Update app.py

app.py

@@ -8,7 +8,7 @@ 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
@@ -22,7 +22,7 @@ def get_stock_data(tickers, start, end):
     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))
@@ -44,13 +44,30 @@ def optimize_portfolio(returns, cov_matrix):
     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("2020-12-31"))
+end_date = st.date_input("Pilih tanggal akhir", pd.to_datetime("2023-12-31"))
 
 if st.button("Analisis Portofolio"):
     try:
@@ -58,6 +75,8 @@ if st.button("Analisis Portofolio"):
         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
@@ -65,16 +84,19 @@ if st.button("Analisis Portofolio"):
 
             if optimal_weights is not None:
                 st.subheader("Bobot Portofolio Optimal:")
-                for i, stock in enumerate(tickers_list):
+                for i, stock in enumerate(stock_data.columns):
                     st.write(f"{stock}: {optimal_weights[i]:.2%}")
 
-                # Plot Efficient Frontier
+                # Simulasi Efficient Frontier
+                results = generate_efficient_frontier(mean_returns, cov_matrix)
+
                 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')
+                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.")