```python
import argparse
import qlib
from qlib.workflow import R
from qlib.workflow.online.manager import OnlineManager
from qlib.workflow.task.manage import TaskManager
from qlib.workflow.task.gen import RollingGen
from qlib.workflow import R
from qlib.utils import init_instance_by_config
from qlib.workflow.online.update import PredUpdater
from qlib.workflow.online.strategy import RollingStrategy
from qlib.workflow.online.simulator import Simulator
from qlib.contrib.evaluate import risk_analysis

class OnlineManagerSimulator:
    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
        qlib.init(provider_uri=self.provider_uri, region=self.region)
        self.online_manager = OnlineManager(
            experiment_name=self.experiment_name,
            trainer=self.trainer,
            task_url=self.task_url,
            task_db_name=self.task_db_name,
            task_pool_name=self.task_pool_name,
            rolling_step=self.rolling_step,
            start_time=self.start_time,
            end_time=self.end_time,
        )

    def reset_experiment(self):
        self.online_manager.reset()

    def run_workflow(self):
        task_manager = TaskManager(self.task_url)
        rolling_gen = RollingGen(self.tasks, self.rolling_step)
        for task_config in rolling_gen:
            task = task_manager.create_task(task_config)
            task_manager.push_task(task)
        self.online_manager.run()

    def train_tasks(self):
        # This method should be implemented based on the specific multiprocessing or distributed computing framework in use.
        pass

    def simulate(self):
        pred_updater = PredUpdater()
        strategy = RollingStrategy()
        simulator = Simulator(start_time=self.start_time, end_time=self.end_time)
        for _ in range(self.rolling_step):
            self.online_manager.update_online_pred(pred_updater)
            self.online_manager.adjust_online_exp(strategy)
            simulator.step()

    def collect_results(self):
        # Collect and return results
        pass

    def get_signals(self):
        # Get and return signals
        pass

    def perform_backtesting(self):
        # Perform backtesting
        pass

    def risk_analysis(self):
        # Perform risk analysis
        pass

if __name__ == "__main__":
    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", type=str, required=True)  # Assuming JSON string for simplicity
    parser.add_argument("--trainer", type=str, required=True)  # Assuming JSON string for simplicity

    args = parser.parse_args()

    tasks = eval(args.tasks)  # Unsafe, but for simplicity in this example
    trainer = eval(args.trainer)  # Unsafe, but for simplicity in this example

    simulator = OnlineManagerSimulator(
        provider_uri=args.provider_uri,
        region=args.region,
        experiment_name=args.experiment_name,
        task_url=args.task_url,
        task_db_name=args.task_db_name,
        task_pool_name=args.task_pool_name,
        rolling_step=args.rolling_step,
        start_time=args.start_time,
        end_time=args.end_time,
        tasks=tasks,
        trainer=trainer,
    )

    simulator.reset_experiment()
    simulator.run_workflow()
    simulator.simulate()
    # Further steps like collect_results, get_signals, perform_backtesting, and risk_analysis can be implemented as needed.
```