菜单
Select appropriate asset universes for portfolio construction based on investor profile, risk tolerance, and investment goals. Use when determining which assets to include in a portfolio.
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基于 SOC 职业分类
Backtest portfolio performance using historical data. Use when validating portfolio strategy with historical returns, calculating realized metrics, and stress testing.
Execute portfolio optimization using Mean-Variance Optimization (MVO) or Hierarchical Risk Parity (HRP). Use when constructing optimal portfolio weights from a set of assets.
Compare different portfolio optimization approaches and present trade-offs. Use when the investor wants to see multiple options or understand alternatives.
Assess portfolio risk using volatility, drawdown, and other risk metrics. Use when evaluating the risk characteristics of a portfolio or comparing risk profiles.
| name | universe-selection |
| description | Select appropriate asset universes for portfolio construction based on investor profile, risk tolerance, and investment goals. Use when determining which assets to include in a portfolio. |
| Investor Type | Universe | Code |
|---|---|---|
| Conservative (low risk, near retirement, preservation) | conservative | get_universe('conservative') |
| Balanced (moderate risk, long horizon, diversified) | global_diversified | get_universe('global_diversified') |
| Aggressive (high risk, growth focus) | us_tech | get_universe('us_tech') |
| Time Horizon | Risk Tolerance | Recommended Universe |
|---|---|---|
| < 5 years | Low | conservative |
| < 5 years | Medium | conservative |
| 5-15 years | Low | conservative |
| 5-15 years | Medium | global_diversified |
| 5-15 years | High | us_tech |
| > 15 years | Low | global_diversified |
| > 15 years | Medium | global_diversified |
| > 15 years | High | us_tech |
| Universe | Assets | Risk Level |
|---|---|---|
conservative | BND, AGG, TLT, IEF, GOVT, LQD, MBB, VMBS | Low |
global_diversified | SPY, EFA, EEM, VWO, TLT, GLD, VNQ, LQD, HYG, DBC, IEF, GOVT, AGG, BND, VTI | Medium |
us_tech | AAPL, MSFT, GOOG, AMZN, META, NVDA, TSLA, CRM, ADBE, INTC, CSCO, ORCL, IBM, QCOM, AMD | High |
from portfolio_optimizer import get_universe
# For conservative investor (capital preservation, low risk, near retirement)
tickers = get_universe('conservative')
# Returns: ['BND', 'AGG', 'TLT', 'IEF', 'GOVT', 'LQD', 'MBB', 'VMBS']
# For balanced investor (moderate risk, diversification)
tickers = get_universe('global_diversified')
# Returns: ['SPY', 'EFA', 'EEM', 'VWO', 'TLT', 'GLD', 'VNQ', 'LQD', 'HYG', 'DBC', 'IEF', 'GOVT', 'AGG', 'BND', 'VTI']
# For aggressive investor (high risk, growth)
tickers = get_universe('us_tech')
# Returns: ['AAPL', 'MSFT', 'GOOG', 'AMZN', 'META', 'NVDA', 'TSLA', ...]