data = self.data_service.get_etf_data(ticker)
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if not etf_data:
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continue
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# Process ETF data
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processed_etf = {
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'ticker': ticker,
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'name': etf_data.get('info', {}).get('longName', ticker),
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'expense_ratio': etf_data.get('info', {}).get('annualReportExpenseRatio', 0.5) / 100,
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'aum': etf_data.get('info', {}).get('totalAssets', 0),
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'avg_volume': etf_data.get('info', {}).get('averageVolume', 0),
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'tracking_error': 0.0, # Not available from yfinance
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'volatility': float(etf_data.get('volatility', 0)),
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'max_drawdown': float(etf_data.get('max_drawdown', 0)),
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'sharpe_ratio': float(etf_data.get('sharpe_ratio', 0)),
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'top_holding_weight': 0.0, # Not available from yfinance
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'dividend_yield': float(etf_data.get('dividend_yield', 0)) / 100,
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'category': etf_data.get('info', {}).get('category', 'Unknown'),
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'asset_class': etf_data.get('info', {}).get('assetClass', 'Unknown'),
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'sector': etf_data.get('info', {}).get('sector', 'Unknown'),
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'region': etf_data.get('info', {}).get('region', 'Unknown'),
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'strategy': etf_data.get('info', {}).get('strategy', 'Unknown')
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}
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processed_etfs.append(processed_etf)
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except Exception as e:
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logger.warning(f"Error processing ETF {ticker} from yfinance: {str(e)}")
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continue
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return processed_etfs
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except Exception as e:
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logger.error(f"Error fetching ETFs from yfinance: {str(e)}")
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return []
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def _filter_etfs(self, etfs: List[Dict], goal: InvestmentGoal) -> List[Dict]:
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"""Filter ETFs based on selection criteria and investment goals"""
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filtered = []
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for etf in etfs:
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try:
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# Skip ETFs with red flags
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if self._has_red_flags(etf):
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continue
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# Apply investment goal specific filters
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if goal.income_target:
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# For income-focused goals, ensure minimum dividend yield
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min_yield = 0.02 # 2% minimum yield
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if goal.income_target > 0.05: # If income target is high
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min_yield = 0.04 # Require higher yield
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if etf.get('dividend_yield', 0) < min_yield:
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continue
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# Apply risk tolerance specific filters
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if goal.risk_tolerance == RiskTolerance.CONSERVATIVE:
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# For conservative investors, focus on:
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# - Lower volatility
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# - Higher dividend yield
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||||||
|
# - Lower max drawdown
|
||||||
|
# - More established ETFs
|
||||||
|
if (etf.get('volatility', 1.0) > 0.15 or
|
||||||
|
etf.get('max_drawdown', 1.0) > 0.20 or
|
||||||
|
etf.get('aum', 0) < 1_000_000_000 or # $1B minimum
|
||||||
|
etf.get('asset_class', '').lower() in ['leveraged', 'inverse']):
|
||||||
|
continue
|
||||||
|
elif goal.risk_tolerance == RiskTolerance.AGGRESSIVE:
|
||||||
|
# For aggressive investors, focus on:
|
||||||
|
# - Higher potential returns (Sharpe ratio)
|
||||||
|
# - Growth potential
|
||||||
|
# - Sector/theme exposure
|
||||||
|
if (etf.get('sharpe_ratio', 0) < 0.8 or
|
||||||
|
etf.get('category', '').lower() in ['bond', 'fixed income'] or
|
||||||
|
etf.get('aum', 0) < 500_000_000): # $500M minimum
|
||||||
|
continue
|
||||||
|
|
||||||
|
# Check if ETF meets tier criteria
|
||||||
|
tier = self._get_etf_tier(etf)
|
||||||
|
if tier > 0: # Only include ETFs that meet at least tier 2 criteria
|
||||||
|
etf['tier'] = tier
|
||||||
|
filtered.append(etf)
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.warning(f"Error filtering ETF {etf.get('ticker', 'unknown')}: {str(e)}")
|
||||||
|
continue
|
||||||
|
|
||||||
|
return filtered
|
||||||
|
|
||||||
|
def _has_red_flags(self, etf: Dict) -> bool:
|
||||||
|
"""Check if ETF has any red flags"""
|
||||||
|
try:
|
||||||
|
# High expense ratio
|
||||||
|
if etf.get('expense_ratio', 1.0) > 0.50:
|
||||||
|
logger.debug(f"ETF {etf.get('ticker')} rejected: High expense ratio")
|
||||||
|
return True
|
||||||
|
|
||||||
|
# Small AUM
|
||||||
|
if etf.get('aum', 0) < 100_000_000: # $100M
|
||||||
|
logger.debug(f"ETF {etf.get('ticker')} rejected: Small AUM")
|
||||||
|
return True
|
||||||
|
|
||||||
|
# High tracking error
|
||||||
|
if etf.get('tracking_error', 1.0) > 0.50:
|
||||||
|
logger.debug(f"ETF {etf.get('ticker')} rejected: High tracking error")
|
||||||
|
return True
|
||||||
|
|
||||||
|
# Concentrated holdings
|
||||||
|
if etf.get('top_holding_weight', 0) > 0.20: # 20%
|
||||||
|
logger.debug(f"ETF {etf.get('ticker')} rejected: Concentrated holdings")
|
||||||
|
return True
|
||||||
|
|
||||||
|
return False
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.warning(f"Error checking red flags for ETF {etf.get('ticker', 'unknown')}: {str(e)}")
|
||||||
|
return True
|
||||||
|
|
||||||
|
def _get_etf_tier(self, etf: Dict) -> int:
|
||||||
|
"""Determine ETF tier based on criteria"""
|
||||||
|
try:
|
||||||
|
# Tier 1 criteria
|
||||||
|
if (etf.get('expense_ratio', 1.0) <= self.selection_criteria['tier1']['expense_ratio'] and
|
||||||
|
etf.get('aum', 0) >= self.selection_criteria['tier1']['aum'] and
|
||||||
|
etf.get('tracking_error', 1.0) <= self.selection_criteria['tier1']['tracking_error'] and
|
||||||
|
etf.get('avg_volume', 0) >= self.selection_criteria['tier1']['avg_volume']):
|
||||||
|
return 1
|
||||||
|
|
||||||
|
# Tier 2 criteria
|
||||||
|
if (etf.get('expense_ratio', 1.0) <= self.selection_criteria['tier2']['expense_ratio'] and
|
||||||
|
etf.get('aum', 0) >= self.selection_criteria['tier2']['aum'] and
|
||||||
|
etf.get('tracking_error', 1.0) <= self.selection_criteria['tier2']['tracking_error'] and
|
||||||
|
etf.get('avg_volume', 0) >= self.selection_criteria['tier2']['avg_volume']):
|
||||||
|
return 2
|
||||||
|
|
||||||
|
return 0
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.warning(f"Error determining tier for ETF {etf.get('ticker', 'unknown')}: {str(e)}")
|
||||||
|
return 0
|
||||||
|
|
||||||
|
def _score_etfs(self, etfs: List[Dict], goal: InvestmentGoal) -> List[Dict]:
|
||||||
|
"""Score ETFs based on investment goals and risk tolerance"""
|
||||||
|
scored = []
|
||||||
|
|
||||||
|
for etf in etfs:
|
||||||
|
try:
|
||||||
|
score = 0
|
||||||
|
score_components = {}
|
||||||
|
|
||||||
|
# Base score from tier
|
||||||
|
tier_score = (3 - etf['tier']) * 10 # Higher score for better tier
|
||||||
|
score += tier_score
|
||||||
|
score_components['tier'] = tier_score
|
||||||
|
|
||||||
|
# Risk-adjusted return score
|
||||||
|
if etf.get('sharpe_ratio'):
|
||||||
|
sharpe_score = min(etf['sharpe_ratio'] * 5, 20) # Max 20 points
|
||||||
|
score += sharpe_score
|
||||||
|
score_components['sharpe'] = sharpe_score
|
||||||
|
|
||||||
|
# Volatility score (lower is better)
|
||||||
|
if etf.get('volatility'):
|
||||||
|
vol_score = max(0, 20 - (etf['volatility'] * 100)) # Max 20 points
|
||||||
|
score += vol_score
|
||||||
|
score_components['volatility'] = vol_score
|
||||||
|
|
||||||
|
# Income goal specific scoring
|
||||||
|
if goal.income_target:
|
||||||
|
# Score based on dividend yield and stability
|
||||||
|
if etf.get('dividend_yield'):
|
||||||
|
# Higher score for ETFs with yield closer to target
|
||||||
|
target_yield = goal.income_target / goal.capital_target
|
||||||
|
yield_diff = abs(etf['dividend_yield'] - target_yield)
|
||||||
|
div_score = max(0, 20 - (yield_diff * 100)) # Max 20 points
|
||||||
|
score += div_score
|
||||||
|
score_components['dividend'] = div_score
|
||||||
|
|
||||||
|
# Bonus for stable dividends
|
||||||
|
if etf.get('dividend_trend', 0) > 0:
|
||||||
|
score += 5
|
||||||
|
score_components['dividend_stability'] = 5
|
||||||
|
|
||||||
|
# AUM score
|
||||||
|
aum_billions = etf.get('aum', 0) / 1_000_000_000
|
||||||
|
aum_score = min(aum_billions, 10) # Max 10 points
|
||||||
|
score += aum_score
|
||||||
|
score_components['aum'] = aum_score
|
||||||
|
|
||||||
|
# Risk tolerance specific scoring
|
||||||
|
if goal.risk_tolerance == RiskTolerance.CONSERVATIVE:
|
||||||
|
# Favor stability and income
|
||||||
|
if etf.get('dividend_yield', 0) > 0.03: # 3% yield
|
||||||
|
score += 10
|
||||||
|
score_components['income_focus'] = 10
|
||||||
|
if etf.get('volatility', 1.0) < 0.12: # Low volatility
|
||||||
|
score += 10
|
||||||
|
score_components['stability'] = 10
|
||||||
|
if etf.get('asset_class', '').lower() in ['equity', 'fixed income']:
|
||||||
|
score += 5
|
||||||
|
score_components['asset_class'] = 5
|
||||||
|
elif goal.risk_tolerance == RiskTolerance.AGGRESSIVE:
|
||||||
|
# Favor growth and momentum
|
||||||
|
if etf.get('sharpe_ratio', 0) > 1.2: # High Sharpe
|
||||||
|
score += 10
|
||||||
|
score_components['risk_adjusted_return'] = 10
|
||||||
|
if etf.get('category', '').lower() in ['technology', 'growth']:
|
||||||
|
score += 10
|
||||||
|
score_components['growth_potential'] = 10
|
||||||
|
if etf.get('strategy', '').lower() in ['momentum', 'growth']:
|
||||||
|
score += 5
|
||||||
|
score_components['strategy'] = 5
|
||||||
|
|
||||||
|
# Add score and components to ETF data
|
||||||
|
etf['score'] = score
|
||||||
|
etf['score_components'] = score_components
|
||||||
|
scored.append(etf)
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.warning(f"Error scoring ETF {etf.get('ticker', 'unknown')}: {str(e)}")
|
||||||
|
continue
|
||||||
|
|
||||||
|
# Sort by score in descending order
|
||||||
|
scored.sort(key=lambda x: x.get('score', 0), reverse=True)
|
||||||
|
return scored
|
||||||
|
|
||||||
|
def _optimize_portfolio(self, scored_etfs: List[Dict], goal: InvestmentGoal) -> List[Dict]:
|
||||||
|
"""Optimize portfolio allocation based on investment goals"""
|
||||||
|
try:
|
||||||
|
# Select top ETFs based on score
|
||||||
|
top_etfs = scored_etfs[:5] # Limit to top 5 ETFs
|
||||||
|
|
||||||
|
# Calculate initial weights based on scores
|
||||||
|
total_score = sum(etf['score'] for etf in top_etfs)
|
||||||
|
if total_score == 0:
|
||||||
|
raise ValueError("No valid scores for portfolio optimization")
|
||||||
|
|
||||||
|
# Assign weights based on scores
|
||||||
|
for etf in top_etfs:
|
||||||
|
etf['weight'] = etf['score'] / total_score
|
||||||
|
|
||||||
|
# Adjust weights based on risk tolerance
|
||||||
|
if goal.risk_tolerance == RiskTolerance.CONSERVATIVE:
|
||||||
|
# Favor ETFs with lower volatility and higher Sharpe ratio
|
||||||
|
for etf in top_etfs:
|
||||||
|
if etf.get('volatility', 0) > 0.15 or etf.get('sharpe_ratio', 0) < 1.0:
|
||||||
|
etf['weight'] *= 0.5
|
||||||
|
elif goal.risk_tolerance == RiskTolerance.AGGRESSIVE:
|
||||||
|
# Favor ETFs with higher potential returns
|
||||||
|
for etf in top_etfs:
|
||||||
|
if etf.get('sharpe_ratio', 0) > 1.5:
|
||||||
|
etf['weight'] *= 1.5
|
||||||
|
|
||||||
|
# Normalize weights
|
||||||
|
total_weight = sum(etf['weight'] for etf in top_etfs)
|
||||||
|
for etf in top_etfs:
|
||||||
|
etf['weight'] /= total_weight
|
||||||
|
|
||||||
|
# Format portfolio output
|
||||||
|
portfolio = []
|
||||||
|
for etf in top_etfs:
|
||||||
|
portfolio.append({
|
||||||
|
'ticker': etf['ticker'],
|
||||||
|
'name': etf['name'],
|
||||||
|
'allocation': etf['weight'],
|
||||||
|
'amount': goal.capital_target * etf['weight'],
|
||||||
|
'score': etf['score'],
|
||||||
|
'tier': etf['tier'],
|
||||||
|
'metrics': {
|
||||||
|
'expense_ratio': etf.get('expense_ratio'),
|
||||||
|
'aum': etf.get('aum'),
|
||||||
|
'tracking_error': etf.get('tracking_error'),
|
||||||
|
'volatility': etf.get('volatility'),
|
||||||
|
'sharpe_ratio': etf.get('sharpe_ratio'),
|
||||||
|
'dividend_yield': etf.get('dividend_yield'),
|
||||||
|
'max_drawdown': etf.get('max_drawdown')
|
||||||
|
}
|
||||||
|
})
|
||||||
|
|
||||||
|
return portfolio
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.error(f"Error optimizing portfolio: {str(e)}")
|
||||||
|
raise ValueError(f"Failed to optimize portfolio allocation: {str(e)}")
|
||||||
@ -2848,8 +2848,88 @@ if st.session_state.simulation_run and st.session_state.df_data is not None:
|
|||||||
st.error("Unable to analyze ETF erosion risk. Please try again.")
|
st.error("Unable to analyze ETF erosion risk. Please try again.")
|
||||||
|
|
||||||
with tab4:
|
with tab4:
|
||||||
st.subheader("🤖 AI Suggestions")
|
st.subheader("🤖 AI Portfolio Suggestions")
|
||||||
st.write("This tab will contain AI suggestions for portfolio optimization.")
|
|
||||||
|
try:
|
||||||
|
# Get values from session state
|
||||||
|
capital_target = st.session_state.initial_capital if st.session_state.mode == "Capital Target" else 3000.0
|
||||||
|
income_target = st.session_state.target * 12 if st.session_state.mode == "Income Target" else 0.0
|
||||||
|
risk_tolerance = st.session_state.risk_tolerance
|
||||||
|
investment_horizon = 5 # Default to 5 years if not specified
|
||||||
|
|
||||||
|
# Initialize services
|
||||||
|
from ETF_Portal.services.data_service import DataService
|
||||||
|
from ETF_Portal.services.etf_selection_service import ETFSelectionService, InvestmentGoal, RiskTolerance
|
||||||
|
|
||||||
|
data_service = DataService()
|
||||||
|
selection_service = ETFSelectionService(data_service)
|
||||||
|
|
||||||
|
# Create investment goal
|
||||||
|
goal = InvestmentGoal(
|
||||||
|
capital_target=capital_target,
|
||||||
|
income_target=income_target if income_target > 0 else None,
|
||||||
|
risk_tolerance=RiskTolerance[risk_tolerance.upper()],
|
||||||
|
investment_horizon=investment_horizon
|
||||||
|
)
|
||||||
|
|
||||||
|
# Get AI suggestions
|
||||||
|
with st.spinner("Analyzing ETFs and generating portfolio suggestions..."):
|
||||||
|
try:
|
||||||
|
portfolio = selection_service.select_etfs(goal)
|
||||||
|
|
||||||
|
if portfolio:
|
||||||
|
# Display portfolio suggestions
|
||||||
|
st.success("Portfolio suggestions generated successfully!")
|
||||||
|
|
||||||
|
# Create a DataFrame for better display
|
||||||
|
portfolio_df = pd.DataFrame(portfolio)
|
||||||
|
portfolio_df['Allocation (%)'] = portfolio_df['allocation'] * 100
|
||||||
|
portfolio_df['Amount ($)'] = portfolio_df['amount']
|
||||||
|
|
||||||
|
# Display portfolio summary
|
||||||
|
st.write("### Portfolio Summary")
|
||||||
|
st.dataframe(
|
||||||
|
portfolio_df[['ticker', 'name', 'Allocation (%)', 'Amount ($)']],
|
||||||
|
hide_index=True
|
||||||
|
)
|
||||||
|
|
||||||
|
# Display detailed metrics
|
||||||
|
st.write("### Detailed Metrics")
|
||||||
|
metrics_df = pd.DataFrame([
|
||||||
|
{
|
||||||
|
'Ticker': etf['ticker'],
|
||||||
|
'Expense Ratio (%)': etf['metrics']['expense_ratio'] * 100,
|
||||||
|
'AUM ($B)': etf['metrics']['aum'] / 1e9,
|
||||||
|
'Volatility (%)': etf['metrics']['volatility'] * 100,
|
||||||
|
'Max Drawdown (%)': etf['metrics']['max_drawdown'] * 100,
|
||||||
|
'Sharpe Ratio': etf['metrics']['sharpe_ratio'],
|
||||||
|
'Dividend Yield (%)': etf['metrics']['dividend_yield'] * 100
|
||||||
|
}
|
||||||
|
for etf in portfolio
|
||||||
|
])
|
||||||
|
st.dataframe(metrics_df, hide_index=True)
|
||||||
|
|
||||||
|
# Display portfolio allocation chart
|
||||||
|
st.write("### Portfolio Allocation")
|
||||||
|
fig = px.pie(
|
||||||
|
portfolio_df,
|
||||||
|
values='Allocation (%)',
|
||||||
|
names='ticker',
|
||||||
|
title='Portfolio Allocation by ETF'
|
||||||
|
)
|
||||||
|
st.plotly_chart(fig)
|
||||||
|
else:
|
||||||
|
st.error("No portfolio suggestions could be generated. Please try different parameters.")
|
||||||
|
|
||||||
|
except ValueError as e:
|
||||||
|
st.error(str(e))
|
||||||
|
except Exception as e:
|
||||||
|
st.error(f"An unexpected error occurred: {str(e)}")
|
||||||
|
logger.error(f"Error generating portfolio suggestions: {str(e)}", exc_info=True)
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
st.error(f"Error initializing services: {str(e)}")
|
||||||
|
logger.error(f"Error initializing services: {str(e)}", exc_info=True)
|
||||||
|
|
||||||
with tab5:
|
with tab5:
|
||||||
st.subheader("📊 ETF Details")
|
st.subheader("📊 ETF Details")
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user