Update app.py

app.py

@@ -1,76 +1,106 @@
 import streamlit as st
-import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt
-import random
-
-# Streamlit app layout
-st.title("Estimating statistics from two lists of historical values")
-
-with st.form("my_form"):
-    N = st.number_input("How many elements do you want for each list?", step=1)
-
-    # Button to generate the numbers
-    if st.form_submit_button("Click to generate random numbers' lists"):
-        # Generate two lists of 100 random numbers each
-        x = [random.randint(0, 20) for _ in range(N)]
-        y = [random.randint(0, 20) for _ in range(N)]
-
-    # Display the lists in the app
-    # st.write('List 1:', x)
-    # st.write('List 2:', y)
-
-if "x" in globals():
-    x_bar = np.mean(x)
-    y_bar = np.mean(y)
-
-    st.subheader("Expected values")
-    st.write(f"E(x) = {x_bar:,.2f}")
-    st.write(f"E(y) = {y_bar:,.2f}")
-
-    var_x = np.var(x)
-    var_y = np.var(y)
-
-    st.subheader("Variances")
-    st.write(f"var(x) = {var_x:,.2f}")
-    st.write(f"var(y) = {var_y:,.2f}")
-
-    cov_xy = np.corrcoef(x, y)[0, 1]
-
-    st.subheader("Correlation")
-    st.write(f"corr(x, y) = {cov_xy:,.2f}")
-
-    plt.scatter(x, y)
-    plt.title("Correlations")
-    plt.xlabel("x")
-    plt.ylabel("y")
-
-    st.pyplot(plt)
-
-    plt.close()
-
-    plt.title("Plot of returns")
-    plt.plot(np.arange(len(x)), x)
-    plt.plot(np.arange(len(y)), y)
-    plt.ylim(-10, 40)
-    plt.xlabel('"time"')
-    plt.ylabel('"returns"')
+import numpy as np
+import optuna
+import plotly.express as px
 
-    st.pyplot(plt)
+st.title("Portfolio weights calculator")
 
-    w_x = (x_bar - y_bar + var_y - cov_xy * np.sqrt(var_x * var_y)) / (
-        var_x + var_y - 2 * cov_xy * np.sqrt(var_x * var_y)
-    )
-    w_y = 1 - w_x
+help_string = "NOTA: El formato utilizado aquí es llamando cada columna de GOOGLEFINANCE."
 
-    var_r = (w_x**2)*var_x + (w_y**2)*var_y + 2*w_x*w_y*cov_xy*np.sqrt(var_x * var_y)
+"Stocks - Sheet1.csv"
 
-    st.subheader(f"Weights and portfolio returns (this is Plus, but you will need to explain how you obtained the answers)")
-    st.write("Assuming x and y represent returns of portfolios")
-    st.write(f"w_x = {w_x:,.2f}")
-    st.write(f"w_y = {w_y:,.2f}")
-    st.write(f"E(r) = {w_x*x_bar + w_y*y_bar:,.2f}")
-    st.write(f"var(r) = {var_r:,.2f}")
+check_box = st.checkbox("¿Deseas usar el archivo precargado?")
 
+if check_box:
+    uploaded_file = "Stocks - Sheet1.csv"
+    file_name = uploaded_file
 else:
-    st.write(":red[Please, give the number of elements in each list]")
+    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:
+    # Can be used wherever a "file-like" object is accepted:
+    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])
+    
+    # Plotting with Plotly
+    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')
+    
+    # Use Streamlit to render the plot
+    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}]")