Update app.py

app.py

@@ -81,7 +81,7 @@ def calculate_profit_loss(stock_data,days_to_monitor):
     return pd.DataFrame(buy_sell_actions)
 
 # Example function to simulate loading and processing stock data
-def get_stock_data(stock_name,rsi_window,days_to_monitor,previous_n_days,):
+def get_stock_data(stock_name,rsi_window,days_to_monitor,previous_n_days,rsi_threshold1,rsi_threshold2):
     stock_name = stock_name.upper()
 
     get_data(stock_name)
@@ -133,9 +133,9 @@ def get_stock_data(stock_name,rsi_window,days_to_monitor,previous_n_days,):
 
     ## Applying the condition
     for i in range(previous_n_days, len(stock_data)):
-        prev_30_days_rsi = stock_data['Daily RSI'][i-previous_n_days:i]
+        prev_n_days_rsi = stock_data['Daily RSI'][i-previous_n_days:i]
         
-        if all(prev_30_days_rsi < 65) and 65 <= stock_data['Daily RSI'].iloc[i] <= 70:
+        if all(prev_n_days_rsi < rsi_threshold1) and rsi_threshold2[0] <= stock_data['Daily RSI'].iloc[i] <= rsi_threshold2[1]:
             stock_data.at[i, 'condition'] = 1
     
     fstock = stock_data[stock_data['condition']==1].reset_index(drop=True)
@@ -146,9 +146,9 @@ def get_stock_data(stock_name,rsi_window,days_to_monitor,previous_n_days,):
     return fstock, profit_data
 
 # Function to save CSV file and return its path
-def save_to_csv(stock_name,rsi_window):
+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)
+    fstock, profit_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
@@ -172,7 +172,11 @@ with gr.Blocks() as demo:
             days_to_monitor = gr.Slider(minimum=1, maximum=30, value=8, label="Days to monitor stock once condition is met", step=1)
             previous_n_days = gr.Slider(minimum=1, maximum=180, value=30, label="Previous n days RSI threshold to check", step=1)
             rsi_threshold1 = gr.Slider(minimum=1, maximum=100, value=65, label="Previous RSI threshold", step=1)
-            rsi_threshold12= RangeSlider(label="Current RSI Range", minimum=0, maximum=100, value=[65, 70])
+            rsi_threshold2 = RangeSlider(label="Current RSI Range", minimum=0, maximum=100, value=[65, 70])
+            range_ = gr.Markdown(value=text.format(min=0, max=100))
+            rsi_threshold2.change(lambda s: text.format(min=s[0], max=s[1]), range_slider, range_,
+                                show_progress="hide", trigger_mode="always_last")
+            
             submit_button = gr.Button("Submit", variant="primary")
         
         with gr.Column():
@@ -182,7 +186,7 @@ with gr.Blocks() as demo:
             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], 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_rsi_data, csv_download])
 
 # Launch the Gradio interface
 demo.launch()