class OnlineManager:
    def __init__(self, provider_uri, region, experiment_name, task_URL, task_database_name, task_pool_name, rolling_step, start_time, end_time, tasks, trainer):
        self.provider_uri = provider_uri
        self.region = region
        self.experiment_name = experiment_name
        self.task_URL = task_URL
        self.task_database_name = task_database_name
        self.task_pool_name = task_pool_name
        self.rolling_step = rolling_step
        self.start_time = start_time
        self.end_time = end_time
        self.tasks = tasks
        self.trainer = trainer

    def reset_experiment(self):
        # Reset the experiment

    def run_workflow(self):
        # Run the entire workflow automatically

    def train_tasks_multiprocessing(self):
        # Train tasks by other processes or machines for multiprocessing

    def main(self):
        self.reset_experiment()
        self.run_workflow()
        # Other steps: collect results, get signals, perform backtesting, risk analysis

if __name__ == "__main__":
    # User-defined parameters
    provider_uri = "example_provider_uri"
    region = "example_region"
    experiment_name = "example_experiment"
    task_URL = "example_task_URL"
    task_database_name = "example_database"
    task_pool_name = "example_pool"
    rolling_step = 10
    start_time = "2022-01-01"
    end_time = "2022-12-31"
    tasks = ["task1", "task2", "task3"]
    trainer = "example_trainer"

    online_manager = OnlineManager(provider_uri, region, experiment_name, task_URL, task_database_name, task_pool_name, rolling_step, start_time, end_time, tasks, trainer)
    online_manager.main()