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143 lines
4.0 KiB
Python
143 lines
4.0 KiB
Python
import qlib
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from qlib.config import REG_CN
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from qlib.workflow.task.gen import RollingGen, task_generator
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from qlib.workflow.task.manage import TaskManager
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from qlib.config import C
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from qlib.workflow.task.manage import run_task
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from qlib.workflow.task.collect import RollingCollector
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from qlib.model.trainer import task_train
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from qlib.workflow import R
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from pprint import pprint
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data_handler_config = {
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"start_time": "2008-01-01",
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"end_time": "2020-08-01",
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"fit_start_time": "2008-01-01",
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"fit_end_time": "2014-12-31",
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"instruments": "csi100",
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}
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dataset_config = {
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"class": "DatasetH",
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"module_path": "qlib.data.dataset",
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"kwargs": {
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"handler": {
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"class": "Alpha158",
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"module_path": "qlib.contrib.data.handler",
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"kwargs": data_handler_config,
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},
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"segments": {
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"train": ("2008-01-01", "2014-12-31"),
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"valid": ("2015-01-01", "2016-12-31"),
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"test": ("2017-01-01", "2020-08-01"),
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},
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},
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}
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record_config = [
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{
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"class": "SignalRecord",
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"module_path": "qlib.workflow.record_temp",
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},
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{
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"class": "SigAnaRecord",
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"module_path": "qlib.workflow.record_temp",
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},
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]
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# use lgb
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task_lgb_config = {
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"model": {
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"class": "LGBModel",
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"module_path": "qlib.contrib.model.gbdt",
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},
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"dataset": dataset_config,
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"record": record_config,
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}
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# use xgboost
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task_xgboost_config = {
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"model": {
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"class": "XGBModel",
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"module_path": "qlib.contrib.model.xgboost",
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},
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"dataset": dataset_config,
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"record": record_config,
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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():
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print("========== reset ==========")
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TaskManager(task_pool=task_pool).remove()
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# exp = R.get_exp(experiment_name=exp_name)
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# for rid in R.list_recorders():
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# exp.delete_recorder(rid)
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# This part corresponds to "Task Generating" in the document
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def task_generating():
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print("========== task_generating ==========")
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tasks = task_generator(
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tasks=[task_xgboost_config, task_lgb_config],
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generators=RollingGen(step=550, rtype=RollingGen.ROLL_SD), # generate different date segment
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)
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pprint(tasks)
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return tasks
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# This part corresponds to "Task Storing" in the document
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def task_storing(tasks):
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print("========== task_storing ==========")
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tm = TaskManager(task_pool=task_pool)
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tm.create_task(tasks) # all tasks will be saved to MongoDB
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# This part corresponds to "Task Running" in the document
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def task_running():
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print("========== task_running ==========")
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run_task(task_train, task_pool, experiment_name=exp_name) # all tasks will be trained using "task_train" method
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# This part corresponds to "Task Collecting" in the document
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def task_collecting():
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print("========== task_collecting ==========")
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def get_task_key(task_config):
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return task_config["model"]["class"]
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def my_filter(recorder):
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# only choose the results of "LGBModel"
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task_key = get_task_key(rolling_collector.get_task(recorder))
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if task_key == "LGBModel":
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return True
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return False
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rolling_collector = RollingCollector(exp_name)
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# group tasks by "get_task_key" and filter tasks by "my_filter"
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pred_rolling = rolling_collector.collect_rolling_predictions(get_task_key, my_filter)
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print(pred_rolling)
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if __name__ == "__main__":
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provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
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mongo_conf = {
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"task_url": "mongodb://10.0.0.4:27017/", # maybe you need to change it to your url
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"task_db_name": "rolling_db",
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}
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exp_name = "rolling_exp" # experiment name, will be used as the experiment in MLflow
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task_pool = "rolling_task" # task pool name, will be used as the document in MongoDB
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qlib.init(provider_uri=provider_uri, region=REG_CN, mongo=mongo_conf)
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reset()
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tasks = task_generating()
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task_storing(tasks)
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task_running()
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task_collecting()
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