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Portfolio

gauss314/skills

  • 181 installs
  • 173 repo stars
  • Updated June 14, 2026
  • gauss314/skills

For development and infrastructure management.

About

portfolio is an AI coding tool that enhances development workflows. Builders use it for infrastructure, integration, and platform development within the catalog ecosystem.

  • portfolio
  • Development

Portfolio by the numbers

  • 181 all-time installs (skills.sh)
  • Ranked #2,188 of 4,347 Backend & APIs skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
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Installs181
repo stars★ 173
Last updatedJune 14, 2026
Repositorygauss314/skills ↗

What it does

For development and infrastructure management.

Files

SKILL.mdMarkdownGitHub ↗

Portfolio — Optimización Cuantitativa de Portafolios

Este skill implementa 3 enfoques de optimización de portafolios desde el material del curso (notebook Clase_08_teoria_2025_portafolio.ipynb y PDF Portafolios 2025 Ucema.pdf):

1. Markowitz / Media-Varianza — Optimización convexa vía scipy.optimize + simulación Monte Carlo + frontera eficiente + CML. 2. Black-Litterman — Combinación bayesiana de retornos de equilibrio de mercado (CAPM inverso) con views del inversor, incluyendo matriz de incertidumbre Ω (método Idzorek). 3. HRP / HERC / NCO — Construcción jerárquica de portafolios mediante clustering (single/complete/average/ward), risk parity y NCO con restricciones.

Todos los scripts usan solo numpy, pandas y scipy. Sin dependencias pesadas. Este skill es autónomo: funciona sin skills/backtesting.

Para ratios de performance post-optimización (Sharpe, Sortino, VaR, drawdowns, etc.) consultar el skill hermana: `skills/backtesting`.

Part of the Gauss314 Skills Repository.

---

File Map

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

Qué hace cada script

ScriptRolFunciones clave
portfolio.pyCore de optimización Markowitzmax_sharpe_optim, min_variance_optim, random_portfolios, efficient_frontier, cml_portfolio, asset_stats
black_litterman.pyBlack-Litterman completomarket_implied_risk_aversion, market_implied_prior_returns, bl_posterior_returns, omega_idzorek
hierarchical.pyHRP / HERC / NCOhrp_portfolio, herc_portfolio, nco_portfolio, nco_with_constraints, hrp_constraints
risk_measures.pyMedidas de riesgovar_historic, cvar, max_drawdown, cdar, diversification_ratio, risk_contribution
covariance.pyEstimación de covarianzacov_hist, cov_ledoit_wolf, cov_oas, cov_ewma

---

Quick Start

Markowitz (scipy.optimize)

# 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

Monte Carlo

# 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

Frontera Eficiente

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

CML — Leverage y Deleverage

El portafolio tangente (máximo Sharpe) se combina con el activo libre de riesgo para obtener cualquier punto sobre la Capital Market Line (CML), manteniendo el mismo Sharpe ratio.

# Portafolio tangente puro (w=1)
py scripts/cli.py cml --assets assets/sample_returns.csv --weight 1.0

# Deleverage: 60% en tangencia, 40% en Rf (menos riesgo, mismo Sharpe)
py scripts/cli.py cml --assets assets/sample_returns.csv --weight 0.6

# Leverage: pide prestado 50% a Rf, invierte 150% en tangencia (más riesgo, mismo Sharpe)
py scripts/cli.py cml --assets assets/sample_returns.csv --weight 1.5

Black-Litterman

# 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

HRP / HERC / NCO

# 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

Riesgo

# 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

---

Usar como Librería

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

# --- CML: leverage/deleverage ---
# 60% en tangencia, 40% en Rf (deleverage)
cml = cml_portfolio(rets, rf=0.045, weight_tangency=0.6)
print(cml['ret'], cml['vol'], cml['sharpe'])  # mismo Sharpe que el tangente

# Leverage: 150% en tangencia (pide prestado 50% a Rf)
cml2 = cml_portfolio(rets, rf=0.045, weight_tangency=1.5)
print(cml2['ret'], cml2['vol'], cml2['sharpe'])  # mismo 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

---

Dependencias

LibreríaRequeridaUso
numpy✅Cómputo vectorizado, álgebra lineal
pandas✅CSV I/O, DataFrames
scipy✅optimize (Markowitz), cluster.hierarchy (HRP/NCO), stats

No requiere Riskfolio-Lib, PyPortfolioOpt, sklearn, cvxpy ni arch.

Para visualización (dendrogramas, frontera eficiente) se puede usar matplotlib opcionalmente. Ejemplos de plots están en el notebook de referencia.

---

Referencias Teóricas

  • Markowitz (1952): "Portfolio Selection", Journal of Finance.
  • Black & Litterman (1992): "Global Portfolio Optimization", Financial Analysts Journal.
  • Idzorek (2005): "A Step-by-Step Guide to the Black-Litterman Model".
  • Lopez de Prado (2016): "Building Diversified Portfolios that Outperform Out of Sample" (HRP).
  • De Prado (2019): "Nested Clustered Optimization", SSRN 3469961.
  • Pfitzinger & Katzke (2019): "NCO with Constraints", SSRN 4409173.
  • Meucci (2006): "Beyond Black-Litterman: Views on Non-Normal Markets", SSRN 1213325.
  • Avramov (2004): "Bayesian Variable Selection in Portfolio Analysis", SSRN 3326617.

Para profundizar en ratios de performance (30+ métricas: Sharpe, Sortino, VaR, cVaR, Kelly, Rachev, Profit Factor, etc.) y backtesting de estrategias: `skills/backtesting`.

---

Notebook de referencia

El contenido teórico y ejemplos numéricos de este skill están basados en:

  • temp/Clase_08_teoria_2025_portafolio.ipynb — Implementaciones en Python de

Markowitz, Monte Carlo, NCO (Riskfolio-Lib), Black-Litterman (PyPortfolioOpt).

  • temp/Portafolios 2025 Ucema.pdf — Marco teórico: MPT, CAPM, Fama-French,

clustering, NCO, Black-Litterman.

Las implementaciones flat numpy en scripts/ replican los resultados de esos notebooks sin depender de las librerías mencionadas.

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