Update app.py

app.py

@@ -1,105 +1,85 @@
-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
+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
+
+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
+
+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
+
+def calculate_returns(data): log_returns = np.log(data / data.shift(1)) return log_returns.mean() * 252, log_returns.cov() * 252
+
+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  
+
+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
+
+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
+
 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"))
+def get_recommended_stocks(): return "KLBF.JK, SIDO.JK, KAEF.JK, TLKM.JK, UNVR.JK"  # Saham relevan saat pandemi
+
+def validate_tickers(tickers): invalid_tickers = [t for t in tickers if not yf.Ticker(t).history(period='1d').empty] if invalid_tickers: st.warning(f"Ticker tidak valid atau tidak memiliki data: {', '.join(invalid_tickers)}") return False return True
+
+st.write("Rekomendasi Saham yang Bertahan Saat COVID-19:") st.write(get_recommended_stocks())
 
-if st.button("Analisis Portofolio"):
-    try:
-        # Ambil data saham
-        stock_data = get_stock_data(tickers_list, start_date, end_date)
+tickers_list = st.text_input("Masukkan ticker saham", "KLBF.JK, SIDO.JK, KAEF.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 stock_data is not None:
-            st.write("Saham dengan data tersedia:", stock_data.columns)  # Debugging
+if st.button("Analisis Portofolio"): if validate_tickers(tickers_list): 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) optimal_weights = optimize_portfolio(mean_returns, cov_matrix)
+
+st.subheader("Statistik Saham")
+        st.write(stock_data.describe())
+        
+        if optimal_weights is not None:
+            st.subheader("Bobot Portofolio Optimal")
+            portfolio_weights = {stock: weight for stock, weight in zip(stock_data.columns, optimal_weights)}
+            st.write(portfolio_weights)
+            
+            fig, ax = plt.subplots()
+            ax.pie(optimal_weights, labels=stock_data.columns, autopct='%1.1f%%', startangle=140)
+            ax.axis('equal')
+            st.pyplot(fig)
             
-            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}")
+            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.")
+