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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)")
st.markdown("""
### Teori Markowitz
Model Markowitz, atau Modern Portfolio Theory (MPT), digunakan untuk membangun portofolio investasi optimal dengan memaksimalkan return untuk tingkat risiko tertentu.
Portofolio yang optimal ditemukan dengan menghitung kombinasi terbaik dari aset yang tersedia untuk meminimalkan risiko dan memaksimalkan return.
""")
def get_recommended_stocks():
return "KLBF.JK, SIDO.JK, KAEF.JK, TLKM.JK, UNVR.JK"
def validate_tickers(tickers):
invalid_tickers = [t for t in tickers if 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())
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 st.button("Analisis Portofolio"):
if validate_tickers(tickers_list):
stock_data = get_stock |