For complex scheduling tasks use Airflow, features are

1. Scheduling: Uses cron-style scheduling
2. Error Handling: Built-in retries and failure callbacks
3. Dependencies: Clear task dependencies
4. Modularity: Separates different concerns into tasks
5. Configuration: Uses Airflow Variables and Connections
6. Monitoring: Built-in UI for monitoring and logging

from airflow.utils.dates import days_ago
from airflow.operators.email_operator import EmailOperator

with DAG(
    'monthly_rebalancing',
    default_args=default_args,
    description='Monthly index rebalancing with notifications',
    schedule_interval='0 18 15 * *',
    catchup=False
) as dag:
    
    start = DummyOperator(task_id='start')
    
    # Data preparation
    fetch_data = PythonOperator(
        task_id='fetch_data',
        python_callable=fetch_market_data
    )
    
    # Processing
    process_sectors = create_sector_tasks()
    
    # Validation
    validate = PythonOperator(
        task_id='validate_results',
        python_callable=validate_weights
    )
    
    # Notification
    notify = EmailOperator(
        task_id='send_notification',
        to='team@quant.com',
        subject='Index Rebalancing Complete',
        html_content='Monthly index rebalancing has completed successfully.'
    )
    
    # Define workflow
    start >> fetch_data >> process_sectors >> validate >> notify