メニュー
Execute portfolio optimization using Mean-Variance Optimization (MVO) or Hierarchical Risk Parity (HRP). Use when constructing optimal portfolio weights from a set of assets.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Backtest portfolio performance using historical data. Use when validating portfolio strategy with historical returns, calculating realized metrics, and stress testing.
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.
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.
| name | optimization-execution |
| description | Execute portfolio optimization using Mean-Variance Optimization (MVO) or Hierarchical Risk Parity (HRP). Use when constructing optimal portfolio weights from a set of assets. |
| Investor Profile | Method | Target |
|---|---|---|
| Conservative (capital preservation) | MVO | min_volatility |
| Balanced (risk-adjusted returns) | MVO | max_sharpe |
| Aggressive (maximum growth) | MVO | max_return |
| Diversification-focused | HRP | N/A |
Copy and modify UNIVERSE, TARGET, and MAX_POSITION based on investor profile:
from portfolio_optimizer import (
get_universe, download_market_data,
PortfolioConfig, optimize_portfolio, backtest_portfolio
)
import warnings
warnings.filterwarnings('ignore')
# === CONFIGURE BASED ON INVESTOR ===
UNIVERSE = 'global_diversified' # conservative | global_diversified | us_tech
TARGET = 'max_sharpe' # min_volatility | max_sharpe | max_return
MAX_POSITION = 0.15 # 15% max per asset
# === EXECUTION ===
tickers = get_universe(UNIVERSE)
prices = download_market_data(tickers, '2019-01-01', '2024-01-01')
config = PortfolioConfig(
tickers=list(prices.columns),
start_date='2019-01-01',
end_date='2024-01-01',
optimization_target=TARGET,
max_position=MAX_POSITION
)
result = optimize_portfolio(config, prices)
backtest = backtest_portfolio(result['weights'], prices)
# === RESULTS ===
print(f"=== PORTFOLIO RESULTS ===")
print(f"Universe: {UNIVERSE}")
print(f"Optimization: {TARGET}")
print(f"Max Position Limit: {MAX_POSITION:.0%}")
print(f"\n=== METRICS ===")
print(f"Expected Return: {result['expected_return']:.2%}")
print(f"Volatility: {result['volatility']:.2%}")
print(f"Sharpe Ratio: {result['sharpe_ratio']:.3f}")
print(f"Backtest Max Drawdown: {backtest['max_drawdown']:.2%}")
print(f"Backtest Total Return: {backtest['total_return']:.2%}")
print(f"\n=== ALLOCATION ===")
for ticker, weight in sorted(result['weights'].items(), key=lambda x: -x[1]):
if weight > 0.01:
print(f" {ticker}: {weight:.1%}")
| Target | What It Does | Use When |
|---|---|---|
min_volatility | Minimizes portfolio variance | Capital preservation is priority |
max_sharpe | Maximizes risk-adjusted return | Balanced growth with reasonable risk |
max_return | Maximizes expected return | Growth is priority, high risk tolerance |
For robust diversification without sensitivity to estimation errors:
from portfolio_optimizer import get_universe, download_market_data, optimize_hrp, backtest_portfolio
import warnings
warnings.filterwarnings('ignore')
tickers = get_universe('global_diversified')
prices = download_market_data(tickers, '2019-01-01', '2024-01-01')
result = optimize_hrp(prices)
backtest = backtest_portfolio(result['weights'], prices)
print(f"HRP Expected Return: {result['expected_return']:.2%}")
print(f"HRP Volatility: {result['volatility']:.2%}")
print(f"HRP Max Drawdown: {backtest['max_drawdown']:.2%}")