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| import streamlit as st | |
| import yfinance as yf | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import scipy.optimize as sco | |
| # Fungsi untuk mengunduh data saham | |
| def get_stock_data(tickers, start, end): | |
| data = yf.download(tickers, start=start, end=end) | |
| if data.empty: | |
| st.error("Data saham tidak ditemukan. Periksa ticker atau rentang tanggal.") | |
| return None | |
| # Cek semua kolom yang tersedia dalam data | |
| st.write("Kolom yang tersedia dalam data:", data.columns.tolist()) | |
| if 'Adj Close' not in data.columns: | |
| st.error("Kolom 'Adj Close' tidak ditemukan. Data yang tersedia ditampilkan di atas.") | |
| return None | |
| return data['Adj Close'] | |
| # Fungsi untuk menghitung return tahunan dan matriks kovarians | |
| def calculate_returns(data): | |
| log_returns = np.log(data / data.shift(1)) | |
| return log_returns.mean() * 252, log_returns.cov() * 252 | |
| # Fungsi untuk menghitung portofolio optimal | |
| def optimize_portfolio(returns, cov_matrix): | |
| num_assets = len(returns) | |
| def sharpe_ratio(weights): | |
| portfolio_return = np.dot(weights, returns) | |
| portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights))) | |
| return -portfolio_return / portfolio_volatility # Negatif untuk minimisasi | |
| constraints = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1}) | |
| bounds = tuple((0, 1) for _ in range(num_assets)) | |
| init_guess = num_assets * [1. / num_assets] | |
| result = sco.minimize(sharpe_ratio, init_guess, method='SLSQP', bounds=bounds, constraints=constraints) | |
| return result.x if result.success else None | |
| # Streamlit UI | |
| st.title("Analisis Portofolio Saham Optimal (Model Markowitz)") | |
| # Input Saham & Tanggal | |
| tickers_list = st.text_input("Masukkan ticker saham (contoh: BBCA.JK, TLKM.JK, BBRI.JK)", "BBCA.JK, TLKM.JK, BBRI.JK").split(", ") | |
| start_date = st.date_input("Pilih tanggal mulai", pd.to_datetime("2020-01-01")) | |
| end_date = st.date_input("Pilih tanggal akhir", pd.to_datetime("2020-12-31")) | |
| if st.button("Analisis Portofolio"): | |
| try: | |
| # Ambil data saham | |
| stock_data = get_stock_data(tickers_list, start_date, end_date) | |
| if stock_data is not None: | |
| mean_returns, cov_matrix = calculate_returns(stock_data) | |
| # Optimasi portofolio | |
| optimal_weights = optimize_portfolio(mean_returns, cov_matrix) | |
| if optimal_weights is not None: | |
| st.subheader("Bobot Portofolio Optimal:") | |
| for i, stock in enumerate(tickers_list): | |
| st.write(f"{stock}: {optimal_weights[i]:.2%}") | |
| # Plot Efficient Frontier | |
| st.subheader("Efficient Frontier") | |
| fig, ax = plt.subplots() | |
| ax.scatter(np.sqrt(np.diag(cov_matrix)), mean_returns, c=mean_returns / np.sqrt(np.diag(cov_matrix)), marker='o') | |
| ax.set_xlabel("Risiko (Standar Deviasi)") | |
| ax.set_ylabel("Return Tahunan") | |
| ax.set_title("Efficient Frontier") | |
| st.pyplot(fig) | |
| else: | |
| st.error("Optimasi portofolio gagal. Coba dengan saham yang berbeda.") | |
| except Exception as e: | |
| st.error(f"Terjadi kesalahan: {e}") |