app.py

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}")