'first'

app.py

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+import gradio as gr
+import yfinance as yf
+from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices
+from pypfopt import EfficientFrontier
+from pypfopt import risk_models
+from pypfopt import expected_returns
+from pypfopt import plotting
+import copy
+import numpy as np
+import pandas as pd
+import plotly.express as px
+import matplotlib.pyplot as plt
+import seaborn as sns
+from datetime import datetime
+from io import BytesIO
+import datetime
+
+def plot_cum_returns(data, title):    
+    daily_cum_returns = 1 + data.dropna().pct_change()
+    daily_cum_returns = daily_cum_returns.cumprod()*100
+    fig = px.line(daily_cum_returns, title=title)
+    return fig
+
+def plot_efficient_frontier_and_max_sharpe(mu, S): 
+    # Optimize portfolio for max Sharpe ratio and plot it out with efficient frontier curve
+    ef = EfficientFrontier(mu, S)
+    fig, ax = plt.subplots(figsize=(6,4))
+    ef_max_sharpe = copy.deepcopy(ef)
+    plotting.plot_efficient_frontier(ef, ax=ax, show_assets=False)
+    # Find the max sharpe portfolio
+    ef_max_sharpe.max_sharpe(risk_free_rate=0.02)
+    ret_tangent, std_tangent, _ = ef_max_sharpe.portfolio_performance()
+    ax.scatter(std_tangent, ret_tangent, marker="*", s=100, c="r", label="Max Sharpe")
+    # Generate random portfolios
+    n_samples = 1000
+    w = np.random.dirichlet(np.ones(ef.n_assets), n_samples)
+    rets = w.dot(ef.expected_returns)
+    stds = np.sqrt(np.diag(w @ ef.cov_matrix @ w.T))
+    sharpes = rets / stds
+    ax.scatter(stds, rets, marker=".", c=sharpes, cmap="viridis_r")
+    # Output
+    ax.legend()
+    return fig
+
+def output_results(start_date, end_date, tickers_string):
+    tickers = tickers_string.split(',')
+    
+    # Get Stock Prices
+    stocks_df = yf.download(tickers, start=start_date, end=end_date)['Adj Close']
+    
+    # Plot Individual Stock Prices
+    fig_indiv_prices = px.line(stocks_df, title='Price of Individual Stocks')
+        
+    # Plot Individual Cumulative Returns
+    fig_cum_returns = plot_cum_returns(stocks_df, 'Cumulative Returns of Individual Stocks Starting with $100')
+    
+    # Calculatge and Plot Correlation Matrix between Stocks
+    corr_df = stocks_df.corr().round(2)
+    fig_corr = px.imshow(corr_df, text_auto=True, title = 'Correlation between Stocks')
+
+    # Calculate expected returns and sample covariance matrix for portfolio optimization later
+    mu = expected_returns.mean_historical_return(stocks_df)
+    S = risk_models.sample_cov(stocks_df)
+
+    # Plot efficient frontier curve
+    fig_efficient_frontier = plot_efficient_frontier_and_max_sharpe(mu, S)
+
+    # Get optimized weights
+    ef = EfficientFrontier(mu, S)
+    ef.max_sharpe(risk_free_rate=0.02)
+    weights = ef.clean_weights()
+    expected_annual_return, annual_volatility, sharpe_ratio = ef.portfolio_performance()
+    
+    expected_annual_return, annual_volatility, sharpe_ratio = '{}%'.format((expected_annual_return*100).round(2)), \
+    '{}%'.format((annual_volatility*100).round(2)), \
+    '{}%'.format((sharpe_ratio*100).round(2))
+    
+    weights_df = pd.DataFrame.from_dict(weights, orient = 'index')
+    weights_df = weights_df.reset_index()
+    weights_df.columns = ['Tickers', 'Weights']
+
+    # Calculate returns of portfolio with optimized weights
+    stocks_df['Optimized Portfolio'] = 0
+    for ticker, weight in weights.items():
+        stocks_df['Optimized Portfolio'] += stocks_df[ticker]*weight
+
+    # Plot Cumulative Returns of Optimized Portfolio
+    fig_cum_returns_optimized = plot_cum_returns(stocks_df['Optimized Portfolio'], 'Cumulative Returns of Optimized Portfolio Starting with $100')
+
+    return  fig_cum_returns_optimized, weights_df, fig_efficient_frontier, fig_corr,   \
+            expected_annual_return, annual_volatility, sharpe_ratio, fig_indiv_prices, fig_cum_returns
+
+
+with gr.Blocks() as app:
+    with gr.Row():
+        start_date = gr.Textbox("2013-01-01", label="Start Date")
+        end_date = gr.Textbox(datetime.datetime.now().date(), label="End Date")
+    
+    with gr.Row():        
+        tickers_string = gr.Textbox("MA,META,V,AMZN,JPM,BA", 
+                                    label='Enter all stock tickers to be included in portfolio separated \
+                                    by commas WITHOUT spaces, e.g. "MA,META,V,AMZN,JPM,BA"')
+        btn = gr.Button("View Optimized Portfolio")
+       
+    with gr.Row():
+        gr.Markdown("Optimzied Portfolio Metrics")
+    with gr.Row():
+        expected_annual_return = gr.Text(label="Expected Annual Return")
+        annual_volatility = gr.Text(label="Annual Volatility")
+        sharpe_ratio = gr.Text(label="Sharpe Ratio")            
+   
+    with gr.Row():        
+        fig_cum_returns_optimized = gr.Plot(label="Cumulative Returns of Optimized Portfolio (Starting Price of $100)")
+        weights_df = gr.DataFrame(label="Optimized Weights of Each Ticker")
+        
+    with gr.Row():
+        fig_efficient_frontier = gr.Plot(label="Efficient Frontier")
+        fig_corr = gr.Plot(label="Correlation between Stocks")
+    
+
+    with gr.Row():
+        fig_indiv_prices = gr.Plot(label="Price of Individual Stocks")
+        fig_cum_returns = gr.Plot(label="Cumulative Returns of Individual Stocks Starting with $100")
+
+    #demo.load(plot_indiv_stock_prices, inputs=[start_date, end_date, tickers_string], outputs=fig_indiv_price, examples=["My name is Andrew, I'm building DeeplearningAI and I live in California", "My name is Poli, I live in Vienna and work at HuggingFace"])
+    btn.click(fn=output_results, inputs=[start_date, end_date, tickers_string], 
+              outputs=[fig_cum_returns_optimized, weights_df, fig_efficient_frontier, fig_corr,   \
+            expected_annual_return, annual_volatility, sharpe_ratio, fig_indiv_prices, fig_cum_returns])
+
+app.launch(server_port=7888)

requirements.txt

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+pandas
+PyPortfolioOpt
+plotly
+matplotlib
+seaborn
+yfinance