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131 lines
5.1 KiB
Python
131 lines
5.1 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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"""
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This example shows how OnlineManager works with rolling tasks.
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There are four parts including first train, routine 1, add strategy and routine 2.
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Firstly, the OnlineManager will finish the first training and set trained models to `online` models.
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Next, the OnlineManager will finish a routine process, including update online prediction -> prepare tasks -> prepare new models -> prepare signals
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Then, we will add some new strategies to the OnlineManager. This will finish first training of new strategies.
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Finally, the OnlineManager will finish second routine and update all strategies.
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"""
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import os
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import fire
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import qlib
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from qlib.workflow import R
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from qlib.workflow.online.strategy import RollingStrategy
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from qlib.workflow.task.gen import RollingGen
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from qlib.workflow.online.manager import OnlineManager
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from qlib.tests.config import CSI100_RECORD_XGBOOST_TASK_CONFIG, CSI100_RECORD_LGB_TASK_CONFIG
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class RollingOnlineExample:
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def __init__(
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self,
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provider_uri="~/.qlib/qlib_data/cn_data",
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region="cn",
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task_url="mongodb://10.0.0.4:27017/",
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task_db_name="rolling_db",
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rolling_step=550,
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tasks=None,
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add_tasks=None,
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):
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if add_tasks is None:
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add_tasks = [CSI100_RECORD_LGB_TASK_CONFIG]
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if tasks is None:
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tasks = [CSI100_RECORD_XGBOOST_TASK_CONFIG]
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mongo_conf = {
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"task_url": task_url, # your MongoDB url
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"task_db_name": task_db_name, # database name
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}
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qlib.init(provider_uri=provider_uri, region=region, mongo=mongo_conf)
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self.tasks = tasks
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self.add_tasks = add_tasks
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self.rolling_step = rolling_step
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strategies = []
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for task in tasks:
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name_id = task["model"]["class"] # NOTE: Assumption: The model class can specify only one strategy
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strategies.append(
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RollingStrategy(
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name_id,
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task,
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RollingGen(step=rolling_step, rtype=RollingGen.ROLL_SD),
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)
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)
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self.rolling_online_manager = OnlineManager(strategies)
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_ROLLING_MANAGER_PATH = (
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".RollingOnlineExample" # the OnlineManager will dump to this file, for it can be loaded when calling routine.
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)
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# Reset all things to the first status, be careful to save important data
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def reset(self):
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for task in self.tasks + self.add_tasks:
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name_id = task["model"]["class"]
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exp = R.get_exp(experiment_name=name_id)
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for rid in exp.list_recorders():
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exp.delete_recorder(rid)
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if os.path.exists(self._ROLLING_MANAGER_PATH):
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os.remove(self._ROLLING_MANAGER_PATH)
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def first_run(self):
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print("========== reset ==========")
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self.reset()
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print("========== first_run ==========")
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self.rolling_online_manager.first_train()
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print("========== collect results ==========")
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print(self.rolling_online_manager.get_collector()())
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print("========== dump ==========")
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self.rolling_online_manager.to_pickle(self._ROLLING_MANAGER_PATH)
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def routine(self):
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print("========== load ==========")
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self.rolling_online_manager = OnlineManager.load(self._ROLLING_MANAGER_PATH)
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print("========== routine ==========")
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self.rolling_online_manager.routine()
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print("========== collect results ==========")
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print(self.rolling_online_manager.get_collector()())
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print("========== signals ==========")
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print(self.rolling_online_manager.get_signals())
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print("========== dump ==========")
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self.rolling_online_manager.to_pickle(self._ROLLING_MANAGER_PATH)
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def add_strategy(self):
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print("========== load ==========")
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self.rolling_online_manager = OnlineManager.load(self._ROLLING_MANAGER_PATH)
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print("========== add strategy ==========")
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strategies = []
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for task in self.add_tasks:
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name_id = task["model"]["class"] # NOTE: Assumption: The model class can specify only one strategy
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strategies.append(
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RollingStrategy(
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name_id,
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task,
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RollingGen(step=self.rolling_step, rtype=RollingGen.ROLL_SD),
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)
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)
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self.rolling_online_manager.add_strategy(strategies=strategies)
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print("========== dump ==========")
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self.rolling_online_manager.to_pickle(self._ROLLING_MANAGER_PATH)
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def main(self):
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self.first_run()
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self.routine()
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self.add_strategy()
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self.routine()
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if __name__ == "__main__":
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####### to train the first version's models, use the command below
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# python rolling_online_management.py first_run
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####### to update the models and predictions after the trading time, use the command below
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# python rolling_online_management.py routine
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####### to define your own parameters, use `--`
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# python rolling_online_management.py first_run --exp_name='your_exp_name' --rolling_step=40
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fire.Fire(RollingOnlineExample)
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