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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) |