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.
Universe Selection Skill
Quick Reference
| 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') |
Decision Matrix
| 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 |
Available Universes
| 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 |
Ready-to-Run Code
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', ...]
By