القائمة
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.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
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.
استنادا إلى تصنيف SOC المهني
| 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', ...]