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Update app.py
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app.py
CHANGED
@@ -84,4 +84,44 @@ end_date = st.date_input("Pilih tanggal akhir", pd.to_datetime("2023-12-31"))
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if st.button("Analisis Portofolio"):
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if validate_tickers(tickers_list):
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stock_data =
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if st.button("Analisis Portofolio"):
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if validate_tickers(tickers_list):
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stock_data = get_stock_data(tickers_list, start_date, end_date)
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if stock_data is not None:
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mean_returns, cov_matrix = calculate_returns(stock_data)
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optimal_weights = optimize_portfolio(mean_returns, cov_matrix)
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st.subheader("Statistik Saham")
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st.write(stock_data.describe())
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if optimal_weights is not None:
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st.subheader("Bobot Portofolio Optimal")
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portfolio_weights = {stock: weight for stock, weight in zip(stock_data.columns, optimal_weights)}
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st.write(portfolio_weights)
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# Pie Chart dengan filter saham dengan bobot signifikan
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filtered_weights = {k: v for k, v in portfolio_weights.items() if v > 0.01}
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fig, ax = plt.subplots()
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ax.pie(filtered_weights.values(), labels=filtered_weights.keys(), autopct='%1.1f%%', startangle=140)
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ax.axis('equal')
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st.pyplot(fig)
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# Efficient Frontier
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results = generate_efficient_frontier(mean_returns, cov_matrix)
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st.subheader("Efficient Frontier")
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fig, ax = plt.subplots()
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scatter = ax.scatter(results[1, :], results[0, :], c=results[2, :], cmap="viridis", marker='o')
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ax.set_xlabel("Risiko (Standar Deviasi)")
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ax.set_ylabel("Return Tahunan")
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ax.set_title("Efficient Frontier")
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fig.colorbar(scatter, label="Sharpe Ratio")
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st.pyplot(fig)
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st.markdown("""
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**Interpretasi:**
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- Bobot dalam portofolio menunjukkan proporsi investasi pada masing-masing saham.
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- Semakin besar bobot, semakin besar porsi dana yang dialokasikan ke saham tersebut.
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- Grafik Efficient Frontier menunjukkan hubungan antara risiko dan return dari berbagai kombinasi portofolio.
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- Portofolio yang berada di frontier efisien memberikan return terbaik untuk tingkat risiko tertentu.
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""")
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else:
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st.error("Optimasi portofolio gagal. Coba dengan saham yang berbeda.")
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