Update app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import optuna
+import plotly.express as px
+import requests
+import io
+
+st.title("Portfolio weights calculator")
+
+help_string = "NOTA: Para su información los datos utilizados se extrajeron de GOOGLEFINANCE."
+
+api_url = "https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&apikey=QVQGE7YPO68S403J&datatype=csv"
+symbols = ['AMZN', 'MELI', 'ETSY']
+
+option = st.selectbox("Selecciona la fuente de datos:", ("API Alpha Vantage", "Subir archivo propio", "Usar archivo predeterminado"))
+
+if option == "API Alpha Vantage":
+    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)
+elif option == "Subir archivo propio":
+    uploaded_file = st.file_uploader("Sube aquí tu archivo de excel", type=[".xls", ".xlsx", ".csv"], help=help_string)
+    if uploaded_file is not None:
+        # Cargar y procesar el archivo subido
+        if file_name[-3:] == "csv":
+            df = pd.read_csv(uploaded_file)
+        else:
+            df = pd.read_excel(uploaded_file)
+        # Resto del procesamiento del archivo ...
+elif option == "Usar archivo predeterminado":
+    uploaded_file = "STOCKS - Hoja 1.csv"  # Opcional: proporciona la ruta al archivo predeterminado
+    # Cargar y procesar el archivo predeterminado
+    # Resto del procesamiento del archivo ...
+
+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'], format="%d/%m/%Y %H:%M:%S")
+    
+    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])
+    
+    fig = px.line(df, x=df.Date, y=stocks, labels={'value': 'Value', 'variable': 'Series'}, title='Time Series Plot')
+    fig.update_layout(xaxis_title='Date', yaxis_title='Value')
+    
+    st.plotly_chart(fig)
+    
+    ret_list = df[stocks_rets].mean().to_numpy().reshape(-1, 1)
+    cov_matrix = df[stocks_rets].cov().to_numpy()
+    
+    optim_choice = st.selectbox("Elige la forma de optomizar :", ("max returns", "min variance", "max returns - variance"))
+    
+    def portfolio_variance(weights, covariance_matrix):
+        return np.dot(weights.T, np.dot(covariance_matrix, weights))
+    
+    def portfolio_returns(weights, expected_returns):
+        return np.dot(weights.T, expected_returns)
+    
+    if optim_choice == "max returns":
+        def objective(trial):
+            w1 = trial.suggest_uniform('w1', 0, 1)
+            w2 = trial.suggest_uniform('w2', 0, 1)
+            w3 = 1 - w1 - w2
+            weights = np.array([w1, w2, w3]).reshape(-1, 1)
+            return np.dot(weights.T, ret_list)
+    
+        study = optuna.create_study(direction="maximize")
+        study.optimize(objective, n_trials=100, show_progress_bar=True)
+    
+    elif optim_choice == "min variance":
+        def objective(trial):
+            w1 = trial.suggest_uniform('w1', 0, 1)
+            w2 = trial.suggest_uniform('w2', 0, 1)
+            w3 = 1 - w1 - w2
+            weights = np.array([w1, w2, w3]).reshape(-1, 1)
+            return np.dot(weights.T, np.dot(cov_matrix, weights))
+    
+        study = optuna.create_study(direction="minimize")
+        study.optimize(objective, n_trials=100, show_progress_bar=True)
+    
+    else:
+        def objective(trial):
+            w1 = trial.suggest_uniform('w1', 0, 1)
+            w2 = trial.suggest_uniform('w2', 0, 1)
+            w3 = 1 - w1 - w2
+            weights = np.array([w1, w2, w3]).reshape(-1, 1)
+            return np.dot(weights.T, ret_list) - np.dot(weights.T, np.dot(cov_matrix, weights))
+    
+        study = optuna.create_study(direction="maximize")
+        study.optimize(objective, n_trials=100, show_progress_bar=True)
+    
+    w1 = study.best_params['w1']
+    w2 = study.best_params['w2']
+    w3 = 1- w1 - w2
+    
+    weights = np.array([w1, w2, w3]).reshape(-1, 1)
+    
+    yearly_returns = (1 + np.dot(weights.T, ret_list)[0, 0]) ** 252 - 1
+    yearly_variance = np.dot(weights.T, np.dot(cov_matrix, weights))[0, 0] * 252
+        
+    st.write(f"Los pesos son: :green[{stocks[0]} -> {w1:,.4f}], :green[{stocks[1]} -> {w2:,.4f}], :green[{stocks[2]} -> {w3:,.4f}]")
+    st.write(f"El retorno anualizado del portafolio es: :green[{yearly_returns:,.4f}]")
+    st.write(f"La varianza anualizado del portafolio es: :green[{yearly_variance:,.4f}]")
+
+