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Create app.py
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app.py
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import streamlit as st
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import pandas as pd
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import numpy as np
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import optuna
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import plotly.express as px
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st.title("Portfolio weights calculator")
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help_string = "NOTA: El formato utilizado aquí es llamando cada columna de GOOGLEFINANCE."
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"Stocks - Sheet1.csv"
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check_box = st.checkbox("¿Deseas usar el archivo precargado?")
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if check_box:
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uploaded_file = "Stocks - Sheet1.csv"
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file_name = uploaded_file
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else:
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uploaded_file = st.file_uploader("Sube aquí tu archivo de excel", type=[".xls", ".xlsx", ".csv"], help=help_string)
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file_name = uploaded_file.name if uploaded_file is not None else None
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if uploaded_file is not None:
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# Can be used wherever a "file-like" object is accepted:
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if file_name[-3:] == "csv":
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df = pd.read_csv(uploaded_file)
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else:
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df = pd.read_excel(uploaded_file)
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df = df.drop(0, axis=0)
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df = df.drop("Unnamed: 2", axis=1).drop("Unnamed: 4", axis=1).rename({"Unnamed: 0": "Date"}, axis=1)
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df['Date'] = pd.to_datetime(df['Date']).dt.date
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stocks = list(df.columns)[-3:]
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stocks_rets = []
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for i in stocks:
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stocks_rets.append(i+"_ret")
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df[i] = df[i].astype(float)
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df[i+"_ret"] = (df[i] - df[i].shift(1)) / df[i].shift(1)
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st.write(df[["Date"] + stocks_rets])
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# Plotting with Plotly
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fig = px.line(df, x=df.Date, y=stocks, labels={'value': 'Value', 'variable': 'Series'}, title='Time Series Plot')
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fig.update_layout(xaxis_title='Date', yaxis_title='Value')
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# Use Streamlit to render the plot
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st.plotly_chart(fig)
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ret_list = df[stocks_rets].mean().to_numpy().reshape(-1, 1)
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cov_matrix = df[stocks_rets].cov().to_numpy()
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optim_choice = st.selectbox("Elige la forma de optomizar :", ("max returns", "min variance", "max returns - variance"))
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def portfolio_variance(weights, covariance_matrix):
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return np.dot(weights.T, np.dot(covariance_matrix, weights))
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def portfolio_returns(weights, expected_returns):
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return np.dot(weights.T, expected_returns)
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if optim_choice == "max returns":
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def objective(trial):
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w1 = trial.suggest_uniform('w1', 0, 1)
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w2 = trial.suggest_uniform('w2', 0, 1)
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w3 = 1 - w1 - w2
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weights = np.array([w1, w2, w3]).reshape(-1, 1)
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return np.dot(weights.T, ret_list)
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study = optuna.create_study(direction="maximize")
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study.optimize(objective, n_trials=100, show_progress_bar=True)
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elif optim_choice == "min variance":
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def objective(trial):
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w1 = trial.suggest_uniform('w1', 0, 1)
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w2 = trial.suggest_uniform('w2', 0, 1)
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w3 = 1 - w1 - w2
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weights = np.array([w1, w2, w3]).reshape(-1, 1)
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return np.dot(weights.T, np.dot(cov_matrix, weights))
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study = optuna.create_study(direction="minimize")
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study.optimize(objective, n_trials=100, show_progress_bar=True)
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else:
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def objective(trial):
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w1 = trial.suggest_uniform('w1', 0, 1)
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w2 = trial.suggest_uniform('w2', 0, 1)
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w3 = 1 - w1 - w2
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weights = np.array([w1, w2, w3]).reshape(-1, 1)
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return np.dot(weights.T, ret_list) - np.dot(weights.T, np.dot(cov_matrix, weights))
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study = optuna.create_study(direction="maximize")
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study.optimize(objective, n_trials=100, show_progress_bar=True)
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w1 = study.best_params['w1']
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w2 = study.best_params['w2']
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w3 = 1- w1 - w2
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weights = np.array([w1, w2, w3]).reshape(-1, 1)
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yearly_returns = (1 + np.dot(weights.T, ret_list)[0, 0]) ** 252 - 1
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yearly_variance = np.dot(weights.T, np.dot(cov_matrix, weights))[0, 0] * 252
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st.write(f"Los pesos son: :green[{stocks[0]} -> {w1:,.4f}], :green[{stocks[1]} -> {w2:,.4f}], :green[{stocks[2]} -> {w3:,.4f}]")
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st.write(f"El retorno anualizado del portafolio es: :green[{yearly_returns:,.4f}]")
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st.write(f"La varianza anualizado del portafolio es: :green[{yearly_variance:,.4f}]")
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