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import streamlit as st
import pandas as pd
import numpy as np
import requests
import matplotlib.pyplot as plt
import io
st.title("Portfolio weights calculator")
help_string = "NOTA: El formato utilizado aquí es llamando cada columna de GOOGLEFINANCE."
check_box = st.checkbox("¿Deseas usar el archivo precargado?")
if check_box:
uploaded_file = "Stocks - Sheet1.csv"
file_name = uploaded_file
else:
uploaded_file = st.file_uploader("Sube aquí tu archivo de excel", type=[".xls", ".xlsx", ".csv"], help=help_string)
file_name = uploaded_file.name if uploaded_file is not None else None
if uploaded_file is not None:
if file_name[-3:] == "csv":
df = pd.read_csv(uploaded_file)
else:
df = pd.read_excel(uploaded_file)
df = df.drop(0, axis=0)
df = df.drop("Unnamed: 2", axis=1).drop("Unnamed: 4", axis=1).rename({"Unnamed: 0": "Date"}, axis=1)
df['Date'] = pd.to_datetime(df['Date']).dt.date
stocks = list(df.columns)[-3:]
stocks_rets = []
for i in stocks:
stocks_rets.append(i+"_ret")
df[i] = df[i].astype(float)
df[i+"_ret"] = (df[i] - df[i].shift(1)) / df[i].shift(1)
st.write(df[["Date"] + stocks_rets])
for stock in stocks:
plt.plot(df["Date"], df[stock], label=stock)
plt.xlabel('Date')
plt.ylabel('Value')
plt.title('Time Series Plot')
plt.legend()
plt.xticks(rotation=45)
st.pyplot(plt)
ret_list = df[stocks_rets].mean().to_numpy().reshape(-1, 1)
cov_matrix = df[stocks_rets].cov().to_numpy()
# Cálculo de los pesos del portafolio
n = len(stocks)
weights = np.ones((n, 1)) / n
yearly_returns = np.dot(weights.T, ret_list)[0, 0] * 252
yearly_variance = np.dot(weights.T, np.dot(cov_matrix, weights))[0, 0] * 252
st.write("Los pesos son:", ", ".join([f"{stocks[i]} -> {weights[i,0]:.4f}" for i in range(n)]))
st.write(f"El retorno anualizado del portafolio es: {yearly_returns:.4f}")
st.write(f"La varianza anualizada del portafolio es: {yearly_variance:.4f}")
# Define api_url dentro del bloque donde estableces la conexión a la API Alpha Vantage
api_url = "https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&apikey=QVQGE7YPO68S403J&datatype=csv"
# Stock symbols
symbols = ['AMZN', 'MELI', 'ETSY']
# Fetch and display data for each stock
for symbol in symbols:
st.subheader(symbol)
response = requests.get(f"{api_url}&symbol={symbol}")
if response.status_code == 200:
data = pd.read_csv(io.BytesIO(response.content))
st.write(f"Datos de la acción {symbol}:")
st.write(data.head())
else:
st.write(f"Error al obtener los datos de la acción {symbol}. Código de estado:", response.status_code) |