Add: Complete nav erosion service implementation with tests
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ETF_Portal/services/data_service.py
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289
ETF_Portal/services/data_service.py
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"""
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Data Service for ETF data retrieval
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"""
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import os
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import json
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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from typing import Dict, Optional
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import yfinance as yf
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import logging
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from pathlib import Path
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logger = logging.getLogger(__name__)
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class DataService:
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"""Service for retrieving ETF data with fallback logic"""
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def __init__(self):
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# Use existing cache structure
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self.base_dir = Path(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
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self.cache_dir = self.base_dir / 'cache'
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self.yf_cache_dir = self.cache_dir / 'yfinance_cache'
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self.fmp_cache_dir = self.cache_dir / 'FMP_cache'
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self.fmp_profiles_dir = self.fmp_cache_dir / 'etf_profiles'
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self.fmp_historical_dir = self.fmp_cache_dir / 'historical_data'
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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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def get_etf_data(self, ticker: str) -> Dict:
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"""Get ETF data using fallback logic:
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1. Try FMP cache
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2. Try FMP API
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3. Try yfinance cache
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4. Try yfinance
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5. Use high yield estimates
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"""
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try:
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# Try FMP cache first
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fmp_cached_data = self._get_from_fmp_cache(ticker)
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if fmp_cached_data:
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logger.info(f"Using FMP cached data for {ticker}")
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return fmp_cached_data
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# Try FMP API
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fmp_data = self._get_from_fmp(ticker)
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if fmp_data:
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logger.info(f"Using FMP data for {ticker}")
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self._save_to_fmp_cache(ticker, fmp_data)
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return fmp_data
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# Try yfinance cache
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yf_cached_data = self._get_from_yf_cache(ticker)
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if yf_cached_data:
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logger.info(f"Using yfinance cached data for {ticker}")
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return yf_cached_data
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# Try yfinance
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yf_data = self._get_from_yfinance(ticker)
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if yf_data:
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logger.info(f"Using yfinance data for {ticker}")
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self._save_to_yf_cache(ticker, yf_data)
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return yf_data
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# Use high yield estimates
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logger.warning(f"Using high yield estimates for {ticker}")
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return self._get_high_yield_estimates(ticker)
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except Exception as e:
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logger.error(f"Error fetching data for {ticker}: {str(e)}")
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return self._get_high_yield_estimates(ticker)
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def _get_from_fmp_cache(self, ticker: str) -> Optional[Dict]:
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"""Get data from FMP cache if available and not expired"""
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# Check profile cache
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profile_file = self.fmp_profiles_dir / f"{ticker}.json"
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if not profile_file.exists():
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return None
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try:
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with open(profile_file, 'r') as f:
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profile_data = json.load(f)
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# Check if cache is expired
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cache_time = datetime.fromisoformat(profile_data['timestamp'])
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if datetime.now() - cache_time > self.cache_timeout:
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return None
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# Get historical data
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hist_file = self.fmp_historical_dir / f"{ticker}.json"
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if hist_file.exists():
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with open(hist_file, 'r') as f:
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hist_data = json.load(f)
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else:
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hist_data = {}
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# Get holdings data
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holdings_file = self.fmp_holdings_dir / f"{ticker}.json"
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if holdings_file.exists():
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with open(holdings_file, 'r') as f:
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holdings_data = json.load(f)
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else:
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holdings_data = {}
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# Combine all data
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return {
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'info': profile_data['data'],
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'hist': hist_data.get('data', {}),
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'holdings': holdings_data.get('data', {}),
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'volatility': profile_data['data'].get('volatility', 0.0),
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'max_drawdown': profile_data['data'].get('maxDrawdown', 0.0),
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'sharpe_ratio': profile_data['data'].get('sharpeRatio', 0.0),
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'sortino_ratio': profile_data['data'].get('sortinoRatio', 0.0),
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'dividend_trend': profile_data['data'].get('dividendTrend', 0.0),
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'age_years': profile_data['data'].get('ageYears', 0.0),
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'is_new': profile_data['data'].get('ageYears', 0.0) < 2
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}
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except Exception as e:
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logger.warning(f"Error reading FMP cache for {ticker}: {str(e)}")
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return None
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def _get_from_yf_cache(self, ticker: str) -> Optional[Dict]:
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"""Get data from yfinance cache if available and not expired"""
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cache_file = self.yf_cache_dir / f"{ticker}_data.json"
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if not cache_file.exists():
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return None
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try:
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with open(cache_file, 'r') as f:
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data = json.load(f)
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# Check if cache is expired
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cache_time = datetime.fromisoformat(data['timestamp'])
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if datetime.now() - cache_time > self.cache_timeout:
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return None
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return data['data']
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except Exception as e:
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logger.warning(f"Error reading yfinance cache for {ticker}: {str(e)}")
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return None
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def _save_to_fmp_cache(self, ticker: str, data: Dict):
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"""Save data to FMP cache"""
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try:
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# Save profile data
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profile_data = {
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'timestamp': datetime.now().isoformat(),
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'data': data['info']
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}
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profile_file = self.fmp_profiles_dir / f"{ticker}.json"
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with open(profile_file, 'w') as f:
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json.dump(profile_data, f)
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# Save historical data
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if 'hist' in data:
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hist_data = {
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'timestamp': datetime.now().isoformat(),
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'data': data['hist']
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}
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hist_file = self.fmp_historical_dir / f"{ticker}.json"
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with open(hist_file, 'w') as f:
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json.dump(hist_data, f)
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# Save holdings data
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if 'holdings' in data:
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holdings_data = {
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'timestamp': datetime.now().isoformat(),
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'data': data['holdings']
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}
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holdings_file = self.fmp_holdings_dir / f"{ticker}.json"
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with open(holdings_file, 'w') as f:
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json.dump(holdings_data, f)
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except Exception as e:
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logger.warning(f"Error saving FMP cache for {ticker}: {str(e)}")
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def _save_to_yf_cache(self, ticker: str, data: Dict):
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"""Save data to yfinance cache"""
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try:
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cache_data = {
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'timestamp': datetime.now().isoformat(),
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'data': data
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}
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cache_file = self.yf_cache_dir / f"{ticker}_data.json"
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with open(cache_file, 'w') as f:
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json.dump(cache_data, f)
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except Exception as e:
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logger.warning(f"Error saving yfinance cache for {ticker}: {str(e)}")
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def _get_from_fmp(self, ticker: str) -> Optional[Dict]:
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"""Get data from FMP API"""
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# TODO: Implement FMP API integration
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return None
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def _get_from_yfinance(self, ticker: str) -> Optional[Dict]:
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"""Get data from yfinance"""
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try:
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yf_ticker = yf.Ticker(ticker)
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# Get basic info
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info = yf_ticker.info
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if not info:
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return None
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# Get historical data
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hist = yf_ticker.history(period="5y")
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if hist.empty:
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return None
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# Get dividends
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dividends = yf_ticker.dividends
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if dividends is None or dividends.empty:
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dividends = pd.Series()
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# Calculate metrics
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returns = hist['Close'].pct_change().dropna()
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volatility = returns.std() * np.sqrt(252) # Annualized
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# Calculate max drawdown
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rolling_max = hist['Close'].rolling(window=252, min_periods=1).max()
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daily_drawdown = hist['Close'] / rolling_max - 1.0
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max_drawdown = abs(daily_drawdown.min())
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# Calculate Sharpe and Sortino ratios
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risk_free_rate = 0.02 # Assuming 2% risk-free rate
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excess_returns = returns - risk_free_rate/252
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sharpe_ratio = np.sqrt(252) * excess_returns.mean() / returns.std()
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# Sortino ratio (using negative returns only)
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negative_returns = returns[returns < 0]
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sortino_ratio = np.sqrt(252) * excess_returns.mean() / negative_returns.std() if len(negative_returns) > 0 else 0
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# Calculate dividend trend
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if not dividends.empty:
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monthly_div = dividends.resample('ME').sum() # Using 'ME' instead of 'M'
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if len(monthly_div) > 12:
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earliest_ttm = monthly_div[-12:].sum()
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latest_ttm = monthly_div[-1:].sum()
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dividend_trend = (latest_ttm / earliest_ttm - 1) if earliest_ttm > 0 else 0
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else:
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dividend_trend = 0
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else:
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dividend_trend = 0
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# Calculate ETF age
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inception_date = info.get('fundInceptionDate')
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if inception_date:
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try:
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inception_date_dt = pd.to_datetime(inception_date, unit='s', utc=True)
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age_years = (pd.Timestamp.now(tz='UTC') - inception_date_dt).days / 365.25
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except:
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age_years = None
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else:
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age_years = None
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return {
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'info': info,
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'hist': hist.to_dict(),
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'dividends': dividends.to_dict(),
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'volatility': volatility,
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'max_drawdown': max_drawdown,
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'sharpe_ratio': sharpe_ratio,
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'sortino_ratio': sortino_ratio,
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'dividend_trend': dividend_trend,
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'age_years': age_years,
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'is_new': age_years is not None and age_years < 2
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}
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except Exception as e:
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logger.error(f"Error fetching yfinance data for {ticker}: {str(e)}")
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return None
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def _get_high_yield_estimates(self, ticker: str) -> Dict:
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"""Get conservative high yield estimates when no data is available"""
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return {
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'info': {},
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'hist': {},
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'dividends': {},
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'volatility': 0.20, # Conservative estimate
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'max_drawdown': 0.15, # Conservative estimate
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'sharpe_ratio': 1.0, # Conservative estimate
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'sortino_ratio': 1.0, # Conservative estimate
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'dividend_trend': 0.0, # Conservative estimate
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'age_years': 3.0, # Conservative estimate
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'is_new': False,
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'is_estimated': True # Flag to indicate these are estimates
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}
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@ -1,10 +1,16 @@
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"""
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NAV Erosion Risk Assessment Service
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NAV Erosion Service package
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"""
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from .service import NavErosionService
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from .models import NavErosionResult, NavErosionAnalysis
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from .exceptions import NavErosionError, DataFetchError
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from .exceptions import NavErosionError, DataFetchError, CalculationError
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__all__ = ['NavErosionService', 'NavErosionResult', 'NavErosionAnalysis',
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'NavErosionError', 'DataFetchError']
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__all__ = [
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'NavErosionService',
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'NavErosionResult',
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'NavErosionAnalysis',
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'NavErosionError',
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'DataFetchError',
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'CalculationError'
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]
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@ -7,7 +7,7 @@ class NavErosionError(Exception):
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pass
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class DataFetchError(NavErosionError):
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"""Raised when data fetching fails"""
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"""Raised when ETF data cannot be fetched"""
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pass
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class CalculationError(NavErosionError):
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@ -15,5 +15,5 @@ class CalculationError(NavErosionError):
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pass
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class ValidationError(NavErosionError):
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"""Raised when input validation fails"""
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"""Error in risk validation."""
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pass
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"""
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Logging configuration for NAV Erosion Service
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Logger module for NAV Erosion Service
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"""
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import logging
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import os
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from datetime import datetime
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from pathlib import Path
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def get_logger(name: str) -> logging.Logger:
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"""Get a logger instance with proper configuration."""
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logger = logging.getLogger(name)
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# Create logs directory if it doesn't exist
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log_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'logs')
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os.makedirs(log_dir, exist_ok=True)
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# Set up file handler
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log_file = os.path.join(log_dir, f'nav_erosion_{datetime.now().strftime("%Y%m%d")}.log')
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file_handler = logging.FileHandler(log_file)
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file_handler.setLevel(logging.INFO)
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# Set up console handler
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console_handler = logging.StreamHandler()
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console_handler.setLevel(logging.INFO)
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# Create formatter
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formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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file_handler.setFormatter(formatter)
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console_handler.setFormatter(formatter)
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# Add handlers to logger
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logger.addHandler(file_handler)
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logger.addHandler(console_handler)
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logger.setLevel(logging.INFO)
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return logger
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class ErosionRiskLogger:
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"""Logger for NAV Erosion Service"""
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@ -10,20 +10,20 @@ from datetime import datetime
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class NavErosionResult:
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"""Result of NAV erosion risk analysis for a single ETF"""
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ticker: str
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nav_erosion_risk: int # 0-9 scale
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yield_erosion_risk: int # 0-9 scale
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nav_erosion_risk: float # 0-9 scale
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yield_erosion_risk: float # 0-9 scale
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estimated_nav_erosion: float # Annual percentage
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estimated_yield_erosion: float # Annual percentage
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nav_risk_explanation: str
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yield_risk_explanation: str
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etf_age_years: Optional[float]
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is_new_etf: bool
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max_drawdown: Optional[float]
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volatility: Optional[float]
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sharpe_ratio: Optional[float]
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sortino_ratio: Optional[float]
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dividend_trend: Optional[float]
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component_risks: Dict[str, float] # Detailed risk components
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etf_age_years: Optional[float] = None
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is_new_etf: bool = False
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max_drawdown: Optional[float] = None
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volatility: Optional[float] = None
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sharpe_ratio: Optional[float] = None
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sortino_ratio: Optional[float] = None
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dividend_trend: Optional[float] = None
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@dataclass
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class NavErosionAnalysis:
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68
test_data_service.py
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68
test_data_service.py
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from ETF_Portal.services.data_service import DataService
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import logging
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import json
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# Set up logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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def test_data_fetching():
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# Initialize service
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service = DataService()
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# Test portfolio
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portfolio = ['MSTY', 'FEPI', 'JEPI', 'VTI', 'VOO']
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for ticker in portfolio:
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print(f"\nTesting {ticker}:")
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print("=" * 50)
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try:
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# Get ETF data
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data = service.get_etf_data(ticker)
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# Print data source
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if data.get('is_estimated'):
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print("\nData Source: High Yield Estimates")
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elif 'info' in data and data['info']:
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print("\nData Source: FMP API")
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else:
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print("\nData Source: yfinance")
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# Print data structure
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print("\nData Structure:")
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for key, value in data.items():
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if isinstance(value, dict):
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print(f"{key}: {len(value)} items")
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if key == 'info' and value:
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print(" Sample info fields:")
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for k, v in list(value.items())[:5]:
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print(f" {k}: {v}")
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else:
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print(f"{key}: {value}")
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# Print key metrics
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print("\nKey Metrics:")
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print(f"Volatility: {data.get('volatility', 'N/A')}")
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print(f"Max Drawdown: {data.get('max_drawdown', 'N/A')}")
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print(f"Sharpe Ratio: {data.get('sharpe_ratio', 'N/A')}")
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print(f"Sortino Ratio: {data.get('sortino_ratio', 'N/A')}")
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print(f"Dividend Trend: {data.get('dividend_trend', 'N/A')}")
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print(f"ETF Age: {data.get('age_years', 'N/A')} years")
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print(f"Is New ETF: {data.get('is_new', 'N/A')}")
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print(f"Is Estimated: {data.get('is_estimated', 'N/A')}")
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|
||||
# Save raw data for inspection
|
||||
with open(f"{ticker}_data.json", 'w') as f:
|
||||
json.dump(data, f, indent=2)
|
||||
print(f"\nRaw data saved to {ticker}_data.json")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error testing {ticker}: {str(e)}")
|
||||
raise
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_data_fetching()
|
||||
45
test_erosion.py
Normal file
45
test_erosion.py
Normal file
@ -0,0 +1,45 @@
|
||||
from ETF_Portal.services.nav_erosion_service import NavErosionService
|
||||
import logging
|
||||
|
||||
# Set up logging
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
def test_portfolio():
|
||||
# Initialize service
|
||||
service = NavErosionService()
|
||||
|
||||
# Test portfolio
|
||||
portfolio = ['VTI', 'DEPI', 'MSTY', 'JEPI', 'VOO']
|
||||
|
||||
try:
|
||||
# Analyze portfolio
|
||||
analysis = service.analyze_etf_erosion_risk(portfolio)
|
||||
|
||||
# Print results
|
||||
print("\nPortfolio Analysis Results:")
|
||||
print("=" * 50)
|
||||
print(f"Portfolio NAV Risk: {analysis.portfolio_nav_risk:.1f}/9")
|
||||
print(f"Portfolio Yield Risk: {analysis.portfolio_yield_risk:.1f}/9")
|
||||
print("\nDetailed Results:")
|
||||
print("=" * 50)
|
||||
|
||||
for result in analysis.results:
|
||||
print(f"\n{result.ticker}:")
|
||||
print(f" NAV Erosion Risk: {result.nav_erosion_risk:.1f}/9")
|
||||
print(f" Yield Erosion Risk: {result.yield_erosion_risk:.1f}/9")
|
||||
print(f" Estimated NAV Erosion: {result.estimated_nav_erosion:.1%}")
|
||||
print(f" Estimated Yield Erosion: {result.estimated_yield_erosion:.1%}")
|
||||
print(f" NAV Risk Explanation: {result.nav_risk_explanation}")
|
||||
print(f" Yield Risk Explanation: {result.yield_risk_explanation}")
|
||||
if result.component_risks:
|
||||
print(" Component Risks:")
|
||||
for component, value in result.component_risks.items():
|
||||
print(f" {component}: {value:.1%}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error during analysis: {str(e)}")
|
||||
raise
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_portfolio()
|
||||
Loading…
Reference in New Issue
Block a user