app.py

import streamlit as st
import pandas as pd
import numpy as np
import requests
import matplotlib.pyplot as plt
import io

# Función para obtener los datos de una acción utilizando Alpha Vantage
def get_stock_data(symbol):
    api_key = "QVQGE7YPO68S403J"
    url = f"https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol={symbol}&apikey={api_key}&datatype=csv"
    response = requests.get(url)
    data = pd.read_csv(io.StringIO(response.text))
    data['Date'] = pd.to_datetime(data['Date'])
    return data

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}")

    # Integración con Alpha Vantage
    st.write("\n\n**Datos de las acciones obtenidos de Alpha Vantage:**")
    stocks_symbols = ["AMZN", "MELI", "ETSY"]

    # Descargar datos de acciones
    stocks_data = {symbol: get_stock_data(symbol) for symbol in stocks_symbols}

    # Crear DataFrame combinado con los datos de las acciones
    df = pd.concat([data.set_index('Date')['close'].rename(symbol) for symbol, data in stocks_data.items()], axis=1)

    # Calcular rendimientos diarios
    returns = df.pct_change()

    # Eliminar primera fila con NaN
    returns = returns.dropna()

    # Mostrar tabla con los rendimientos diarios
    st.write("Rendimientos diarios:")
    st.write(returns.head())

    # Gráfico de series temporales
    st.write("Gráfico de series temporales:")
    plt.figure(figsize=(10, 6))
    for symbol in stocks_symbols:
        plt.plot(df.index, df[symbol], label=symbol)
    plt.xlabel('Fecha')
    plt.ylabel('Valor')
    plt.title('Gráfico de Series Temporales')
    plt.legend()
    st.pyplot(plt)

    # Calcular rendimientos anuales y varianza
    weights = np.ones((len(stocks_symbols), 1)) / len(stocks_symbols)
    annual_returns = returns.mean() * 252
    annual_variance = np.dot(weights.T, np.dot(returns.cov() * 252, weights))

    # Mostrar resultados
    st.write("Los pesos son:")
    for i, symbol in enumerate(stocks_symbols):
        st.write(f"{symbol} -> {weights[i, 0]:.4f}")

    st.write(f"El retorno anualizado del portafolio es: {annual_returns.sum():.4f}")
    st.write(f"La varianza anualizada del portafolio es: {annual_variance[0, 0]:.4f}")