skills/portfolio/
├── SKILL.md                           ← Este archivo
├── references/
│   ├── PORTFOLIO_THEORY.md            ← MPT, Markowitz, frontera eficiente (ES)
│   ├── BLACK_LITTERMAN.md             ← BL: prior, views, posterior, omega (ES)
│   ├── HIERARCHICAL.md                ← HRP, HERC, NCO, clustering (ES)
│   └── RISK_MEASURES.md               ← VaR, CVaR, MAD, MSV, DR, MDD (ES)
├── assets/
│   ├── sample_prices.csv              ← Precios multi-activo para ejemplos
│   ├── sample_returns.csv             ← Retornos multi-activo
│   ├── sample_mcaps.json              ← Market caps para Black-Litterman
│   └── defaults.json                  ← Parámetros default
├── scripts/
│   ├── __init__.py
│   ├── portfolio.py                   ← Core: Markowitz, Sharpe, Monte Carlo, frontera
│   ├── black_litterman.py             ← BL: prior, posterior, omega, views
│   ├── hierarchical.py                ← HRP/HERC/NCO: clustering, risk parity, constraints
│   ├── risk_measures.py               ← VaR, CVaR, MAD, MSV, MDD, DR
│   ├── covariance.py                  ← Covarianza: hist, ledoit-wolf, oas, ewma
│   └── cli.py                         ← CLI unificada (12 modos)
└── tests/
    ├── __init__.py
    └── test_portfolio.py              ← Tests + validación contra notebook

# Max Sharpe con 3 activos
py scripts/cli.py markowitz --assets assets/sample_returns.csv

# Con tasa libre de riesgo personalizada
py scripts/cli.py markowitz --assets assets/sample_returns.csv --rf 0.05

# Estadísticas individuales
py scripts/cli.py stats --assets assets/sample_returns.csv

# Simular 10.000 carteras aleatorias
py scripts/cli.py montecarlo --assets assets/sample_returns.csv

# Guardar frontera a CSV
py scripts/cli.py montecarlo --assets assets/sample_returns.csv --save frontier.csv

py scripts/cli.py frontier --assets assets/sample_returns.csv --n 50

# Prior: retornos implícitos de mercado (CAPM inverso)
py scripts/cli.py bl-prior --assets assets/sample_returns.csv --market-prices assets/sample_prices.csv --mcaps assets/sample_mcaps.json

# BL completo con views + optimización
py scripts/cli.py bl --assets assets/sample_returns.csv --market-prices assets/sample_prices.csv --mcaps assets/sample_mcaps.json --views '{"BMA": 0.25, "LOMA": 0.4, "MELI": -0.1}' --confidences "0.3,0.5,0.8" --optimize

# Hierarchical Risk Parity
py scripts/cli.py hrp --assets assets/sample_returns.csv

# Nested Clustered Optimization
py scripts/cli.py nco --assets assets/sample_returns.csv --clusters 3

# NCO con restricciones
py scripts/cli.py nco-con --assets assets/sample_returns.csv --constraints assets/sample_constraints.csv --classes assets/sample_classes.csv

# Todas las medidas de riesgo
py scripts/cli.py risk --prices assets/sample_prices.csv

# Medida específica
py scripts/cli.py risk --prices assets/sample_prices.csv --measure var

from scripts.portfolio import *
from scripts.black_litterman import *
from scripts.hierarchical import *

import numpy as np

# --- Markowitz ---
rets = pd.read_csv('assets/sample_returns.csv', index_col=0)
result = max_sharpe_optim(rets, rf=0.045)
print(result['weights'], result['sharpe'])  # pesos óptimos, Sharpe

# --- Monte Carlo ---
port_df = random_portfolios(rets, n_portfolios=10000, rf=0.045)
best = port_df.loc[port_df['sharpe'].idxmax()]
print(best['weights'])  # mejor combinación Monte Carlo

# --- Black-Litterman ---
import json
with open('assets/sample_mcaps.json') as f:
    mcaps = json.load(f)
spy = pd.read_csv('assets/sample_prices.csv')['SPY'].pct_change().dropna()
bl_result = bl_pipeline(rets, spy.values, mcaps,
                        view_dict={'BMA': 0.25, 'LOMA': 0.4},
                        view_confidences=[0.3, 0.5], rf=0.045)
print(bl_result['posterior'])  # retornos a posteriori

# --- HRP ---
hrp_result = hrp_portfolio(rets, linkage_method='ward')
print(hrp_result['weights'])  # pesos HRP