 Here is a basic structure for the class `OnlineManager` using the qlib library. Please note that this is a simplified version and might need further adjustments based on your specific requirements.

```python
import sys
import time
import multiprocessing
import qlib

class OnlineManager:
    def __init__(self, provider_uri, region, experiment_name, task_url, task_db_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_db_name = task_db_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
        self.qlib_env = qlib.qlib_init(provider_uri=self.provider_uri, region=self.region)

    def reset(self):
        # Code for resetting the experiment
        pass

    def run(self):
        # Code for running the entire workflow automatically
        pass

    def train_tasks(self):
        # Code for training tasks by other processes or machines for multiprocessing
        pass

    def simulate(self):
        # Code for simulating the workflow
        pass

    def collect_results(self):
        # Code for collecting results
        pass

    def get_signals(self):
        # Code for getting signals
        pass

    def backtesting(self):
        # Code for backtesting
        pass

    def risk_analysis(self):
        # Code for risk analysis
        pass

def main(args):
    # Initialize the OnlineManager
    manager = OnlineManager(args.provider_uri, args.region, args.experiment_name, args.task_url, args.task_db_name, args.task_pool_name, args.rolling_step, args.start_time, args.end_time, args.tasks, args.trainer)

    # Reset the experiment
    manager.reset()

    # Run the workflow
    manager.run()

    # Collect results
    manager.collect_results()

    # Get signals
    manager.get_signals()

    # Perform backtesting
    manager.backtesting()

    # Perform risk analysis
    manager.risk_analysis()

if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument("--provider_uri", type=str, required=True)
    parser.add_argument("--region", type=str, required=True)
    parser.add_argument("--experiment_name", type=str, required=True)
    parser.add_argument("--task_url", type=str, required=True)
    parser.add_argument("--task_db_name", type=str, required=True)
    parser.add_argument("--task_pool_name", type=str, required=True)
    parser.add_argument("--rolling_step", type=int, required=True)
    parser.add_argument("--start_time", type=str, required=True)
    parser.add_argument("--end_time", type=str, required=True)
    parser.add_argument("--tasks", nargs='+', type=str, required=True)
    parser.add_argument("--trainer", type=str, required=True)

    args = parser.parse_args()
    main(args)
```

To make this script executable from the command line, save it as `online_manager.py` and run it using:

```bash
python online_manager.py --provider_uri <your_provider_uri> --region <your_region> --experiment_name <your_experiment_name> --task_url <your_task_url> --task_db_name <your_task_db_name> --task_pool_name <your_task_pool_name> --rolling_step <your_rolling_step> --start_time <your_start_time> --end_time <your_end_time> --tasks <your_tasks> --trainer <your_trainer>
```