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

    # Gunakan 'Adj Close' jika ada, jika tidak pakai 'Close'
    if 'Adj Close' in data.columns:
        return data['Adj Close']
    elif 'Close' in data.columns:
        st.warning("Menggunakan 'Close' karena 'Adj Close' tidak tersedia.")
        return data['Close']
    else:
        st.error("Data harga penutupan tidak ditemukan.")
        return None

# 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

# Fungsi untuk mensimulasikan portofolio acak
def generate_efficient_frontier(returns, cov_matrix, num_portfolios=5000):
    num_assets = len(returns)
    results = np.zeros((3, num_portfolios))

    for i in range(num_portfolios):
        weights = np.random.dirichlet(np.ones(num_assets), size=1)[0]
        portfolio_return = np.dot(weights, returns)
        portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))
        sharpe_ratio = portfolio_return / portfolio_volatility

        results[0, i] = portfolio_return
        results[1, i] = portfolio_volatility
        results[2, i] = sharpe_ratio

    return results

# 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("2023-12-31"))

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:
            st.write("Saham dengan data tersedia:", stock_data.columns)  # Debugging
            
            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(stock_data.columns):
                    st.write(f"{stock}: {optimal_weights[i]:.2%}")

                # Simulasi Efficient Frontier
                results = generate_efficient_frontier(mean_returns, cov_matrix)

                st.subheader("Efficient Frontier")
                fig, ax = plt.subplots()
                scatter = ax.scatter(results[1, :], results[0, :], c=results[2, :], cmap="viridis", marker='o')
                ax.set_xlabel("Risiko (Standar Deviasi)")
                ax.set_ylabel("Return Tahunan")
                ax.set_title("Efficient Frontier")
                fig.colorbar(scatter, label="Sharpe Ratio")
                st.pyplot(fig)
            else:
                st.error("Optimasi portofolio gagal. Coba dengan saham yang berbeda.")

    except Exception as e:
        st.error(f"Terjadi kesalahan: {e}")