Update app.py

app.py

@@ -1,69 +1,71 @@
 import streamlit as st
-import pandas as pd
-import numpy as np
 import yfinance as yf
+import numpy as np
+import pandas as pd
 import matplotlib.pyplot as plt
-from scipy.optimize import minimize
+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)
-    st.write("Data mentah yang diunduh:")
-    st.write(data.head())  # Menampilkan 5 baris pertama untuk debug
-    if 'Adj Close' in data.columns:
-        return data['Adj Close']
-    else:
-        st.error("Kolom 'Adj Close' tidak ditemukan. Coba cek ticker atau tanggal.")
-        return None
+    data = yf.download(tickers, start=start, end=end)['Adj Close']
+    return data
 
-# Fungsi untuk menghitung return harian
-def calculate_daily_returns(data):
-    return data.pct_change().dropna()
+# 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 dengan Model Markowitz
-def optimize_portfolio(returns):
-    num_assets = len(returns.columns)
-    weights = np.random.random(num_assets)
-    weights /= np.sum(weights)
+# Fungsi untuk menghitung portofolio optimal
+def optimize_portfolio(returns, cov_matrix):
+    num_assets = len(returns)
+    
+    # Fungsi untuk menghitung rasio Sharpe (return / risiko)
+    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
 
-    def portfolio_performance(weights):
-        port_return = np.sum(returns.mean() * weights) * 252
-        port_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))
-        return port_volatility
+    constraints = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1})  # Total bobot = 100%
+    bounds = tuple((0, 1) for _ in range(num_assets))  # Bobot saham antara 0 - 1
+    init_guess = num_assets * [1. / num_assets]  # Tebakan awal
 
-    constraints = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1})
-    bounds = tuple((0, 1) for _ in range(num_assets))
-    result = minimize(portfolio_performance, weights, method='SLSQP', bounds=bounds, constraints=constraints)
-    
-    return result.x
+    result = sco.minimize(sharpe_ratio, init_guess, method='SLSQP', bounds=bounds, constraints=constraints)
+    return result.x  # Bobot optimal
 
 # Streamlit UI
-st.title("Analisis Portofolio Saham Model Markowitz")
+st.title("Analisis Portofolio Saham Optimal (Model Markowitz)")
 
-# Input pengguna untuk daftar saham
-tickers = st.text_input("Masukkan kode saham (pisahkan dengan koma)", "BBCA.JK,TLKM.JK,UNVR.JK")
+# 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"):
-    tickers_list = [t.strip() for t in tickers.split(',')]
-    data = get_stock_data(tickers_list, start_date, end_date)
-    
-    if not data.empty:
-        st.write("Data Harga Saham")
-        st.line_chart(data)
+if st.button("Analisis Portofolio"):
+    try:
+        # Ambil data saham
+        stock_data = get_stock_data(tickers_list, start_date, end_date)
+        if stock_data.isnull().values.any():
+            st.error("Data tidak lengkap atau saham tidak ditemukan.")
+        else:
+            # Hitung return dan kovarians
+            mean_returns, cov_matrix = calculate_returns(stock_data)
+
+            # Optimasi portofolio
+            optimal_weights = optimize_portfolio(mean_returns, cov_matrix)
 
-        returns = calculate_daily_returns(data)
-        optimal_weights = optimize_portfolio(returns)
+            # Tampilkan hasil
+            st.subheader("Bobot Portofolio Optimal:")
+            for i, stock in enumerate(tickers_list):
+                st.write(f"{stock}: {optimal_weights[i]:.2%}")
 
-        st.write("Bobot Optimal Portofolio:")
-        for ticker, weight in zip(tickers_list, optimal_weights):
-            st.write(f"{ticker}: {weight:.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)
 
-        # Visualisasi Portofolio
-        fig, ax = plt.subplots()
-        ax.pie(optimal_weights, labels=tickers_list, autopct='%1.1f%%', startangle=90)
-        ax.axis("equal")
-        st.pyplot(fig)
-    else:
-        st.error("Gagal mengambil data saham. Pastikan kode saham benar.")
+    except Exception as e:
+        st.error(f"Terjadi kesalahan: {e}")