Update app.py

app.py

@@ -6,7 +6,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 import os
 
-from processes import data_option
+from processes import *
 # Función para obtener los datos y mostrarlos en el DataFrame de la UI
 
 def generar_tabla(option_type, ticker_input, expiration_date, size_of_the_window):
@@ -29,7 +29,68 @@ def generar_tabla(option_type, ticker_input, expiration_date, size_of_the_window
         return df_final
     else:
         return pd.DataFrame()
-        
+
+
+def parse_opciones_table(df):
+    """
+    Limpia y convierte un DataFrame extraído de una tabla decorada de opciones.
+    Filtra filas válidas, convierte tipos y normaliza valores booleanos.
+    """
+    # Filtrar filas que contienen símbolos válidos de opciones (ej. GOOGL251219P00165000)
+    df_clean = df[df['contractSymbol'].str.contains(r'^[A-Z]{4,6}\d{6}[PC]\d{8}$', regex=True)].copy()
+
+    # Convertir columnas relevantes a tipos numéricos
+    df_clean['strike'] = pd.to_numeric(df_clean['strike'], errors='coerce')
+    df_clean['volume'] = pd.to_numeric(df_clean['volume'], errors='coerce')
+    df_clean['lastPrice'] = pd.to_numeric(df_clean['lastPrice'], errors='coerce')
+    df_clean['impliedVolatility'] = df_clean['impliedVolatility'].astype(str).str.replace('%', '').astype(float) / 100
+
+    # Normalizar columna inTheMoney
+    #df_clean['inTheMoney'] = df_clean['inTheMoney'].astype(str).str.upper().map({'VERDADERO': True, 'FALSO': False})
+
+    # Eliminar filas con valores nulos en columnas clave
+    df_clean = df_clean.dropna(subset=['strike', 'volume', 'lastPrice', 'impliedVolatility'])
+
+    return df_clean
+
+def plot_opciones(df_put, df_call, spot_price):
+    
+    puts_itm = df_put[df_put['strike'] > spot_price]
+    puts_otm = df_put[df_put['strike'] <= spot_price]
+    calls_itm = df_call[df_call['strike'] < spot_price]
+    calls_otm = df_call[df_call['strike'] >= spot_price]
+
+    fig = go.Figure()
+    fig.add_trace(go.Bar(x=puts_itm['strike'], y=puts_itm['volume'], name='PUT ITM', marker_color='green', width=0.3))
+    fig.add_trace(go.Bar(x=puts_otm['strike'], y=puts_otm['volume'], name='PUT OTM', marker_color='red',width=0.3))
+    fig.add_trace(go.Bar(x=calls_itm['strike'], y=calls_itm['volume'], name='CALL ITM', marker_color='blue',width=0.3))
+    fig.add_trace(go.Bar(x=calls_otm['strike'], y=calls_otm['volume'], name='CALL OTM', marker_color='orange',width=0.3))
+    fig.add_trace(go.Scatter(x=[spot_price], y=[0], mode='markers+text',
+                             marker=dict(color='black', size=10),
+                             text=[f'Spot: {spot_price}'],
+                             textposition='top center',
+                             name='Spot Price'))
+        # Agrega la línea vertical discontinua
+    fig.add_trace(go.Scatter(
+        x=[spot_price, spot_price],
+        y=[0, max(max(puts_itm['volume']), max(puts_otm['volume']), max(calls_itm['volume']), max(calls_otm['volume'])) * 1.1],
+        mode='lines',
+        name=f'Spot: {spot_price}',
+        marker=dict(color='black', size=10),
+        line=dict(dash='dash'),
+        showlegend=False
+    ))
+
+
+    fig.update_layout(title='Distribución de Volumen de Opciones PUT y CALL',
+                      xaxis_title='Strike',
+                      yaxis_title='Volumen',
+                      barmode='stack',
+                      template='plotly_white')
+    fig.show()
+
+
+
 def generar_todo(ticker_input, expiration_date, size_of_the_window):
     ticker_input = ticker_input
     puts = generar_tabla("put", ticker_input, expiration_date, size_of_the_window)
@@ -128,7 +189,17 @@ with gr.Blocks(title="Análisis de Opciones") as demo:
             outputs=[output_file, output_msg]
         )
 
-    #with gr.Tab("Analizador"):
+    with gr.Tab("Analizador"):
+
+        output_plot = gr.Plot(label="Distribución de Volumen de las opciones")
+        
+        
+        gr.on(
+            triggers=None,  # Esto hace que se ejecute al cargar
+            fn=plot_opciones,
+            inputs=[parse_opciones_table(output_df_put,output_df_plot, price],
+            outputs=output_plot
+        )