Taller Clustering acciones S&P 500#
Aplicar los tres métodos de clustering estudiados y, con cada uno, determinar los mejores hiperparámetros para obtener los mejores clusters.
import yfinance as yf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Obtener la lista de tickers del S&P 500 desde Wikipedia
url = "https://en.wikipedia.org/wiki/List_of_S%26P_500_companies"
table = pd.read_html(url)
tickers_df = table[0]
tickers = tickers_df["Symbol"].tolist()
# Descargar datos históricos para cada ticker
data = {}
for ticker in tickers:
data[ticker] = yf.download(ticker, start="2023-06-30", end="2024-07-01")
# Crear un DataFrame que contenga los precios de cierre de todas las acciones
closing_prices = pd.DataFrame({ticker: data[ticker]["Close"] for ticker in tickers})
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# Calcular características (media y desviación estándar del retorno diario)
features = []
for ticker in tickers:
df = data[ticker]
df["Rendimiento diario"] = df["Adj Close"].pct_change()
mean_return = df["Rendimiento diario"].mean()
std_return = df["Rendimiento diario"].std()
features.append([ticker, mean_return, std_return])
# Crear un DataFrame con las características
features_df = pd.DataFrame(
features, columns=["Ticker", "Media rendimientos", "Desviación estándar"]
)
# Graficar los datos
plt.figure(figsize=(10, 6))
plt.scatter(
features_df["Media rendimientos"], features_df["Desviación estándar"], color="blue"
)
# Añadir etiquetas a cada punto
for i in range(features_df.shape[0]):
plt.text(
features_df["Media rendimientos"][i],
features_df["Desviación estándar"][i],
features_df["Ticker"][i],
)
plt.xlabel("Media rendimientos")
plt.ylabel("esvación estándar")
plt.title("Media rendimientos vs. Desviación estándar")
plt.grid(True)
posx and posy should be finite values
posx and posy should be finite values
posx and posy should be finite values
posx and posy should be finite values
posx and posy should be finite values
posx and posy should be finite values
