import pandas as pd
from statsmodels.tsa.statespace.sarimax import SARIMAX
import numpy as np

defexecution_code(data:pd.DataFrame) -> pd.DataFrame:
    # Ensure correct types
    data['sales_month'] = pd.to_datetime(data['sales_month'])
    data['total_quantity_sold'] = pd.to_numeric(data['total_quantity_sold'])
    
    # Set index for time series
    ts = data.set_index('sales_month')['total_quantity_sold']
    
    # Fit SARIMA model (auto seasonal order for monthly data)# With only 10 data points, model complexity must be limited# We'll use (1,1,1)x(0,1,1,12) as a simple seasonal model
    model = SARIMAX(ts, order=(1,1,1), seasonal_order=(0,1,1,12), enforce_stationarity=False, enforce_invertibility=False)
    results = model.fit(disp=False)
    
    # Forecast for Oct, Nov, Dec 2023
    forecast_index = pd.date_range(start='2023-10-01', end='2023-12-01', freq='MS')
    n_periods = len(forecast_index)
    forecast = results.get_forecast(steps=n_periods)
    forecast_values = forecast.predicted_mean
    conf_int = forecast.conf_int()
    
    # Prepare result DataFrame
    result = pd.DataFrame({
        'forecast_month': forecast_index,
        'forecast_quantity': np.round(forecast_values.values, 2),
        'lower_ci': np.round(conf_int.iloc[:,0].values, 2),
        'upper_ci': np.round(conf_int.iloc[:,1].values, 2)
    })
    return result