SKILL.md

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', ...]

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