refactor: remove duplicate ETF display from sidebar for cleaner UI
This commit is contained in:
parent
e0dc6e57eb
commit
3929f2d4f0
@ -19,6 +19,7 @@ import sys
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import logging
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import traceback
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from dotenv import load_dotenv
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import re
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# Load environment variables
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load_dotenv(override=True) # Force reload of environment variables
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@ -340,11 +341,123 @@ def calculate_correlation_matrix(price_data_dict: Dict[str, pd.DataFrame]) -> pd
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logger.error(traceback.format_exc())
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return pd.DataFrame()
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def optimize_portfolio_allocation(
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etf_metrics: List[Dict[str, Any]],
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risk_tolerance: str,
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correlation_matrix: pd.DataFrame
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) -> List[Dict[str, Any]]:
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def calculate_etf_risk_score(etf: Dict[str, Any]) -> float:
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"""
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Calculate a comprehensive risk score for an ETF based on multiple metrics.
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Args:
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etf: Dictionary containing ETF metrics
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Returns:
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float: Risk score (0-100, higher means higher risk)
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"""
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try:
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score = 0
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metrics_used = 0
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# Primary Metrics (60% of total score)
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# 1. Volatility (20%)
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if 'volatility' in etf:
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volatility = etf['volatility']
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if volatility < 10:
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score += 20
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elif volatility < 15:
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score += 15
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elif volatility < 20:
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score += 10
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else:
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score += 5
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metrics_used += 1
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# 2. Yield (20%)
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if 'yield' in etf:
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yield_value = etf['yield']
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if yield_value < 3:
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score += 5
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elif yield_value < 6:
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score += 10
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elif yield_value < 10:
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score += 15
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else:
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score += 20
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metrics_used += 1
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# 3. Sharpe/Sortino Ratio (20%)
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if 'sharpe_ratio' in etf:
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sharpe = etf['sharpe_ratio']
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if sharpe > 1.5:
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score += 5
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elif sharpe > 1.0:
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score += 10
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elif sharpe > 0.8:
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score += 15
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else:
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score += 20
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metrics_used += 1
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# Secondary Metrics (40% of total score)
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# 1. Dividend Growth (10%)
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if 'dividend_growth' in etf:
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growth = etf['dividend_growth']
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if growth > 10:
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score += 5
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elif growth > 5:
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score += 7
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elif growth > 0:
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score += 10
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else:
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score += 15
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metrics_used += 1
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# 2. Payout Ratio (10%)
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if 'payout_ratio' in etf:
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ratio = etf['payout_ratio']
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if ratio < 40:
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score += 5
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elif ratio < 60:
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score += 7
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elif ratio < 80:
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score += 10
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else:
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score += 15
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metrics_used += 1
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# 3. Expense Ratio (10%)
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if 'expense_ratio' in etf:
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ratio = etf['expense_ratio']
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if ratio < 0.2:
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score += 5
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elif ratio < 0.4:
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score += 7
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elif ratio < 0.6:
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score += 10
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else:
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score += 15
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metrics_used += 1
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# 4. AUM/Volume (10%)
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if 'aum' in etf:
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aum = etf['aum']
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if aum > 5e9: # > $5B
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score += 5
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elif aum > 1e9: # > $1B
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score += 7
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elif aum > 500e6: # > $500M
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score += 10
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else:
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score += 15
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metrics_used += 1
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# Normalize score based on available metrics
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if metrics_used > 0:
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return score / metrics_used
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return 50 # Default middle score if no metrics available
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except Exception as e:
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logger.error(f"Error calculating ETF risk score: {str(e)}")
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return 50
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def optimize_portfolio_allocation(etf_metrics: List[Dict[str, Any]], risk_tolerance: str, correlation_matrix: pd.DataFrame) -> List[Dict[str, Any]]:
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"""
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Optimize portfolio allocation based on risk tolerance and ETF metrics.
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@ -360,93 +473,61 @@ def optimize_portfolio_allocation(
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logger.info(f"Optimizing portfolio allocation for {risk_tolerance} risk tolerance")
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logger.info(f"ETF metrics: {etf_metrics}")
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# Group ETFs by risk category
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low_risk = [etf for etf in etf_metrics if etf.get("Risk Level", "Unknown") == "Low"]
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medium_risk = [etf for etf in etf_metrics if etf.get("Risk Level", "Unknown") == "Medium"]
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high_risk = [etf for etf in etf_metrics if etf.get("Risk Level", "Unknown") == "High"]
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# Calculate risk scores for each ETF
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for etf in etf_metrics:
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etf['risk_score'] = calculate_etf_risk_score(etf)
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logger.info(f"Risk score for {etf['Ticker']}: {etf['risk_score']:.2f}")
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logger.info(f"Risk groups - Low: {len(low_risk)}, Medium: {len(medium_risk)}, High: {len(high_risk)}")
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# Sort ETFs by risk score based on risk tolerance
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if risk_tolerance == "Conservative":
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# For conservative, prefer lower risk
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sorted_etfs = sorted(etf_metrics, key=lambda x: x['risk_score'])
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elif risk_tolerance == "Aggressive":
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# For aggressive, prefer higher risk
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sorted_etfs = sorted(etf_metrics, key=lambda x: x['risk_score'], reverse=True)
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else: # Moderate
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# For moderate, sort by Sharpe ratio first, then risk score
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sorted_etfs = sorted(etf_metrics,
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key=lambda x: (x.get('sharpe_ratio', 0), -x['risk_score']),
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reverse=True)
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# Sort ETFs by score within each risk category
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low_risk.sort(key=lambda x: x.get("score", 0), reverse=True)
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medium_risk.sort(key=lambda x: x.get("score", 0), reverse=True)
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high_risk.sort(key=lambda x: x.get("score", 0), reverse=True)
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logger.info(f"Sorted ETFs: {[etf['Ticker'] for etf in sorted_etfs]}")
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# Initialize allocations
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allocations = []
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# Calculate base allocations based on risk tolerance
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num_etfs = len(sorted_etfs)
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if num_etfs == 0:
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return []
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if risk_tolerance == "Conservative":
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# Conservative allocation
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if low_risk:
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# Allocate 50% to low-risk ETFs
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low_risk_alloc = 50.0 / len(low_risk)
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for etf in low_risk:
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allocations.append({"ticker": etf["Ticker"], "allocation": low_risk_alloc})
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if medium_risk:
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# Allocate 30% to medium-risk ETFs
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medium_risk_alloc = 30.0 / len(medium_risk)
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for etf in medium_risk:
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allocations.append({"ticker": etf["Ticker"], "allocation": medium_risk_alloc})
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if high_risk:
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# Allocate 20% to high-risk ETFs
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high_risk_alloc = 20.0 / len(high_risk)
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for etf in high_risk:
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allocations.append({"ticker": etf["Ticker"], "allocation": high_risk_alloc})
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# 50% to low risk, 30% to medium risk, 20% to high risk
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base_allocations = [0.5] + [0.3] + [0.2] + [0.0] * (num_etfs - 3)
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elif risk_tolerance == "Moderate":
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# Moderate allocation
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if low_risk:
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# Allocate 30% to low-risk ETFs
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low_risk_alloc = 30.0 / len(low_risk)
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for etf in low_risk:
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allocations.append({"ticker": etf["Ticker"], "allocation": low_risk_alloc})
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if medium_risk:
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# Allocate 40% to medium-risk ETFs
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medium_risk_alloc = 40.0 / len(medium_risk)
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for etf in medium_risk:
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allocations.append({"ticker": etf["Ticker"], "allocation": medium_risk_alloc})
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if high_risk:
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# Allocate 30% to high-risk ETFs
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high_risk_alloc = 30.0 / len(high_risk)
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for etf in high_risk:
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allocations.append({"ticker": etf["Ticker"], "allocation": high_risk_alloc})
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# 40% to medium risk, 30% to low risk, 30% to high risk
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base_allocations = [0.4] + [0.3] + [0.3] + [0.0] * (num_etfs - 3)
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else: # Aggressive
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# Aggressive allocation
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if low_risk:
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# Allocate 20% to low-risk ETFs
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low_risk_alloc = 20.0 / len(low_risk)
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for etf in low_risk:
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allocations.append({"ticker": etf["Ticker"], "allocation": low_risk_alloc})
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if medium_risk:
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# Allocate 40% to medium-risk ETFs
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medium_risk_alloc = 40.0 / len(medium_risk)
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for etf in medium_risk:
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allocations.append({"ticker": etf["Ticker"], "allocation": medium_risk_alloc})
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if high_risk:
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# Allocate 40% to high-risk ETFs
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high_risk_alloc = 40.0 / len(high_risk)
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for etf in high_risk:
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allocations.append({"ticker": etf["Ticker"], "allocation": high_risk_alloc})
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# 40% to high risk, 40% to medium risk, 20% to low risk
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base_allocations = [0.4] + [0.4] + [0.2] + [0.0] * (num_etfs - 3)
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# If no allocations were made, use equal weighting
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if not allocations:
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logger.warning("No risk-based allocations made, using equal weighting")
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total_etfs = len(etf_metrics)
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equal_alloc = 100.0 / total_etfs
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allocations = [{"ticker": etf["Ticker"], "allocation": equal_alloc} for etf in etf_metrics]
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# Adjust allocations based on number of ETFs
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if num_etfs < len(base_allocations):
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base_allocations = base_allocations[:num_etfs]
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# Normalize to ensure sum is 1
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total = sum(base_allocations)
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base_allocations = [alloc/total for alloc in base_allocations]
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logger.info(f"Final allocations: {allocations}")
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return allocations
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# Create final allocation list
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final_allocations = []
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for etf, allocation in zip(sorted_etfs, base_allocations):
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final_allocations.append({
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"ticker": etf["Ticker"],
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"allocation": allocation * 100 # Convert to percentage
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})
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logger.info(f"Final allocations: {final_allocations}")
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return final_allocations
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except Exception as e:
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logger.error(f"Error optimizing portfolio allocation: {str(e)}")
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logger.error(f"Error in optimize_portfolio_allocation: {str(e)}")
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logger.error(traceback.format_exc())
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return []
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@ -1344,37 +1425,263 @@ st.set_page_config(
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# Initialize session state variables
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if 'simulation_run' not in st.session_state:
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st.session_state.simulation_run = False
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logger.info("Initialized simulation_run in session state")
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if 'df_data' not in st.session_state:
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st.session_state.df_data = None
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logger.info("Initialized df_data in session state")
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if 'final_alloc' not in st.session_state:
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st.session_state.final_alloc = None
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logger.info("Initialized final_alloc in session state")
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if 'mode' not in st.session_state:
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st.session_state.mode = 'Capital Target'
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logger.info("Initialized mode in session state")
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if 'target' not in st.session_state:
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st.session_state.target = 0
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logger.info("Initialized target in session state")
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if 'initial_capital' not in st.session_state:
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st.session_state.initial_capital = 0
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logger.info("Initialized initial_capital in session state")
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if 'enable_drip' not in st.session_state:
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st.session_state.enable_drip = False
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logger.info("Initialized enable_drip in session state")
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if 'enable_erosion' not in st.session_state:
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st.session_state.enable_erosion = False
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logger.info("Initialized enable_erosion in session state")
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if 'api_calls' not in st.session_state:
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st.session_state.api_calls = 0
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logger.info("Initialized api_calls in session state")
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if 'force_refresh_data' not in st.session_state:
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st.session_state.force_refresh_data = False
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logger.info("Initialized force_refresh_data in session state")
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if 'etf_allocations' not in st.session_state:
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st.session_state.etf_allocations = []
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logger.info("Initialized empty etf_allocations in session state")
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if 'risk_tolerance' not in st.session_state:
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st.session_state.risk_tolerance = "Moderate"
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logger.info("Initialized risk_tolerance in session state")
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# Main title
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st.title("📈 ETF Portfolio Builder")
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# Sidebar for simulation parameters
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with st.sidebar:
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st.header("Simulation Parameters")
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# Function to remove ticker
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def remove_ticker(ticker_to_remove: str) -> None:
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"""Remove a ticker from the portfolio."""
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try:
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logger.info(f"Removing ticker: {ticker_to_remove}")
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current_allocations = list(st.session_state.etf_allocations)
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st.session_state.etf_allocations = [etf for etf in current_allocations if etf["ticker"] != ticker_to_remove]
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logger.info(f"Updated allocations after removal: {st.session_state.etf_allocations}")
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st.rerun()
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except Exception as e:
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logger.error(f"Error removing ticker: {str(e)}")
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st.error(f"Error removing ticker: {str(e)}")
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# Display current tickers in the main space
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if st.session_state.etf_allocations:
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st.subheader("Selected ETFs")
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st.markdown("""
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<style>
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.ticker-container {
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display: flex;
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flex-wrap: wrap;
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gap: 8px;
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margin-bottom: 20px;
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}
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.ticker-item {
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display: inline-flex;
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align-items: center;
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color: #2ecc71;
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font-size: 1.1em;
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font-weight: 500;
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}
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.ticker-close {
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margin-left: 2px;
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cursor: pointer;
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font-size: 1.1em;
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opacity: 0.7;
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}
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.ticker-close:hover {
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opacity: 1;
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}
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</style>
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""", unsafe_allow_html=True)
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# Add refresh data button at the top
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if st.button("🔄 Refresh Data", use_container_width=True):
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st.info("Refreshing ETF data...")
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# Add your data refresh logic here
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st.success("Data refreshed successfully!")
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# Create a container for tickers
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ticker_container = st.container()
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with ticker_container:
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# Display each ticker with a close button
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for etf in st.session_state.etf_allocations:
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col1, col2 = st.columns([0.05, 0.95]) # Adjusted column ratio
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with col1:
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if st.button("×", key=f"remove_{etf['ticker']}",
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help=f"Remove {etf['ticker']} from portfolio"):
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remove_ticker(etf['ticker'])
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with col2:
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st.markdown(f"<div class='ticker-item'>{etf['ticker']}</div>", unsafe_allow_html=True)
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# Debug information
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logger.info("=== Session State Debug ===")
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logger.info(f"Full session state: {dict(st.session_state)}")
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logger.info(f"ETF allocations type: {type(st.session_state.etf_allocations)}")
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logger.info(f"ETF allocations content: {st.session_state.etf_allocations}")
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logger.info("=== End Session State Debug ===")
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def add_etf_to_portfolio(ticker: str) -> bool:
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"""Add an ETF to the portfolio with proper validation and error handling."""
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try:
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logger.info("=== Adding ETF to Portfolio ===")
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logger.info(f"Input ticker: {ticker}")
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logger.info(f"Current allocations before adding: {st.session_state.etf_allocations}")
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logger.info(f"Current allocations type: {type(st.session_state.etf_allocations)}")
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# Validate ticker format
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if not re.match(r'^[A-Z]{1,7}$', ticker.upper()):
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logger.warning(f"Invalid ticker format: {ticker}")
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st.error("Invalid ticker format. Must be 1-7 uppercase letters.")
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return False
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# Check if ticker already exists
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if any(etf["ticker"] == ticker.upper() for etf in st.session_state.etf_allocations):
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logger.warning(f"Ticker {ticker.upper()} already exists in portfolio")
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st.warning(f"{ticker.upper()} is already in your portfolio.")
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return False
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# Verify ticker exists by fetching data
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logger.info(f"Fetching data for ticker: {ticker.upper()}")
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etf_data = fetch_etf_data([ticker.upper()])
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logger.info(f"Fetched ETF data: {etf_data}")
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if etf_data is None or etf_data.empty:
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logger.warning(f"Unknown ticker: {ticker.upper()}")
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st.error(f"Unknown ticker: {ticker.upper()}. Please enter a valid ETF ticker.")
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return False
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# Create new ETF entry
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new_etf = {
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"ticker": ticker.upper(),
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"allocation": 0.0
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}
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logger.info(f"Created new ETF entry: {new_etf}")
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# Update session state
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current_allocations = list(st.session_state.etf_allocations)
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current_allocations.append(new_etf)
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st.session_state.etf_allocations = current_allocations
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logger.info(f"Updated session state allocations: {st.session_state.etf_allocations}")
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logger.info(f"Updated allocations type: {type(st.session_state.etf_allocations)}")
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# Recalculate allocations based on risk tolerance
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if len(st.session_state.etf_allocations) > 0:
|
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risk_tolerance = st.session_state.risk_tolerance
|
||||
tickers = [etf["ticker"] for etf in st.session_state.etf_allocations]
|
||||
logger.info(f"Recalculating allocations for tickers: {tickers}")
|
||||
|
||||
df_data = fetch_etf_data(tickers)
|
||||
logger.info(f"Fetched data for recalculation: {df_data}")
|
||||
|
||||
if df_data is not None and not df_data.empty:
|
||||
etf_metrics = df_data.to_dict('records')
|
||||
new_allocations = optimize_portfolio_allocation(
|
||||
etf_metrics,
|
||||
risk_tolerance,
|
||||
pd.DataFrame()
|
||||
)
|
||||
logger.info(f"Calculated new allocations: {new_allocations}")
|
||||
st.session_state.etf_allocations = new_allocations
|
||||
logger.info(f"Updated session state with new allocations: {st.session_state.etf_allocations}")
|
||||
|
||||
logger.info("=== End Adding ETF to Portfolio ===")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error("=== Error Adding ETF to Portfolio ===")
|
||||
logger.error(f"Error: {str(e)}")
|
||||
logger.error(traceback.format_exc())
|
||||
st.error(f"Error adding ETF: {str(e)}")
|
||||
return False
|
||||
|
||||
def remove_etf_from_portfolio(index: int) -> bool:
|
||||
"""Remove an ETF from the portfolio with proper validation and error handling."""
|
||||
try:
|
||||
logger.info(f"Attempting to remove ETF at index: {index}")
|
||||
logger.info(f"Current allocations before removal: {st.session_state.etf_allocations}")
|
||||
|
||||
if not st.session_state.etf_allocations or index >= len(st.session_state.etf_allocations):
|
||||
logger.warning(f"Invalid ETF index for removal: {index}")
|
||||
return False
|
||||
|
||||
# Create new list without the removed ETF
|
||||
current_allocations = list(st.session_state.etf_allocations)
|
||||
removed_etf = current_allocations.pop(index)
|
||||
st.session_state.etf_allocations = current_allocations
|
||||
|
||||
logger.info(f"Successfully removed ETF: {removed_etf}")
|
||||
logger.info(f"Updated allocations: {st.session_state.etf_allocations}")
|
||||
|
||||
# Recalculate allocations if there are remaining ETFs
|
||||
if st.session_state.etf_allocations:
|
||||
risk_tolerance = st.session_state.risk_tolerance
|
||||
tickers = [etf["ticker"] for etf in st.session_state.etf_allocations]
|
||||
df_data = fetch_etf_data(tickers)
|
||||
|
||||
if df_data is not None and not df_data.empty:
|
||||
etf_metrics = df_data.to_dict('records')
|
||||
new_allocations = optimize_portfolio_allocation(
|
||||
etf_metrics,
|
||||
risk_tolerance,
|
||||
pd.DataFrame()
|
||||
)
|
||||
st.session_state.etf_allocations = new_allocations
|
||||
logger.info(f"Recalculated allocations: {new_allocations}")
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error removing ETF: {str(e)}")
|
||||
logger.error(traceback.format_exc())
|
||||
st.error(f"Error removing ETF: {str(e)}")
|
||||
return False
|
||||
|
||||
# Sidebar for ETF input
|
||||
with st.sidebar:
|
||||
st.header("ETF Allocation")
|
||||
|
||||
# Create a container for ETF input
|
||||
with st.container():
|
||||
# Input field for ETF ticker only
|
||||
new_ticker = st.text_input("ETF Ticker", help="Enter a valid ETF ticker (e.g., SCHD)")
|
||||
|
||||
# Add button to add ETF
|
||||
add_etf_button = st.button("ADD ETF", use_container_width=True)
|
||||
if add_etf_button:
|
||||
logger.info("=== Add ETF Button Clicked ===")
|
||||
logger.info(f"Input ticker: {new_ticker}")
|
||||
logger.info(f"Current allocations: {st.session_state.etf_allocations}")
|
||||
|
||||
if not new_ticker:
|
||||
st.error("Please enter an ETF ticker.")
|
||||
logger.warning("No ticker provided")
|
||||
elif len(st.session_state.etf_allocations) >= 10:
|
||||
st.error("Maximum of 10 ETFs allowed in portfolio.")
|
||||
logger.warning("Maximum ETF limit reached")
|
||||
else:
|
||||
if add_etf_to_portfolio(new_ticker):
|
||||
st.success(f"Added {new_ticker.upper()} to portfolio.")
|
||||
logger.info("Successfully added ETF, triggering rerun")
|
||||
st.rerun()
|
||||
|
||||
# Display total allocation
|
||||
if st.session_state.etf_allocations:
|
||||
current_total = sum(etf["allocation"] for etf in st.session_state.etf_allocations)
|
||||
st.metric("Total Allocation (%)", f"{current_total:.2f}")
|
||||
|
||||
# Add a warning if total is not 100%
|
||||
if abs(current_total - 100) > 0.1:
|
||||
st.warning("Total allocation should be 100%")
|
||||
else:
|
||||
st.info("No ETFs added yet. Please add ETFs to your portfolio.")
|
||||
logger.info("No ETFs in portfolio")
|
||||
|
||||
# Mode selection
|
||||
simulation_mode = st.radio(
|
||||
@ -1406,6 +1713,7 @@ with st.sidebar:
|
||||
options=["Conservative", "Moderate", "Aggressive"],
|
||||
value="Moderate"
|
||||
)
|
||||
st.session_state.risk_tolerance = risk_tolerance
|
||||
|
||||
# Additional options
|
||||
st.subheader("Additional Options")
|
||||
@ -1424,88 +1732,55 @@ with st.sidebar:
|
||||
index=1
|
||||
)
|
||||
|
||||
# ETF Selection
|
||||
st.subheader("ETF Selection")
|
||||
|
||||
# Create a form for ETF selection
|
||||
with st.form("etf_selection_form"):
|
||||
# Number of ETFs
|
||||
num_etfs = st.number_input("Number of ETFs", min_value=1, max_value=10, value=3, step=1)
|
||||
|
||||
# Create columns for ETF inputs
|
||||
etf_inputs = []
|
||||
for i in range(num_etfs):
|
||||
ticker = st.text_input(f"ETF {i+1} Ticker", key=f"ticker_{i}")
|
||||
if ticker: # Only add non-empty tickers
|
||||
etf_inputs.append({"ticker": ticker.upper().strip()})
|
||||
|
||||
# Submit button
|
||||
submitted = st.form_submit_button("Run Portfolio Simulation", type="primary")
|
||||
|
||||
if submitted:
|
||||
# Run simulation button
|
||||
if st.button("Run Portfolio Simulation", type="primary", use_container_width=True):
|
||||
if not st.session_state.etf_allocations:
|
||||
st.error("Please add at least one ETF to your portfolio.")
|
||||
else:
|
||||
try:
|
||||
if not etf_inputs:
|
||||
st.error("Please enter at least one ETF ticker")
|
||||
# Store parameters in session state
|
||||
st.session_state.mode = simulation_mode
|
||||
st.session_state.enable_drip = enable_drip == "Yes"
|
||||
st.session_state.enable_erosion = enable_erosion == "Yes"
|
||||
|
||||
if simulation_mode == "Income Target":
|
||||
st.session_state.target = monthly_target
|
||||
else:
|
||||
logger.info(f"Form submitted with {len(etf_inputs)} ETFs: {etf_inputs}")
|
||||
|
||||
# Store parameters in session state
|
||||
st.session_state.mode = simulation_mode
|
||||
st.session_state.enable_drip = enable_drip == "Yes"
|
||||
st.session_state.enable_erosion = enable_erosion == "Yes"
|
||||
|
||||
st.session_state.target = initial_capital
|
||||
st.session_state.initial_capital = initial_capital
|
||||
|
||||
# Run simulation
|
||||
logger.info("Starting portfolio simulation...")
|
||||
logger.info(f"ETF allocations: {st.session_state.etf_allocations}")
|
||||
|
||||
tickers = [etf["ticker"] for etf in st.session_state.etf_allocations]
|
||||
df_data = fetch_etf_data(tickers)
|
||||
logger.info(f"Fetched ETF data:\n{df_data}")
|
||||
|
||||
if df_data is not None and not df_data.empty:
|
||||
if simulation_mode == "Income Target":
|
||||
st.session_state.target = monthly_target
|
||||
logger.info(f"Allocating for income target: ${monthly_target}")
|
||||
final_alloc = allocate_for_income(df_data, monthly_target, st.session_state.etf_allocations)
|
||||
else:
|
||||
st.session_state.target = initial_capital
|
||||
st.session_state.initial_capital = initial_capital
|
||||
logger.info(f"Allocating for capital target: ${initial_capital}")
|
||||
final_alloc = allocate_for_capital(df_data, initial_capital, st.session_state.etf_allocations)
|
||||
|
||||
# Run simulation
|
||||
logger.info("Starting portfolio simulation...")
|
||||
logger.info(f"ETF inputs: {etf_inputs}")
|
||||
logger.info(f"Final allocation result:\n{final_alloc}")
|
||||
|
||||
df_data = fetch_etf_data([etf["ticker"] for etf in etf_inputs])
|
||||
logger.info(f"Fetched ETF data:\n{df_data}")
|
||||
|
||||
if df_data is not None and not df_data.empty:
|
||||
logger.info("Calculating optimal allocations...")
|
||||
# Calculate allocations based on risk tolerance
|
||||
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}")
|
||||
if final_alloc is not None and not final_alloc.empty:
|
||||
st.session_state.simulation_run = True
|
||||
st.session_state.df_data = df_data
|
||||
st.session_state.final_alloc = final_alloc
|
||||
st.success("Portfolio simulation completed!")
|
||||
st.rerun()
|
||||
else:
|
||||
st.error("Failed to fetch ETF data. Please check your tickers and try again.")
|
||||
logger.error("ETF data fetch returned empty DataFrame")
|
||||
st.error("Failed to generate portfolio allocation. Please check your inputs and try again.")
|
||||
else:
|
||||
st.error("Failed to fetch ETF data. Please check your tickers and try again.")
|
||||
|
||||
except Exception as e:
|
||||
st.error(f"Error running simulation: {str(e)}")
|
||||
logger.error(f"Error in form submission: {str(e)}")
|
||||
logger.error(f"Error in simulation: {str(e)}")
|
||||
logger.error(traceback.format_exc())
|
||||
|
||||
# Add reset simulation button at the bottom of sidebar
|
||||
@ -1671,7 +1946,8 @@ if st.session_state.simulation_run and st.session_state.df_data is not None:
|
||||
min_value=0.0,
|
||||
max_value=100.0,
|
||||
step=0.1,
|
||||
format="%.1f"
|
||||
format="%.1f",
|
||||
required=True
|
||||
),
|
||||
"Yield (%)": st.column_config.TextColumn("Yield (%)", disabled=True),
|
||||
"Price Per Share": st.column_config.TextColumn("Price Per Share", disabled=True),
|
||||
@ -1684,6 +1960,16 @@ if st.session_state.simulation_run and st.session_state.df_data is not None:
|
||||
# Calculate total allocation
|
||||
total_alloc = edited_df["Allocation (%)"].sum()
|
||||
|
||||
# Validate individual allocations
|
||||
invalid_allocations = edited_df[
|
||||
(edited_df["Allocation (%)"] <= 0) |
|
||||
(edited_df["Allocation (%)"] > 100)
|
||||
]
|
||||
|
||||
if not invalid_allocations.empty:
|
||||
for _, row in invalid_allocations.iterrows():
|
||||
st.error(f"Invalid allocation for {row['Ticker']}: must be between 0% and 100%")
|
||||
|
||||
# Display total allocation with color coding
|
||||
if abs(total_alloc - 100) <= 0.1:
|
||||
st.metric("Total Allocation (%)", f"{total_alloc:.2f}", delta=None)
|
||||
@ -1691,9 +1977,7 @@ if st.session_state.simulation_run and st.session_state.df_data is not None:
|
||||
st.metric("Total Allocation (%)", f"{total_alloc:.2f}",
|
||||
delta=f"{total_alloc - 100:.2f}",
|
||||
delta_color="off")
|
||||
|
||||
if abs(total_alloc - 100) > 0.1:
|
||||
st.warning("Total allocation should be 100%")
|
||||
st.error("Total allocation must be exactly 100%")
|
||||
|
||||
# Create columns for quick actions
|
||||
col1, col2, col3 = st.columns(3)
|
||||
@ -1709,7 +1993,7 @@ if st.session_state.simulation_run and st.session_state.df_data is not None:
|
||||
|
||||
# Submit button for manual edits
|
||||
submitted = st.form_submit_button("Update Allocations",
|
||||
disabled=abs(total_alloc - 100) > 0.1,
|
||||
disabled=abs(total_alloc - 100) > 0.1 or not invalid_allocations.empty,
|
||||
type="primary",
|
||||
use_container_width=True)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user