File size: 3,304 Bytes
ba37076 5ec5ce2 ba37076 a857666 ba37076 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | import streamlit as st
import yfinance as yf
import pandas as pd
import numpy as np
import plotly.express as px
# Page configuration
st.set_page_config(page_title="Portfolio Optimization Dashboard", layout="wide")
# Main Title
st.title("📊 Markowitz Portfolio Optimization Dashboard")
st.markdown("An interactive application to analyze returns and risks for a portfolio of financial assets using real market data.")
st.title("Dr .medini Atmane university of Eloued Algeria")
# Sidebar for User Inputs
st.sidebar.header("Portfolio Settings")
tickers_input = st.sidebar.text_input("Enter Ticker Symbols (comma-separated)", "AAPL, MSFT, GOOGL")
start_date = st.sidebar.date_input("Start Date", pd.to_datetime("2023-01-01"))
end_date = st.sidebar.date_input("End Date", pd.to_datetime("today"))
# Execution Button
if st.sidebar.button("Analyze Data"):
# Clean and format the input tickers
tickers = [t.strip().upper() for t in tickers_input.split(",")]
with st.spinner('Fetching financial data and calculating returns...'):
try:
# Fetch adjusted closing prices from Yahoo Finance
data = yf.download(tickers, start=start_date, end=end_date)['Close']
# Verify if data is retrieved successfully
if data.empty:
st.error("No data found for these tickers. Please verify the symbols and try again.")
else:
# Calculate daily returns
returns = data.pct_change().dropna()
# 1. Plot Cumulative Returns
st.subheader("📈 Cumulative Returns Performance")
cumulative_returns = (1 + returns).cumprod()
st.line_chart(cumulative_returns)
# Create a two-column layout
col1, col2 = st.columns(2)
with col1:
# 2. Correlation Matrix
st.subheader("🔗 Correlation Matrix")
st.markdown("Analyzes how assets move together to ensure effective diversification.")
corr = returns.corr()
# Apply a heatmap style to the dataframe
st.dataframe(corr.style.background_gradient(cmap='Blues'), use_container_width=True)
with col2:
# 3. Annualized Return and Risk Assessment
st.subheader("⚖️ Expected Return & Annualized Risk")
# Assuming 252 trading days in a year
annual_returns = returns.mean() * 252
annual_volatility = returns.std() * np.sqrt(252)
# Compile the summary into a DataFrame
summary_df = pd.DataFrame({
"Expected Annual Return": annual_returns,
"Annualized Volatility (Risk)": annual_volatility
})
st.dataframe(summary_df.style.format("{:.2%}"), use_container_width=True)
except Exception as e:
st.error(f"An error occurred while processing the data: {e}")
else:
# Default instruction state
st.info("👈 Please enter the stock tickers in the sidebar and click 'Analyze Data' to begin.") |