Create app.py

app.py

@@ -0,0 +1,81 @@
+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)