Financial Analysis Skill
Cuándo usar esta skill
- Obtener datos de precios históricos de acciones
- Analizar fundamentales de una empresa (P/E, P/B, etc.)
- Calcular métricas de rentabilidad y riesgo (Sharpe ratio, volatilidad)
- Comparar empresas del mismo sector
- Crear un análisis de cartera básico
Setup
pip install yfinance pandas numpy matplotlib
Obtener datos básicos de una acción
import yfinance as yf
import pandas as pd
import numpy as np
def get_stock_info(ticker: str) -> dict:
"""Información fundamental de una empresa"""
stock = yf.Ticker(ticker)
info = stock.info
return {
"company": info.get("longName"),
"sector": info.get("sector"),
"industry": info.get("industry"),
"market_cap": info.get("marketCap"),
"price": info.get("currentPrice"),
"52w_high": info.get("fiftyTwoWeekHigh"),
"52w_low": info.get("fiftyTwoWeekLow"),
# Valoración
"pe_ratio": info.get("trailingPE"),
"forward_pe": info.get("forwardPE"),
"pb_ratio": info.get("priceToBook"),
"ps_ratio": info.get("priceToSalesTrailing12Months"),
# Crecimiento
"revenue_growth": info.get("revenueGrowth"),
"earnings_growth": info.get("earningsGrowth"),
# Rentabilidad
"roe": info.get("returnOnEquity"),
"roa": info.get("returnOnAssets"),
"gross_margins": info.get("grossMargins"),
"operating_margins": info.get("operatingMargins"),
"profit_margins": info.get("profitMargins"),
# Dividendo
"dividend_yield": info.get("dividendYield"),
"payout_ratio": info.get("payoutRatio"),
# Deuda
"debt_to_equity": info.get("debtToEquity"),
"current_ratio": info.get("currentRatio"),
"quick_ratio": info.get("quickRatio"),
}
def get_price_history(ticker: str, period: str = "1y", interval: str = "1d") -> pd.DataFrame:
"""
Obtener historial de precios
period: 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max
interval: 1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo
"""
stock = yf.Ticker(ticker)
hist = stock.history(period=period, interval=interval)
return hist
Análisis de rendimiento
def performance_analysis(ticker: str, period: str = "1y") -> dict:
"""Métricas de rendimiento y riesgo"""
hist = get_price_history(ticker, period=period)
# Retornos diarios
returns = hist['Close'].pct_change().dropna()
# Métricas básicas
total_return = (hist['Close'].iloc[-1] / hist['Close'].iloc[0] - 1) * 100
# Volatilidad anualizada (std diaria * sqrt(252))
annual_volatility = returns.std() * np.sqrt(252) * 100
# Sharpe Ratio (asumiendo risk-free rate del 4%)
risk_free_rate = 0.04
annual_return = returns.mean() * 252
sharpe_ratio = (annual_return - risk_free_rate) / (returns.std() * np.sqrt(252))
# Maximum Drawdown
rolling_max = hist['Close'].expanding().max()
drawdown = (hist['Close'] - rolling_max) / rolling_max * 100
max_drawdown = drawdown.min()
# Beta vs S&P 500
spy = yf.Ticker("SPY").history(period=period)['Close'].pct_change().dropna()
# Alinear índices
aligned = pd.concat([returns, spy], axis=1, join='inner')
aligned.columns = ['stock', 'market']
covariance = aligned.cov()
beta = covariance.iloc[0, 1] / covariance.iloc[1, 1]
return {
"ticker": ticker,
"period": period,
"total_return_pct": round(total_return, 2),
"annual_volatility_pct": round(annual_volatility, 2),
"sharpe_ratio": round(sharpe_ratio, 3),
"max_drawdown_pct": round(max_drawdown, 2),
"beta": round(beta, 3),
"avg_daily_return_pct": round(returns.mean() * 100, 4),
"best_day_pct": round(returns.max() * 100, 2),
"worst_day_pct": round(returns.min() * 100, 2),
}
Análisis comparativo de empresas
def compare_companies(tickers: list[str]) -> pd.DataFrame:
"""Comparar métricas financieras de múltiples empresas"""
metrics = []
for ticker in tickers:
try:
info = get_stock_info(ticker)
perf = performance_analysis(ticker)
metrics.append({
"Ticker": ticker,
"Empresa": info["company"],
"Sector": info["sector"],
"Market Cap (B)": round(info["market_cap"] / 1e9, 1) if info["market_cap"] else None,
"P/E": round(info["pe_ratio"], 1) if info["pe_ratio"] else None,
"P/B": round(info["pb_ratio"], 2) if info["pb_ratio"] else None,
"Margen neto %": round(info["profit_margins"] * 100, 1) if info["profit_margins"] else None,
"ROE %": round(info["roe"] * 100, 1) if info["roe"] else None,
"Crec. ingresos %": round(info["revenue_growth"] * 100, 1) if info["revenue_growth"] else None,
"Deuda/Equity": round(info["debt_to_equity"], 2) if info["debt_to_equity"] else None,
"Div. Yield %": round(info["dividend_yield"] * 100, 2) if info["dividend_yield"] else None,
"Retorno 1Y %": perf["total_return_pct"],
"Volatilidad %": perf["annual_volatility_pct"],
"Sharpe": perf["sharpe_ratio"],
"Beta": perf["beta"],
})
except Exception as e:
print(f"Error con {ticker}: {e}")
return pd.DataFrame(metrics).set_index("Ticker")
# Comparar empresas de big tech
comparison = compare_companies(["AAPL", "MSFT", "GOOGL", "META", "AMZN"])
print(comparison.to_string())
Análisis de cartera
def portfolio_analysis(holdings: dict, period: str = "1y") -> dict:
"""
holdings: {"AAPL": 0.30, "MSFT": 0.25, "GOOGL": 0.20, "AMZN": 0.25} # pesos
"""
all_returns = {}
for ticker in holdings:
hist = get_price_history(ticker, period=period)
all_returns[ticker] = hist['Close'].pct_change().dropna()
returns_df = pd.DataFrame(all_returns).dropna()
weights = np.array([holdings[t] for t in returns_df.columns])
# Retorno de cartera
portfolio_returns = returns_df @ weights
# Métricas de cartera
annual_return = portfolio_returns.mean() * 252 * 100
annual_vol = portfolio_returns.std() * np.sqrt(252) * 100
sharpe = (portfolio_returns.mean() * 252 - 0.04) / (portfolio_returns.std() * np.sqrt(252))
# Matriz de correlaciones
correlation_matrix = returns_df.corr().round(3)
return {
"total_return_pct": round((portfolio_returns + 1).prod() * 100 - 100, 2),
"annual_return_pct": round(annual_return, 2),
"annual_volatility_pct": round(annual_vol, 2),
"sharpe_ratio": round(sharpe, 3),
"correlations": correlation_matrix.to_dict(),
"individual_weights": holdings,
}
Interpretar métricas financieras
P/E Ratio (Price-to-Earnings):
< 15 → Value stock (puede estar infravalorada o en declive)
15-25 → Fair value (typical)
25-40 → Growth premium (mercado espera crecimiento)
> 40 → Muy caro o high-growth (ej: tech en expansión)
N/A → Sin beneficios (pérdidas)
P/B Ratio (Price-to-Book):
< 1 → Cotiza por debajo del valor contable (oportunidad o trampa)
1-3 → Razonable para muchos sectores
> 5 → Típico en empresas de tech/servicios (alto intangible)
ROE (Return on Equity):
< 10% → Bajo
10-20% → Bueno
> 20% → Excelente
Margen neto:
< 5% → Bajo (ej: retail, distribución)
5-15% → Moderado
> 20% → Alto (ej: software, pharma)
Beta:
< 1 → Menos volátil que el mercado (defensivo)
= 1 → Mueve como el mercado
> 1 → Más volátil (mayor riesgo/retorno potencial)
Deuda/Equity:
< 0.5 → Conservador
0.5-1.5 → Moderado
> 2 → Alto (puede ser un riesgo)
⚠️ Importante: Siempre comparar con el sector. Un P/E alto no es malo
si la empresa crece rápido. Un P/E bajo puede indicar problemas.
Referencias
