refactor: optimize portfolio allocation for risk tolerance
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pages/ETF_Portal_Builder.py
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60
pages/ETF_Portal_Builder.py
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# Main title
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st.title("📈 ETF Portfolio Builder")
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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.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: 10px;
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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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background-color: #2ecc71;
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color: white;
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padding: 5px 10px;
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border-radius: 15px;
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font-size: 1em;
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font-weight: 500;
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}
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.ticker-close {
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margin-left: 8px;
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cursor: pointer;
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font-size: 0.9em;
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opacity: 0.8;
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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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# Create columns for the tickers
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cols = st.columns([1] * len(st.session_state.etf_allocations))
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# Display each ticker with a close button
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for i, etf in enumerate(st.session_state.etf_allocations):
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with cols[i]:
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if st.button(f"× {etf['ticker']}", 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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# Debug information
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logger.info("=== Session State Debug ===")
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@ -478,20 +478,9 @@ def optimize_portfolio_allocation(etf_metrics: List[Dict[str, Any]], risk_tolera
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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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# 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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logger.info(f"Sorted ETFs: {[etf['Ticker'] for etf in sorted_etfs]}")
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# Sort ETFs by yield (higher yield = higher risk)
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sorted_etfs = sorted(etf_metrics, key=lambda x: x.get('Yield (%)', 0), reverse=True)
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logger.info(f"Sorted ETFs by yield: {[etf['Ticker'] for etf in sorted_etfs]}")
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# Calculate base allocations based on risk tolerance
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num_etfs = len(sorted_etfs)
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@ -499,14 +488,16 @@ def optimize_portfolio_allocation(etf_metrics: List[Dict[str, Any]], risk_tolera
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return []
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if risk_tolerance == "Conservative":
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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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# For conservative, allocate more to lower yielding ETFs
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# This will require more capital to achieve the same income
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base_allocations = [0.4] + [0.3] + [0.2] + [0.1] + [0.0] * (num_etfs - 4)
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elif risk_tolerance == "Moderate":
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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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# For moderate, balance between yield and risk
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base_allocations = [0.3] + [0.3] + [0.2] + [0.2] + [0.0] * (num_etfs - 4)
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else: # Aggressive
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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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# For aggressive, allocate more to higher yielding ETFs
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# This will require less capital to achieve the same income
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base_allocations = [0.4] + [0.3] + [0.2] + [0.1] + [0.0] * (num_etfs - 4)
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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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@ -1711,10 +1702,53 @@ with st.sidebar:
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risk_tolerance = st.select_slider(
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"Risk Tolerance",
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options=["Conservative", "Moderate", "Aggressive"],
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value="Moderate"
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value=st.session_state.get("risk_tolerance", "Moderate"),
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key="risk_tolerance_slider"
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)
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# Check if risk tolerance changed
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if risk_tolerance != st.session_state.get("risk_tolerance"):
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logger.info("=== Risk Tolerance Change Detection ===")
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logger.info(f"Current risk tolerance in session state: {st.session_state.get('risk_tolerance')}")
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logger.info(f"New risk tolerance from slider: {risk_tolerance}")
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# Update session state
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st.session_state.risk_tolerance = risk_tolerance
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# Recalculate allocations if we have ETFs
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if st.session_state.etf_allocations:
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logger.info("Recalculating allocations due to risk tolerance change")
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tickers = [etf["ticker"] for etf in st.session_state.etf_allocations]
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df_data = fetch_etf_data(tickers)
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if df_data is not None and not df_data.empty:
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etf_metrics = df_data.to_dict('records')
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new_allocations = optimize_portfolio_allocation(
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etf_metrics,
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risk_tolerance,
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pd.DataFrame()
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)
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st.session_state.etf_allocations = new_allocations
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logger.info(f"New allocations after risk tolerance change: {new_allocations}")
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# If simulation has been run, update final allocation
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if st.session_state.simulation_run and st.session_state.df_data is not None:
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if st.session_state.mode == "Income Target":
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final_alloc = allocate_for_income(
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st.session_state.df_data,
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st.session_state.target,
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new_allocations
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)
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else:
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final_alloc = allocate_for_capital(
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st.session_state.df_data,
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st.session_state.initial_capital,
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new_allocations
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)
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if final_alloc is not None:
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st.session_state.final_alloc = final_alloc
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st.rerun()
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# Additional options
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st.subheader("Additional Options")
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