Update app.py

app.py

@@ -19,33 +19,14 @@ 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):
+
+def output_results(start_date, end_date, tickers_string, investment_amount):
     tickers = tickers_string.split(',')
     
     # Get Stock Prices
     stocks_df = yf.download(tickers, start=start_date, end=end_date)['Adj Close']
-    
+    company_names = stocks_df['Adj Close'].columns.tolist()
     # Plot Individual Stock Prices
     fig_indiv_prices = px.line(stocks_df, title='Price of Individual Stocks')
         
@@ -53,15 +34,15 @@ def output_results(start_date, end_date, tickers_string):
     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)
@@ -70,13 +51,21 @@ def output_results(start_date, end_date, tickers_string):
     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')
-    weights_df = weights_df.reset_index()
+    weights_df = pd.DataFrame.from_dict(weights, orient='index').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():
@@ -85,8 +74,8 @@ def output_results(start_date, end_date, tickers_string):
     # 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
+    return  fig_cum_returns_optimized, allocation_df,  \
+            expected_annual_return, leftover.round(), fig_indiv_prices, fig_cum_returns
 
 
 with gr.Blocks() as app:
@@ -101,30 +90,34 @@ 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")
-        annual_volatility = gr.Text(label="Annual Volatility")
-        sharpe_ratio = gr.Text(label="Sharpe Ratio")            
+        leftover = gr.Number(label="Leftover Amount (in ₹)")            
    
     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")
+        allocation_df = gr.DataFrame(label="Stock Allocation")
         
-    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], 
-              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])
+    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])
 
 app.launch()