Update app.py

app.py

@@ -77,8 +77,18 @@ def calculate_profit_loss(stock_data,days_to_monitor):
                 'Profit/Loss (%)': profit_loss_percent,
                 'No trigger': no_trigger
             })
+
+    dft = pd.DataFrame(buy_sell_actions)
+
+    dff = pd.DataFrame()
+    dff['+ve trade probability'] = len(dft[dft['Profit/Loss (%)'] > 0])
+    dff['Mean returns'] = dft['Profit/Loss (%)'].mean()
+    dff['Median returns'] =  dft['Profit/Loss (%)'].median()
+    dff['Best return'] =  dft['Profit/Loss (%)'].max()
+    dff['worst return'] =  dft['Profit/Loss (%)'].min()
+
     
-    return pd.DataFrame(buy_sell_actions)
+    return dft, dff
 
 # Example function to simulate loading and processing stock data
 def get_stock_data(stock_name,rsi_window,days_to_monitor,previous_n_days,rsi_threshold1,rsi_threshold2):
@@ -140,21 +150,21 @@ def get_stock_data(stock_name,rsi_window,days_to_monitor,previous_n_days,rsi_thr
     
     fstock = stock_data[stock_data['condition']==1].reset_index(drop=True)
 
-    profit_data = calculate_profit_loss(stock_data,days_to_monitor)
+    profit_data, summary_data = calculate_profit_loss(stock_data,days_to_monitor)
 
     # Returning two dataframes: One for the full stock data, another for RSI values
-    return fstock, profit_data
+    return fstock, profit_data,summary_data
 
 # Function to save CSV file and return its path
 def save_to_csv(stock_input,rsi_window_slider,days_to_monitor,previous_n_days,rsi_threshold1,rsi_threshold2):
     stock_name = stock_name.upper()
-    fstock, profit_data = get_stock_data(stock_name,rsi_window,days_to_monitor,previous_n_days,rsi_threshold1,rsi_threshold2)
+    fstock, profit_data,summary_data = get_stock_data(stock_name,rsi_window,days_to_monitor,previous_n_days,rsi_threshold1,rsi_threshold2)
     csv_file_path = f'{stock_name}.csv'
 
     # fstock['Date'] = pd.to_datetime(fstock['Date']).dt.date
     # profit_data['Date'] = pd.to_datetime(profit_data['Date']).dt.date
 
-    return fstock, profit_data, csv_file_path
+    return fstock, profit_data, summary_data, csv_file_path
 
 # Create the Gradio interface
 with gr.Blocks() as demo:
@@ -183,11 +193,12 @@ with gr.Blocks() as demo:
         with gr.Column():
             gr.Markdown("<h3 style='text-align: center;'>Output</h3>")
             output_stock_data = gr.DataFrame(label="Dates where Conditions met", interactive=False)
-            output_rsi_data = gr.DataFrame(label="Profit and Loss Statement", interactive=False)
+            output_pl_data = gr.DataFrame(label="Profit and Loss Statement", interactive=False)
+            output_summary_data = gr.DataFrame(label="Returns Summary", interactive=False)
             csv_download = gr.File(label="Download the full CSV")
 
     # When the button is clicked, show the two dataframes and provide a downloadable CSV
-    submit_button.click(save_to_csv, inputs=[stock_input,rsi_window_slider,days_to_monitor,previous_n_days,rsi_threshold1,rsi_threshold2], outputs=[output_stock_data, output_rsi_data, csv_download])
+    submit_button.click(save_to_csv, inputs=[stock_input,rsi_window_slider,days_to_monitor,previous_n_days,rsi_threshold1,rsi_threshold2], outputs=[output_stock_data, output_pl_data, output_summary_data,csv_download])
 
 # Launch the Gradio interface
 demo.launch()