Update app.py

app.py

@@ -5,60 +5,52 @@ 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)['Adj Close']
-    return data
+    data = yf.download(tickers, start=start, end=end)
+    if 'Adj Close' in data.columns:
+        return data['Adj Close']
+    else:
+        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)
     
-    # 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
-
-    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
+        return -portfolio_return / portfolio_volatility
 
+    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  # Bobot optimal
+    return result.x
 
-# 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.isnull().values.any():
-            st.error("Data tidak lengkap atau saham tidak ditemukan.")
+        
+        if stock_data is None or stock_data.isnull().values.any():
+            st.error("Data tidak ditemukan atau tidak lengkap. Periksa ticker atau tanggal yang dipilih.")
         else:
-            # Hitung return dan kovarians
             mean_returns, cov_matrix = calculate_returns(stock_data)
-
-            # Optimasi portofolio
             optimal_weights = optimize_portfolio(mean_returns, cov_matrix)
-
-            # Tampilkan hasil
+            
             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')
@@ -66,6 +58,5 @@ if st.button("Analisis Portofolio"):
             ax.set_ylabel("Return Tahunan")
             ax.set_title("Efficient Frontier")
             st.pyplot(fig)
-
     except Exception as e:
-        st.error(f"Terjadi kesalahan: {e}")
+        st.error(f"Terjadi kesalahan: {e}")