import pandas as pd import plotly.graph_objects as go from plotly.subplots import make_subplots import time import signal import sys PATH_UBUNTU = "/var/www/html/flask_project/" def signal_handler(sig, frame): print("Interrupción recibida, deteniendo...") sys.exit(0) signal.signal(signal.SIGINT, signal_handler) while True: # leyebdo el archivo del server df = pd.read_csv(PATH_UBUNTU + 'optionChain_2024-06-25.csv') df['timestamp'] = pd.to_datetime(df['timestamp'], format='%m/%d/%Y %H:%M:%S') # strikes para iron condors strikes_list_iron_butterfly = [ # [5430.0, 5405.0, 5430.0, 5380.0, 'IRON BUTTERFLY'], # [5510.0, 5520.0, 5460.0, 5450.0, 'IRON CONDOR'], # [5485.0, 5490.0, 5440.0, 5435.0, 'SONAR'], ] # Strikes para vertical(call o put) strikes_list_vertical = [ [5445.0, 5440.0, 'PUT', 'VERTICAL'], # [5515.0, 5520.0, 'CALL', 'VERTICAL'], ] start_time = '06/25/2024 10:30:00' start_time = pd.to_datetime(start_time) # Filtrando datos!!! df = df[df['timestamp'] >= start_time] # Comprobar si después del filtrado hay datos if df.empty: print("No hay datos disponibles después del filtrado por start_time.") else: print(f"{len(df)} registros después del filtrado por start_time.") # Crear un DataFrame para almacenar los resultados de todas las configuraciones result_df = pd.DataFrame() verticals_df = pd.DataFrame() # Función para calcular valores de Iron Butterfly def calcular_iron_butterfly(strikes, label, df): filtered_df = df[df['strike'].isin(strikes)] sell_call_df = filtered_df[filtered_df['strike'] == strikes[0]].copy() buy_call_df = filtered_df[filtered_df['strike'] == strikes[1]].copy() sell_put_df = filtered_df[filtered_df['strike'] == strikes[2]].copy() buy_put_df = filtered_df[filtered_df['strike'] == strikes[3]].copy() sell_call_df.loc[:, 'mean_sell_call'] = 100 * (sell_call_df['bid_call'] + sell_call_df['ask_call']) / 2 sell_put_df.loc[:, 'mean_sell_put'] = 100 * (sell_put_df['bid_put'] + sell_put_df['ask_put']) / 2 buy_call_df.loc[:, 'mean_buy_call'] = 100 * (buy_call_df['bid_call'] + buy_call_df['ask_call']) / 2 buy_put_df.loc[:, 'mean_buy_put'] = 100 * (buy_put_df['bid_put'] + buy_put_df['ask_put']) / 2 iron_butterfly_values = pd.DataFrame() iron_butterfly_values['timestamp'] = sell_call_df['timestamp'] iron_butterfly_values['mean_sell_call'] = sell_call_df['mean_sell_call'].values iron_butterfly_values['mean_sell_put'] = sell_put_df['mean_sell_put'].values iron_butterfly_values['mean_buy_call'] = buy_call_df['mean_buy_call'].values iron_butterfly_values['mean_buy_put'] = buy_put_df['mean_buy_put'].values iron_butterfly_values['iron_butterfly_value'] = ( iron_butterfly_values['mean_sell_call'] + iron_butterfly_values['mean_sell_put'] - iron_butterfly_values['mean_buy_call'] - iron_butterfly_values['mean_buy_put'] ) credito_inicial = iron_butterfly_values['iron_butterfly_value'].iloc[0] iron_butterfly_values['diferencia'] = credito_inicial - iron_butterfly_values['iron_butterfly_value'] iron_butterfly_values['label'] = f'{label} [{strikes[0]}, {strikes[1]}] [{strikes[2]}, {strikes[3]}]' return iron_butterfly_values # Función para calcular valores de Verticales def calcular_vertical(strikes, label, df): option_type = strikes[2] filtered_df = df[df['strike'].isin(strikes[:2])] if option_type == 'CALL': sell_df = filtered_df[filtered_df['strike'] == strikes[0]].copy() buy_df = filtered_df[filtered_df['strike'] == strikes[1]].copy() sell_df.loc[:, 'mean_sell'] = 100 * (sell_df['bid_call'] + sell_df['ask_call']) / 2 buy_df.loc[:, 'mean_buy'] = 100 * (buy_df['bid_call'] + buy_df['ask_call']) / 2 elif option_type == 'PUT': sell_df = filtered_df[filtered_df['strike'] == strikes[0]].copy() buy_df = filtered_df[filtered_df['strike'] == strikes[1]].copy() sell_df.loc[:, 'mean_sell'] = 100 * (sell_df['bid_put'] + sell_df['ask_put']) / 2 buy_df.loc[:, 'mean_buy'] = 100 * (buy_df['bid_put'] + buy_df['ask_put']) / 2 vertical_values = pd.DataFrame() vertical_values['timestamp'] = sell_df['timestamp'] vertical_values['mean_sell'] = sell_df['mean_sell'].values vertical_values['mean_buy'] = buy_df['mean_buy'].values vertical_values['vertical_value'] = vertical_values['mean_sell'] - vertical_values['mean_buy'] credito_inicial = vertical_values['vertical_value'].iloc[0] vertical_values['diferencia'] = credito_inicial - vertical_values['vertical_value'] vertical_values['label'] = f'{label} [{strikes[0]}, {strikes[1]}] {strikes[2]}' # Agregar las columnas sell_strike y buy_strike vertical_values['sell_strike'] = strikes[0] vertical_values['buy_strike'] = strikes[1] return vertical_values # Calcular valores de Iron Butterfly for strikes in strikes_list_iron_butterfly: result_df = pd.concat([result_df, calcular_iron_butterfly(strikes[:4], strikes[4], df)], ignore_index=True) # Calcular valores de Verticales for strikes in strikes_list_vertical: vertical_result = calcular_vertical(strikes[:3], strikes[3], df) result_df = pd.concat([result_df, vertical_result], ignore_index=True) verticals_df = pd.concat([verticals_df, vertical_result[['timestamp', 'sell_strike', 'buy_strike', 'mean_sell', 'mean_buy', 'vertical_value', 'diferencia']]], ignore_index=True) # Guardar los datos de las verticales en un CSV verticals_df.to_csv('/home/seba/projects/VPS_Streaming_CS/verticals_result.csv', index=False) # Verificar que result_df no esté vacío y que contenga la columna 'timestamp' if not result_df.empty and 'timestamp' in result_df.columns: # Crear el gráfico de líneas con dos ejes Y fig = make_subplots(specs=[[{"secondary_y": True}]]) # Agregar las líneas de diferencias del valor de Iron Butterfly y Verticales for label in result_df['label'].unique(): df_label = result_df[result_df['label'] == label] fig.add_trace( go.Scatter(x=df_label['timestamp'], y=df_label['diferencia'], mode='lines', name=label, line=dict(width=1)), secondary_y=False ) # Agregar la línea para 'underlying_price' en color negro continuo y ancho de 3 fig.add_trace( go.Scatter( x=df['timestamp'], y=df['underlying_price'], mode='lines', name='Underlying Price', line=dict(color='black', width=1) ), secondary_y=True ) # Actualizar los títulos de los ejes fig.update_xaxes(title_text='Timestamp') fig.update_yaxes(title_text='Profit & Loss ($)', secondary_y=False) fig.update_yaxes(title_text='Underlying Price', secondary_y=True) # Actualizar el título del gráfico start_time_str = start_time.strftime('%m/%d/%Y %H:%M:%S') fig.update_layout(title_text='Seguimiento Estrategias Bola 8 - ' + start_time_str) # Mostrar el gráfico fig.show() else: print("No hay datos disponibles para mostrar el gráfico.") # Esperar 10 segundos antes de la siguiente iteración time.sleep(10)