Update app.py

app.py

@@ -19,9 +19,28 @@ def plot_cum_returns(data, title):
     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 with random weights
+    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, investment_amount):
+def output_results(start_date, end_date, tickers_string):
     tickers = tickers_string.split(',')
     
     # Get Stock Prices
@@ -34,15 +53,15 @@ def output_results(start_date, end_date, tickers_string, investment_amount):
     fig_cum_returns = plot_cum_returns(stocks_df, 'Cumulative Returns of Individual Stocks Starting with ₹100')
     
     # Calculate 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')
+    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)
+    fig_efficient_frontier = plot_efficient_frontier_and_max_sharpe(mu, S)
 
     # Get optimized weights
     ef = EfficientFrontier(mu, S)
@@ -51,21 +70,13 @@ def output_results(start_date, end_date, tickers_string, investment_amount):
     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))
+    '{}%'.format((annual_volatility*100).round(2)), \
+    '{}%'.format((sharpe_ratio*100).round(2))
     
-    weights_df = pd.DataFrame.from_dict(weights, orient='index').reset_index()
+    weights_df = pd.DataFrame.from_dict(weights, orient = 'index')
+    weights_df = weights_df.reset_index()
     weights_df.columns = ['Tickers', 'Weights']
 
-    # Get the latest prices
-    latest_prices = get_latest_prices(stocks_df)
-
-    # Allocate the stocks based on the given budget
-    da = DiscreteAllocation(weights, latest_prices, total_portfolio_value=investment_amount)
-    allocation, leftover = da.lp_portfolio()
-    
-    allocation_df = pd.DataFrame(list(allocation.items()), columns=['Ticker', 'Shares'])
-    
     # Calculate returns of portfolio with optimized weights
     stocks_df['Optimized Portfolio'] = 0
     for ticker, weight in weights.items():
@@ -74,8 +85,8 @@ def output_results(start_date, end_date, tickers_string, investment_amount):
     # 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, allocation_df,  \
-            expected_annual_return, leftover.round(), fig_indiv_prices, fig_cum_returns
+    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:
@@ -90,34 +101,30 @@ with gr.Blocks() as app:
         tickers_string = gr.Textbox("TCS.NS,INFY.NS,RELIANCE.NS,HDFCBANK.NS,ICICIBANK.NS,SBIN.NS", 
                                     label='Enter all stock tickers to be included in portfolio separated \
                                     by commas WITHOUT spaces, e.g. "TCS.NS,INFY.NS,RELIANCE.NS,HDFCBANK.NS,ICICIBANK.NS,SBIN.NS"')
-        investment_amount = gr.Number(label="Investment Amount (in ₹)")
         btn = gr.Button("Get Optimized Portfolio")
        
-    # with gr.Row():
-    #     gr.HTML("<h3>Optimized Portfolio Metrics</h3>")
+    with gr.Row():
+        gr.HTML("<h3>Optimized Portfolio Metrics</h3>")
         
     with gr.Row():
         expected_annual_return = gr.Text(label="Expected Annual Return")
-        leftover = gr.Number(label="Leftover Amount (in ₹)")            
+        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)")
-        allocation_df = gr.DataFrame(label="Stock Allocation")
+        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_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")
-        
-    # with gr.Row():
-    #     allocation_df = gr.DataFrame(label="Stock Allocation")
-        #leftover = gr.Number(label="Leftover Amount (in ₹)")
 
-    btn.click(fn=output_results, inputs=[start_date, end_date, tickers_string, investment_amount], 
-              outputs=[fig_cum_returns_optimized, allocation_df, \
-                       expected_annual_return,leftover, fig_indiv_prices, fig_cum_returns])
+    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()