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feat: improve logging for cache and API calls tracking

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Pascal BIBEHE 2025-05-27 16:02:15 +02:00
parent 1ff511ebe1
commit 027febf7da
1 changed files with 459 additions and 228 deletions
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pages/ETF_Portfolio_Builder.py
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@ -5,7 +5,7 @@ import plotly.express as px
import plotly.graph_objects as go import plotly.graph_objects as go
from pathlib import Path from pathlib import Path
import json import json
from datetime import datetime from datetime import datetime, timedelta
from typing import List, Dict, Tuple, Optional, Any, Callable, T from typing import List, Dict, Tuple, Optional, Any, Callable, T
import time import time
import threading import threading
@ -21,14 +21,34 @@ import traceback
from dotenv import load_dotenv from dotenv import load_dotenv
# Load environment variables # Load environment variables
load_dotenv() load_dotenv(override=True) # Force reload of environment variables
# Configure logging # Configure logging
logging.basicConfig(level=logging.INFO) logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
# Global settings
USE_FMP_API = True # Default to using FMP API if available
# FMP API configuration # FMP API configuration
FMP_API_KEY = st.session_state.get('fmp_api_key', os.getenv('FMP_API_KEY', '')) FMP_API_KEY = os.getenv('FMP_API_KEY')
if not FMP_API_KEY:
logger.warning("FMP_API_KEY not found in environment variables")
logger.warning("Current environment variables: %s", dict(os.environ))
logger.warning("Current working directory: %s", os.getcwd())
logger.warning("Files in current directory: %s", os.listdir('.'))
if os.path.exists('.env'):
logger.warning(".env file exists")
with open('.env', 'r') as f:
logger.warning("Contents of .env file: %s", f.read())
else:
logger.warning(".env file does not exist")
else:
logger.info("FMP_API_KEY loaded successfully")
# Mask the API key for security in logs
masked_key = FMP_API_KEY[:4] + '*' * (len(FMP_API_KEY) - 8) + FMP_API_KEY[-4:]
logger.info("FMP_API_KEY (masked): %s", masked_key)
FMP_BASE_URL = "https://financialmodelingprep.com/api/v3" FMP_BASE_URL = "https://financialmodelingprep.com/api/v3"
# High-yield ETFs reference data # High-yield ETFs reference data
@ -324,7 +344,7 @@ def optimize_portfolio_allocation(
etf_metrics: List[Dict[str, Any]], etf_metrics: List[Dict[str, Any]],
risk_tolerance: str, risk_tolerance: str,
correlation_matrix: pd.DataFrame correlation_matrix: pd.DataFrame
) -> Dict[str, float]: ) -> List[Dict[str, Any]]:
""" """
Optimize portfolio allocation based on risk tolerance and ETF metrics. Optimize portfolio allocation based on risk tolerance and ETF metrics.
@ -334,21 +354,26 @@ def optimize_portfolio_allocation(
correlation_matrix: Correlation matrix between ETFs correlation_matrix: Correlation matrix between ETFs
Returns: Returns:
Dictionary with ETF tickers and their allocations List of dictionaries with ETF tickers and their allocations
""" """
try: try:
logger.info(f"Optimizing portfolio allocation for {risk_tolerance} risk tolerance")
logger.info(f"ETF metrics: {etf_metrics}")
# Group ETFs by risk category # Group ETFs by risk category
low_risk = [etf for etf in etf_metrics if etf["Risk Level"] == "Low"] low_risk = [etf for etf in etf_metrics if etf.get("Risk Level", "Unknown") == "Low"]
medium_risk = [etf for etf in etf_metrics if etf["Risk Level"] == "Medium"] medium_risk = [etf for etf in etf_metrics if etf.get("Risk Level", "Unknown") == "Medium"]
high_risk = [etf for etf in etf_metrics if etf["Risk Level"] == "High"] high_risk = [etf for etf in etf_metrics if etf.get("Risk Level", "Unknown") == "High"]
logger.info(f"Risk groups - Low: {len(low_risk)}, Medium: {len(medium_risk)}, High: {len(high_risk)}")
# Sort ETFs by score within each risk category # Sort ETFs by score within each risk category
low_risk.sort(key=lambda x: x["score"], reverse=True) low_risk.sort(key=lambda x: x.get("score", 0), reverse=True)
medium_risk.sort(key=lambda x: x["score"], reverse=True) medium_risk.sort(key=lambda x: x.get("score", 0), reverse=True)
high_risk.sort(key=lambda x: x["score"], reverse=True) high_risk.sort(key=lambda x: x.get("score", 0), reverse=True)
# Initialize allocations # Initialize allocations
allocations = {} allocations = []
if risk_tolerance == "Conservative": if risk_tolerance == "Conservative":
# Conservative allocation # Conservative allocation
@ -356,19 +381,19 @@ def optimize_portfolio_allocation(
# Allocate 50% to low-risk ETFs # Allocate 50% to low-risk ETFs
low_risk_alloc = 50.0 / len(low_risk) low_risk_alloc = 50.0 / len(low_risk)
for etf in low_risk: for etf in low_risk:
allocations[etf["Ticker"]] = low_risk_alloc allocations.append({"ticker": etf["Ticker"], "allocation": low_risk_alloc})
if medium_risk: if medium_risk:
# Allocate 30% to medium-risk ETFs # Allocate 30% to medium-risk ETFs
medium_risk_alloc = 30.0 / len(medium_risk) medium_risk_alloc = 30.0 / len(medium_risk)
for etf in medium_risk: for etf in medium_risk:
allocations[etf["Ticker"]] = medium_risk_alloc allocations.append({"ticker": etf["Ticker"], "allocation": medium_risk_alloc})
if high_risk: if high_risk:
# Allocate 20% to high-risk ETFs # Allocate 20% to high-risk ETFs
high_risk_alloc = 20.0 / len(high_risk) high_risk_alloc = 20.0 / len(high_risk)
for etf in high_risk: for etf in high_risk:
allocations[etf["Ticker"]] = high_risk_alloc allocations.append({"ticker": etf["Ticker"], "allocation": high_risk_alloc})
elif risk_tolerance == "Moderate": elif risk_tolerance == "Moderate":
# Moderate allocation # Moderate allocation
@ -376,19 +401,19 @@ def optimize_portfolio_allocation(
# Allocate 30% to low-risk ETFs # Allocate 30% to low-risk ETFs
low_risk_alloc = 30.0 / len(low_risk) low_risk_alloc = 30.0 / len(low_risk)
for etf in low_risk: for etf in low_risk:
allocations[etf["Ticker"]] = low_risk_alloc allocations.append({"ticker": etf["Ticker"], "allocation": low_risk_alloc})
if medium_risk: if medium_risk:
# Allocate 40% to medium-risk ETFs # Allocate 40% to medium-risk ETFs
medium_risk_alloc = 40.0 / len(medium_risk) medium_risk_alloc = 40.0 / len(medium_risk)
for etf in medium_risk: for etf in medium_risk:
allocations[etf["Ticker"]] = medium_risk_alloc allocations.append({"ticker": etf["Ticker"], "allocation": medium_risk_alloc})
if high_risk: if high_risk:
# Allocate 30% to high-risk ETFs # Allocate 30% to high-risk ETFs
high_risk_alloc = 30.0 / len(high_risk) high_risk_alloc = 30.0 / len(high_risk)
for etf in high_risk: for etf in high_risk:
allocations[etf["Ticker"]] = high_risk_alloc allocations.append({"ticker": etf["Ticker"], "allocation": high_risk_alloc})
else: # Aggressive else: # Aggressive
# Aggressive allocation # Aggressive allocation
@ -396,36 +421,34 @@ def optimize_portfolio_allocation(
# Allocate 20% to low-risk ETFs # Allocate 20% to low-risk ETFs
low_risk_alloc = 20.0 / len(low_risk) low_risk_alloc = 20.0 / len(low_risk)
for etf in low_risk: for etf in low_risk:
allocations[etf["Ticker"]] = low_risk_alloc allocations.append({"ticker": etf["Ticker"], "allocation": low_risk_alloc})
if medium_risk: if medium_risk:
# Allocate 40% to medium-risk ETFs # Allocate 40% to medium-risk ETFs
medium_risk_alloc = 40.0 / len(medium_risk) medium_risk_alloc = 40.0 / len(medium_risk)
for etf in medium_risk: for etf in medium_risk:
allocations[etf["Ticker"]] = medium_risk_alloc allocations.append({"ticker": etf["Ticker"], "allocation": medium_risk_alloc})
if high_risk: if high_risk:
# Allocate 40% to high-risk ETFs # Allocate 40% to high-risk ETFs
high_risk_alloc = 40.0 / len(high_risk) high_risk_alloc = 40.0 / len(high_risk)
for etf in high_risk: for etf in high_risk:
allocations[etf["Ticker"]] = high_risk_alloc allocations.append({"ticker": etf["Ticker"], "allocation": high_risk_alloc})
# Adjust allocations based on correlation # If no allocations were made, use equal weighting
if not correlation_matrix.empty: if not allocations:
allocations = adjust_allocations_for_correlation(allocations, correlation_matrix) logger.warning("No risk-based allocations made, using equal weighting")
total_etfs = len(etf_metrics)
equal_alloc = 100.0 / total_etfs
allocations = [{"ticker": etf["Ticker"], "allocation": equal_alloc} for etf in etf_metrics]
# Normalize allocations to ensure they sum to 100% logger.info(f"Final allocations: {allocations}")
total_alloc = sum(allocations.values())
if total_alloc > 0:
allocations = {k: (v / total_alloc) * 100 for k, v in allocations.items()}
logger.info(f"Optimized allocations for {risk_tolerance} risk tolerance: {allocations}")
return allocations return allocations
except Exception as e: except Exception as e:
logger.error(f"Error optimizing portfolio allocation: {str(e)}") logger.error(f"Error optimizing portfolio allocation: {str(e)}")
logger.error(traceback.format_exc()) logger.error(traceback.format_exc())
return {} return []
def adjust_allocations_for_correlation( def adjust_allocations_for_correlation(
allocations: Dict[str, float], allocations: Dict[str, float],
@ -493,15 +516,18 @@ def fetch_etf_data_fmp(ticker: str) -> Optional[Dict[str, Any]]:
""" """
try: try:
if not FMP_API_KEY: if not FMP_API_KEY:
logger.warning("FMP API key not configured, skipping FMP data fetch") 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 return None
session = get_fmp_session() session = get_fmp_session()
# Get profile data for current price # Get profile data for current price
profile_url = f"{FMP_BASE_URL}/profile/{ticker}?apikey={FMP_API_KEY}" profile_url = f"{FMP_BASE_URL}/profile/{ticker}?apikey={FMP_API_KEY}"
logger.info(f"Fetching FMP profile data for {ticker}") logger.info(f"Making FMP API call to {profile_url}")
profile_response = session.get(profile_url) profile_response = session.get(profile_url)
st.session_state.api_calls += 1
logger.info(f"FMP API call count: {st.session_state.api_calls}")
if profile_response.status_code != 200: if profile_response.status_code != 200:
logger.error(f"FMP API error for {ticker}: {profile_response.status_code}") logger.error(f"FMP API error for {ticker}: {profile_response.status_code}")
@ -523,8 +549,10 @@ def fetch_etf_data_fmp(ticker: str) -> Optional[Dict[str, Any]]:
# Get dividend history # Get dividend history
dividend_url = f"{FMP_BASE_URL}/historical-price-full/stock_dividend/{ticker}?apikey={FMP_API_KEY}" dividend_url = f"{FMP_BASE_URL}/historical-price-full/stock_dividend/{ticker}?apikey={FMP_API_KEY}"
logger.info(f"Fetching FMP dividend data for {ticker}") logger.info(f"Making FMP API call to {dividend_url}")
dividend_response = session.get(dividend_url) dividend_response = session.get(dividend_url)
st.session_state.api_calls += 1
logger.info(f"FMP API call count: {st.session_state.api_calls}")
if dividend_response.status_code != 200: if dividend_response.status_code != 200:
logger.error(f"FMP API error for dividend data: {dividend_response.status_code}") logger.error(f"FMP API error for dividend data: {dividend_response.status_code}")
@ -660,22 +688,89 @@ def fetch_etf_data(tickers: List[str]) -> pd.DataFrame:
""" """
try: try:
data = {} 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()}")
for ticker in tickers: for ticker in tickers:
if not ticker: # Skip empty tickers if not ticker: # Skip empty tickers
continue continue
logger.info(f"Processing {ticker}") logger.info(f"\n=== Processing {ticker} ===")
# Try FMP first # Check cache first if not forcing refresh
etf_data = fetch_etf_data_fmp(ticker) 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"Making FMP API call for {ticker}")
etf_data = fetch_etf_data_fmp(ticker)
if etf_data is not None:
# 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
# If FMP fails, try yfinance # If FMP fails, try yfinance
if etf_data is None: logger.info(f"Falling back to yfinance for {ticker}")
logger.info(f"Falling back to yfinance for {ticker}") etf_data = fetch_etf_data_yfinance(ticker)
etf_data = fetch_etf_data_yfinance(ticker) if etf_data is not None:
# 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 # Only use HIGH_YIELD_ETFS data if both FMP and yfinance failed
if etf_data is None and ticker in HIGH_YIELD_ETFS: if ticker in HIGH_YIELD_ETFS:
logger.info(f"Using fallback data from HIGH_YIELD_ETFS for {ticker}") logger.info(f"Using fallback data from HIGH_YIELD_ETFS for {ticker}")
etf_data = { etf_data = {
"Ticker": ticker, "Ticker": ticker,
@ -684,10 +779,7 @@ def fetch_etf_data(tickers: List[str]) -> pd.DataFrame:
"Distribution Period": HIGH_YIELD_ETFS[ticker]["frequency"], "Distribution Period": HIGH_YIELD_ETFS[ticker]["frequency"],
"Risk Level": "High" "Risk Level": "High"
} }
if etf_data is not None:
data[ticker] = etf_data data[ticker] = etf_data
logger.info(f"Final data for {ticker}: {etf_data}")
else: else:
logger.error(f"Failed to fetch data for {ticker} from all sources") logger.error(f"Failed to fetch data for {ticker} from all sources")
@ -719,173 +811,109 @@ def fetch_etf_data(tickers: List[str]) -> pd.DataFrame:
return pd.DataFrame() return pd.DataFrame()
def run_portfolio_simulation( def run_portfolio_simulation(
mode: str, tickers: List[str],
target: float, weights: List[float],
risk_tolerance: str, initial_investment: float,
etf_inputs: List[Dict[str, str]], start_date: str,
enable_drip: bool, end_date: str,
enable_erosion: bool rebalance_frequency: str = 'monthly',
) -> Tuple[pd.DataFrame, pd.DataFrame]: use_fmp: bool = True
) -> Dict[str, Any]:
""" """
Run the portfolio simulation using the new optimization system. Run portfolio simulation with the given parameters.
Args: Args:
mode: Simulation mode ("income_target" or "capital_target") tickers: List of ETF tickers
target: Target value (monthly income or initial capital) weights: List of portfolio weights
risk_tolerance: Risk tolerance level initial_investment: Initial investment amount
etf_inputs: List of ETF inputs start_date: Start date for simulation
enable_drip: Whether to enable dividend reinvestment end_date: End date for simulation
enable_erosion: Whether to enable NAV & yield erosion rebalance_frequency: Frequency of rebalancing
use_fmp: Whether to use FMP API for data
Returns: Returns:
Tuple of (ETF data DataFrame, Final allocation DataFrame) Dictionary with simulation results
""" """
try: try:
logger.info(f"Starting portfolio simulation with mode: {mode}, target: {target}") # Validate inputs
logger.info(f"ETF inputs: {etf_inputs}") if not tickers or not weights:
raise ValueError("No tickers or weights provided")
# Fetch real ETF data if len(tickers) != len(weights):
tickers = [input["ticker"] for input in etf_inputs if input["ticker"]] # Filter out empty tickers raise ValueError("Number of tickers must match number of weights")
logger.info(f"Processing tickers: {tickers}") if not all(0 <= w <= 1 for w in weights):
raise ValueError("Weights must be between 0 and 1")
if not tickers: if sum(weights) != 1:
st.error("No valid tickers provided") raise ValueError("Weights must sum to 1")
return pd.DataFrame(), pd.DataFrame()
# Get historical data
# Fetch price and dividend data for all ETFs historical_data = {}
price_data_dict = {}
dividend_data_dict = {}
etf_metrics_list = []
for ticker in tickers: for ticker in tickers:
try: if use_fmp and FMP_API_KEY:
# Fetch price history data = fetch_etf_data_fmp(ticker)
price_url = f"{FMP_BASE_URL}/historical-price-full/{ticker}?apikey={FMP_API_KEY}" if data and 'historical' in data:
price_response = get_fmp_session().get(price_url) historical_data[ticker] = data['historical']
if price_response.status_code == 200:
price_data = pd.DataFrame(price_response.json().get("historical", []))
if not price_data.empty:
price_data_dict[ticker] = price_data
# Fetch dividend history
dividend_url = f"{FMP_BASE_URL}/historical-price-full/stock_dividend/{ticker}?apikey={FMP_API_KEY}"
dividend_response = get_fmp_session().get(dividend_url)
if dividend_response.status_code == 200:
dividend_data = pd.DataFrame(dividend_response.json().get("historical", []))
if not dividend_data.empty:
dividend_data_dict[ticker] = dividend_data
# Calculate metrics
if ticker in price_data_dict and ticker in dividend_data_dict:
metrics = calculate_etf_metrics(
ticker,
price_data_dict[ticker],
dividend_data_dict[ticker]
)
etf_metrics_list.append(metrics)
else: else:
logger.warning(f"Missing price or dividend data for {ticker}") 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']
except Exception as e: if not historical_data:
logger.error(f"Error processing {ticker}: {str(e)}") raise ValueError("No historical data available for any tickers")
continue
if not etf_metrics_list:
st.error("Failed to fetch ETF data")
return pd.DataFrame(), pd.DataFrame()
# Calculate correlation matrix
correlation_matrix = calculate_correlation_matrix(price_data_dict)
# Optimize portfolio allocation
allocations = optimize_portfolio_allocation(
etf_metrics_list,
risk_tolerance,
correlation_matrix
)
if not allocations:
st.error("Failed to optimize portfolio allocation")
return pd.DataFrame(), pd.DataFrame()
# Create final allocation DataFrame
final_alloc = pd.DataFrame(etf_metrics_list)
# Ensure all required columns exist
required_columns = [
"Ticker",
"Yield (%)",
"Price",
"Risk Level"
]
for col in required_columns:
if col not in final_alloc.columns:
logger.error(f"Missing required column: {col}")
st.error(f"Missing required column: {col}")
return pd.DataFrame(), pd.DataFrame()
# Add allocation column
final_alloc["Allocation (%)"] = final_alloc["Ticker"].map(allocations)
if mode == "income_target":
# Calculate required capital for income target
monthly_income = target
annual_income = monthly_income * 12
# Calculate weighted average yield # Create portfolio DataFrame
weighted_yield = (final_alloc["Allocation (%)"] * final_alloc["Yield (%)"]).sum() / 100 portfolio = pd.DataFrame()
logger.info(f"Calculated weighted yield: {weighted_yield:.2f}%") for ticker, data in historical_data.items():
portfolio[ticker] = data['close']
# Validate weighted yield # Calculate portfolio returns
if weighted_yield <= 0: portfolio_returns = portfolio.pct_change()
st.error(f"Invalid weighted yield calculated: {weighted_yield:.2f}%") portfolio_returns = portfolio_returns.fillna(0)
return pd.DataFrame(), pd.DataFrame()
# Calculate weighted returns
weighted_returns = pd.DataFrame()
for i, ticker in enumerate(tickers):
weighted_returns[ticker] = portfolio_returns[ticker] * weights[i]
# Calculate required capital based on weighted yield portfolio_returns['portfolio'] = weighted_returns.sum(axis=1)
required_capital = (annual_income / weighted_yield) * 100
logger.info(f"Calculated required capital: ${required_capital:,.2f}")
else:
required_capital = target
logger.info(f"Using provided capital: ${required_capital:,.2f}")
# Calculate capital allocation and income # Calculate cumulative returns
final_alloc["Capital Allocated ($)"] = (final_alloc["Allocation (%)"] / 100) * required_capital cumulative_returns = (1 + portfolio_returns).cumprod()
final_alloc["Shares"] = final_alloc["Capital Allocated ($)"] / final_alloc["Price"]
final_alloc["Income Contributed ($)"] = (final_alloc["Capital Allocated ($)"] * final_alloc["Yield (%)"]) / 100
logger.info(f"Final allocation calculated:\n{final_alloc}") # Calculate portfolio value
portfolio_value = initial_investment * cumulative_returns['portfolio']
# Apply erosion if enabled # Calculate metrics
if enable_erosion: total_return = (portfolio_value.iloc[-1] / initial_investment) - 1
# Apply a small erosion factor to yield and price annual_return = (1 + total_return) ** (252 / len(portfolio_value)) - 1
erosion_factor = 0.98 # 2% erosion per year volatility = portfolio_returns['portfolio'].std() * np.sqrt(252)
final_alloc["Yield (%)"] = final_alloc["Yield (%)"] * erosion_factor sharpe_ratio = annual_return / volatility if volatility != 0 else 0
final_alloc["Price"] = final_alloc["Price"] * erosion_factor
final_alloc["Income Contributed ($)"] = (final_alloc["Capital Allocated ($)"] * final_alloc["Yield (%)"]) / 100
logger.info("Applied erosion factor to yield and price")
# Validate final calculations # Calculate drawdown
total_capital = final_alloc["Capital Allocated ($)"].sum() rolling_max = portfolio_value.expanding().max()
total_income = final_alloc["Income Contributed ($)"].sum() drawdown = (portfolio_value - rolling_max) / rolling_max
effective_yield = (total_income / total_capital) * 100 max_drawdown = drawdown.min()
logger.info(f"Final validation - Total Capital: ${total_capital:,.2f}, Total Income: ${total_income:,.2f}, Effective Yield: {effective_yield:.2f}%") return {
'portfolio_value': portfolio_value,
if effective_yield <= 0: 'returns': portfolio_returns,
st.error(f"Invalid effective yield calculated: {effective_yield:.2f}%") 'cumulative_returns': cumulative_returns,
return pd.DataFrame(), pd.DataFrame() 'total_return': total_return,
'annual_return': annual_return,
# Create ETF data DataFrame for display 'volatility': volatility,
etf_data = pd.DataFrame(etf_metrics_list) 'sharpe_ratio': sharpe_ratio,
'max_drawdown': max_drawdown,
return etf_data, final_alloc 'drawdown': drawdown
}
except Exception as e: except Exception as e:
st.error(f"Error in portfolio simulation: {str(e)}") logger.error(f"Error in portfolio simulation: {str(e)}")
logger.error(f"Error in run_portfolio_simulation: {str(e)}") st.error(f"Error running portfolio simulation: {str(e)}")
logger.error(traceback.format_exc()) return None
return pd.DataFrame(), pd.DataFrame()
def portfolio_summary(final_alloc: pd.DataFrame) -> None: def portfolio_summary(final_alloc: pd.DataFrame) -> None:
""" """
@ -1090,25 +1118,54 @@ def allocate_for_income(df: pd.DataFrame, target: float, etf_allocations: List[D
# Create final allocation DataFrame # Create final allocation DataFrame
final_alloc = df.copy() 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 # Set allocations
for alloc in etf_allocations: for alloc in etf_allocations:
mask = final_alloc["Ticker"] == alloc["ticker"] mask = final_alloc["Ticker"] == alloc["ticker"]
final_alloc.loc[mask, "Allocation (%)"] = alloc["allocation"] 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 # Calculate required capital for income target
monthly_income = target monthly_income = target
annual_income = monthly_income * 12 annual_income = monthly_income * 12
avg_yield = final_alloc["Yield (%)"].mean()
required_capital = (annual_income / avg_yield) * 100 # 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
required_capital = (annual_income / weighted_yield) * 100
# Calculate capital allocation and income # Calculate capital allocation and income
final_alloc["Capital Allocated ($)"] = (final_alloc["Allocation (%)"] / 100) * required_capital final_alloc["Capital Allocated ($)"] = (final_alloc["Allocation (%)"] / 100) * required_capital
final_alloc["Shares"] = final_alloc["Capital Allocated ($)"] / final_alloc["Price"] final_alloc["Shares"] = final_alloc["Capital Allocated ($)"] / final_alloc["Price"]
final_alloc["Income Contributed ($)"] = (final_alloc["Capital Allocated ($)"] * final_alloc["Yield (%)"]) / 100 final_alloc["Income Contributed ($)"] = (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: ${required_capital:,.2f}")
logger.info(f"Final allocations:\n{final_alloc}")
return final_alloc return final_alloc
except Exception as e: except Exception as e:
st.error(f"Error in income allocation: {str(e)}") logger.error(f"Error in income allocation: {str(e)}")
logger.error(traceback.format_exc())
return None return None
def allocate_for_capital(df: pd.DataFrame, initial_capital: float, etf_allocations: List[Dict[str, Any]]) -> pd.DataFrame: def allocate_for_capital(df: pd.DataFrame, initial_capital: float, etf_allocations: List[Dict[str, Any]]) -> pd.DataFrame:
@ -1127,19 +1184,41 @@ def allocate_for_capital(df: pd.DataFrame, initial_capital: float, etf_allocatio
# Create final allocation DataFrame # Create final allocation DataFrame
final_alloc = df.copy() final_alloc = df.copy()
# Initialize allocation column if it doesn't exist
if "Allocation (%)" not in final_alloc.columns:
final_alloc["Allocation (%)"] = 0.0
# Set allocations # Set allocations
for alloc in etf_allocations: for alloc in etf_allocations:
mask = final_alloc["Ticker"] == alloc["ticker"] mask = final_alloc["Ticker"] == alloc["ticker"]
final_alloc.loc[mask, "Allocation (%)"] = alloc["allocation"] if mask.any():
final_alloc.loc[mask, "Allocation (%)"] = alloc["allocation"]
else:
logger.warning(f"Ticker {alloc['ticker']} not found in DataFrame")
# Verify allocations are set
if final_alloc["Allocation (%)"].sum() == 0:
logger.error("No allocations were set")
return None
# Calculate capital allocation and income # Calculate capital allocation and income
final_alloc["Capital Allocated ($)"] = (final_alloc["Allocation (%)"] / 100) * initial_capital final_alloc["Capital Allocated ($)"] = (final_alloc["Allocation (%)"] / 100) * initial_capital
final_alloc["Shares"] = final_alloc["Capital Allocated ($)"] / final_alloc["Price"] final_alloc["Shares"] = final_alloc["Capital Allocated ($)"] / final_alloc["Price"]
final_alloc["Income Contributed ($)"] = (final_alloc["Capital Allocated ($)"] * final_alloc["Yield (%)"]) / 100 final_alloc["Income Contributed ($)"] = (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 return final_alloc
except Exception as e: except Exception as e:
st.error(f"Error in capital allocation: {str(e)}") logger.error(f"Error in capital allocation: {str(e)}")
logger.error(traceback.format_exc())
return None return None
def reset_simulation(): def reset_simulation():
@ -1154,23 +1233,105 @@ def reset_simulation():
st.session_state.enable_erosion = False st.session_state.enable_erosion = False
st.rerun() st.rerun()
def test_fmp_connection(): def test_fmp_connection() -> bool:
"""Test the FMP API connection and display status.""" """Test connection to FMP API."""
try: try:
if not FMP_API_KEY: if not FMP_API_KEY:
return False, "No API key found" st.error("FMP API key not found in environment variables")
return False
session = get_fmp_session() session = get_fmp_session()
test_url = f"{FMP_BASE_URL}/profile/AAPL?apikey={FMP_API_KEY}" 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) 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: if response.status_code == 200:
data = response.json() st.success("Successfully connected to FMP API")
if data and isinstance(data, list) and len(data) > 0: return True
return True, "Connected" else:
return False, f"Error: {response.status_code}" 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: except Exception as e:
return False, f"Error: {str(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 # Set page config
st.set_page_config( st.set_page_config(
@ -1197,6 +1358,10 @@ if 'enable_drip' not in st.session_state:
st.session_state.enable_drip = False st.session_state.enable_drip = False
if 'enable_erosion' not in st.session_state: if 'enable_erosion' not in st.session_state:
st.session_state.enable_erosion = False st.session_state.enable_erosion = False
if 'api_calls' not in st.session_state:
st.session_state.api_calls = 0
if 'force_refresh_data' not in st.session_state:
st.session_state.force_refresh_data = False
# Main title # Main title
st.title("📈 ETF Portfolio Builder") st.title("📈 ETF Portfolio Builder")
@ -1296,27 +1461,47 @@ with st.sidebar:
st.session_state.initial_capital = initial_capital st.session_state.initial_capital = initial_capital
# Run simulation # Run simulation
df_data, final_alloc = run_portfolio_simulation( logger.info("Starting portfolio simulation...")
simulation_mode.lower().replace(" ", "_"), logger.info(f"ETF inputs: {etf_inputs}")
st.session_state.target,
risk_tolerance,
etf_inputs,
st.session_state.enable_drip,
st.session_state.enable_erosion
)
if df_data is not None and not df_data.empty and final_alloc is not None and not final_alloc.empty: df_data = fetch_etf_data([etf["ticker"] for etf in etf_inputs])
# Store results in session state logger.info(f"Fetched ETF data:\n{df_data}")
st.session_state.simulation_run = True
st.session_state.df_data = df_data if df_data is not None and not df_data.empty:
st.session_state.final_alloc = final_alloc logger.info("Calculating optimal allocations...")
st.success("Portfolio simulation completed!") # Calculate allocations based on risk tolerance
st.rerun() etf_allocations = optimize_portfolio_allocation(
df_data.to_dict('records'),
risk_tolerance,
pd.DataFrame() # Empty correlation matrix for now
)
logger.info(f"Optimal allocations: {etf_allocations}")
if simulation_mode == "Income Target":
logger.info(f"Allocating for income target: ${monthly_target}")
final_alloc = allocate_for_income(df_data, monthly_target, etf_allocations)
else:
logger.info(f"Allocating for capital target: ${initial_capital}")
final_alloc = allocate_for_capital(df_data, initial_capital, etf_allocations)
logger.info(f"Final allocation result:\n{final_alloc}")
if final_alloc is not None and not final_alloc.empty:
# Store results in session state
st.session_state.simulation_run = True
st.session_state.df_data = df_data
st.session_state.final_alloc = final_alloc
st.success("Portfolio simulation completed!")
st.rerun()
else:
st.error("Failed to generate portfolio allocation. Please check your inputs and try again.")
logger.error("Allocation returned empty DataFrame")
logger.error(f"df_data columns: {df_data.columns}")
logger.error(f"df_data shape: {df_data.shape}")
logger.error(f"df_data:\n{df_data}")
else: else:
st.error("Simulation failed to generate valid results. Please check your inputs and try again.") st.error("Failed to fetch ETF data. Please check your tickers and try again.")
logger.error("Simulation returned empty DataFrames") logger.error("ETF data fetch returned empty DataFrame")
logger.error(f"df_data: {df_data}")
logger.error(f"final_alloc: {final_alloc}")
except Exception as e: except Exception as e:
st.error(f"Error running simulation: {str(e)}") st.error(f"Error running simulation: {str(e)}")
@ -1330,11 +1515,57 @@ with st.sidebar:
# Add FMP connection status to the navigation bar # Add FMP connection status to the navigation bar
st.sidebar.markdown("---") st.sidebar.markdown("---")
st.sidebar.subheader("FMP API Status") st.sidebar.subheader("FMP API Status")
connection_status, message = test_fmp_connection() connection_status = test_fmp_connection()
if connection_status: if connection_status:
st.sidebar.success(f"✅ FMP API: {message}") st.sidebar.success("✅ FMP API: Connected")
else: else:
st.sidebar.error(f"❌ FMP API: {message}") 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"):
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") 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")
# Display results and interactive allocation adjustment UI after simulation is run # 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: if st.session_state.simulation_run and st.session_state.df_data is not None: