Update app.py

app.py

@@ -1,115 +1,54 @@
 import streamlit as st
-import yfinance as yf
-import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
-import seaborn as sns
-import scipy.optimize as sco
-import plotly.express as px
-import plotly.graph_objects as go
-from fpdf import FPDF
 
-def get_stock_data(tickers, start, end):
-    data = yf.download(tickers, start=start, end=end)
+def main():
+    st.title("Financial Budget Planner 💰")
+    st.write("Kelola keuanganmu dengan lebih baik!")
     
-    if data.empty:
-        st.error("Data saham tidak ditemukan. Periksa ticker atau rentang tanggal.")
-        return None
+    # Input pemasukan
+    income = st.number_input("Masukkan total pemasukan bulanan (Rp)", min_value=0, step=100000)
     
-    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)
+    # Input pengeluaran
+    st.subheader("Pengeluaran Bulanan")
+    categories = ["Makanan", "Transportasi", "Hiburan", "Tagihan", "Tabungan", "Lainnya"]
+    expenses = {}
+    total_expense = 0
     
-    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  
+    for category in categories:
+        expenses[category] = st.number_input(f"{category} (Rp)", min_value=0, step=50000)
+        total_expense += expenses[category]
     
-    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]
+    # Sisa saldo
+    remaining_balance = income - total_expense
+    st.subheader("Ringkasan Keuangan")
+    st.write(f"Total Pengeluaran: Rp {total_expense:,}")
+    st.write(f"Sisa Saldo: Rp {remaining_balance:,}")
     
-    result = sco.minimize(sharpe_ratio, init_guess, method='SLSQP', bounds=bounds, constraints=constraints)
-    return result.x if result.success else None
-
-def simulate_monte_carlo(returns, cov_matrix, num_simulations=10000, time_horizon=252):
-    num_assets = len(returns)
-    simulated_returns = np.zeros(num_simulations)
-    simulated_volatility = np.zeros(num_simulations)
+    # Visualisasi data
+    if total_expense > 0:
+        df = pd.DataFrame(expenses.items(), columns=["Kategori", "Jumlah"])
+        fig, ax = plt.subplots()
+        ax.pie(df["Jumlah"], labels=df["Kategori"], autopct='%1.1f%%', startangle=90, colors=["#ff9999", "#66b3ff", "#99ff99", "#ffcc99", "#c2c2f0", "#ffb3e6"])
+        ax.axis('equal')
+        st.pyplot(fig)
     
-    for i in range(num_simulations):
-        weights = np.random.dirichlet(np.ones(num_assets), size=1)[0]
-        simulated_returns[i] = np.dot(weights, returns)
-        simulated_volatility[i] = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))
+    # Rekomendasi tabungan
+    if income > 0:
+        save_percentage = 20 if income > 5000000 else 10
+        recommended_savings = income * save_percentage / 100
+        st.write(f"💡 Rekomendasi: Sisihkan sekitar **{save_percentage}%** dari pemasukan untuk tabungan: Rp {recommended_savings:,}")
     
-    fig = px.scatter(x=simulated_volatility, y=simulated_returns, color=simulated_returns/simulated_volatility,
-                     labels={'x': 'Volatilitas', 'y': 'Return'},
-                     title='Simulasi Monte Carlo')
-    st.plotly_chart(fig)
-
-def stock_screener():
-    st.sidebar.header("Stock Screener")
-    pe_ratio = st.sidebar.slider("Maksimum P/E Ratio", 0, 100, 20)
-    market_cap = st.sidebar.selectbox("Market Cap", ["Small", "Mid", "Large"])
+    # Notifikasi jika over budget
+    if remaining_balance < 0:
+        st.warning("⚠️ Pengeluaran lebih besar dari pemasukan! Pertimbangkan untuk mengurangi pengeluaran.")
     
-    # Simulasi data screener (contoh)
-    screener_data = pd.DataFrame({
-        'Ticker': ['AAPL', 'MSFT', 'GOOGL', 'TSLA', 'AMZN'],
-        'P/E Ratio': [30, 25, 35, 40, 28],
-        'Market Cap': ['Large', 'Large', 'Large', 'Mid', 'Large']
-    })
-    filtered_stocks = screener_data[(screener_data['P/E Ratio'] <= pe_ratio) & (screener_data['Market Cap'] == market_cap)]
-    st.sidebar.write("Saham yang memenuhi kriteria:")
-    st.sidebar.write(filtered_stocks)
-
-def export_to_pdf(stock_data):
-    pdf = FPDF()
-    pdf.add_page()
-    pdf.set_font("Arial", size=12)
-    pdf.cell(200, 10, txt="Laporan Analisis Saham", ln=True, align='C')
-    pdf.ln(10)
-    for ticker in stock_data.columns:
-        pdf.cell(200, 10, txt=f"{ticker}: {stock_data[ticker].iloc[-1]}", ln=True)
-    pdf.output("stock_report.pdf")
-    st.success("Laporan PDF telah dibuat!")
-
-st.title("Analisis Portofolio Saham Optimal (Model Markowitz)")
-
-st.write("Rekomendasi Saham yang Bertahan Saat COVID-19:")
-st.write("KLBF.JK, SIDO.JK, KAEF.JK, TLKM.JK, UNVR.JK")
-
-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"))
-stock_screener()
+if __name__ == "__main__":
+    main()
 
-if st.button("Analisis Portofolio"):
-    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)
+# Requirements:
+# streamlit
+# pandas
+# matplotlib
 
-        st.subheader("Simulasi Monte Carlo")
-        simulate_monte_carlo(mean_returns, cov_matrix)
 
-        if st.button("Export to PDF"):
-            export_to_pdf(stock_data)
-            
-        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)
-        else:
-            st.error("Optimasi portofolio gagal. Coba dengan saham yang berbeda.")