Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import yfinance as yf
+
+# The efficient frontier function modified for use in Streamlit
+def plot_efficient_frontier(dataframes, names):
+    # Your efficient frontier function with slight modifications for Streamlit
+    # This is the same function you've been working with, so ensure it's correctly adjusted for Streamlit output
+    
+    # Ensure you include the code for the function here
+    # Calculate daily returns for each asset
+    returns = pd.concat([df['Close'].pct_change().dropna() for df in dataframes], axis=1)
+    returns.columns = names
+
+    # Calculate mean and standard deviation of daily returns for each asset
+    mean_returns = returns.mean() * 252  # Annualize the mean returns
+    cov_matrix = returns.cov() * 252  # Annualize the covariance matrix
+
+    num_portfolios = 10000
+    all_weights = np.zeros((num_portfolios, len(dataframes)))
+    ret_arr = np.zeros(num_portfolios)
+    vol_arr = np.zeros(num_portfolios)
+    sharpe_arr = np.zeros(num_portfolios)
+
+    for i in range(num_portfolios):
+        weights = np.random.random(len(dataframes))
+        weights /= np.sum(weights)  # Normalize weights
+        all_weights[i, :] = weights
+
+        # Expected portfolio return
+        ret_arr[i] = np.dot(weights, mean_returns)
+
+        # Expected portfolio volatility
+        vol_arr[i] = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))
+
+        # Sharpe Ratio, assuming risk-free rate is 0
+        sharpe_arr[i] = ret_arr[i] / vol_arr[i]
+
+    # Find the portfolio with the highest Sharpe ratio
+    max_sharpe_idx = np.argmax(sharpe_arr)
+    max_sharpe_return = ret_arr[max_sharpe_idx]
+    max_sharpe_volatility = vol_arr[max_sharpe_idx]
+    optimal_weights = all_weights[max_sharpe_idx]
+
+    # Find the portfolio with the minimum volatility
+    min_vol_idx = np.argmin(vol_arr)
+    min_vol_return = ret_arr[min_vol_idx]
+    min_vol_volatility = vol_arr[min_vol_idx]
+
+    # Print the portfolio weights for the optimal portfolio
+    print("Optimal Portfolio Weights:")
+    for i, name in enumerate(names):
+        print(f"{name}: {optimal_weights[i]:.4f}")
+
+    # Plotting the efficient frontier
+    plt.figure(figsize=(10,6))
+    scatter = plt.scatter(vol_arr, ret_arr, c=sharpe_arr, cmap='Blues')
+    plt.colorbar(scatter, label='Sharpe Ratio')
+    plt.xlabel('Volatility')
+    plt.ylabel('Return')
+    plt.title('Efficient Frontier for a Portfolio of ' + str(len(names)) + ' Assets')
+
+    # Highlight the optimal portfolio
+    plt.scatter(max_sharpe_volatility, max_sharpe_return, c='red', s=200, edgecolors='black', marker='*', label='Optimal Portfolio (Max Sharpe Ratio)')
+
+    # Highlight the minimum volatility portfolio
+    plt.scatter(min_vol_volatility, min_vol_return, c='purple', s=150, edgecolors='black', marker='o', label='Minimum Volatility Portfolio')
+
+    # Set axes starting points
+    plt.xlim(0, plt.xlim()[1])
+    plt.ylim(0, plt.ylim()[1])
+
+    plt.legend()
+
+    plt.show()
+
+# Streamlit application layout
+st.title("Portfolio Optimization with Efficient Frontier")
+
+# User input for tickers
+user_input = st.text_input("Enter tickers separated by commas (e.g., AAPL,MSFT,GOOGL)", "NVDA,AMD,AAPL,MSFT,GOOGL")
+
+# Process input tickers
+tickers = [ticker.strip().upper() for ticker in user_input.split(',')]
+
+# Fetch data and calculate efficient frontier upon button click
+if st.button("Optimize Portfolio"):
+    # Fetch stock data
+    timeframe = '1y'  # Define the timeframe for data fetching
+    dataframes = []
+    for ticker in tickers:
+        tick = yf.Ticker(ticker)
+        stock_data = tick.history(period=timeframe)
+        dataframes.append(stock_data)
+    
+    # Ensure we have the data before proceeding
+    if len(dataframes) > 0:
+        # Plot efficient frontier and display optimal weights
+        plot_efficient_frontier(dataframes, tickers)
+    else:
+        st.write("Please enter valid tickers.")
+
+# Instructions to run the app