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95 lines
3.0 KiB
Python
95 lines
3.0 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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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": {"class": "Alpha158", "module_path": "qlib.contrib.data.handler", "kwargs": data_handler_config,},
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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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{"class": "SignalRecord", "module_path": "qlib.workflow.record_temp",},
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{"class": "SigAnaRecord", "module_path": "qlib.workflow.record_temp",},
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]
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# use lgb
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task_lgb_config = {
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"model": {"class": "LGBModel", "module_path": "qlib.contrib.model.gbdt",},
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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": {"class": "XGBModel", "module_path": "qlib.contrib.model.xgboost",},
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"dataset": dataset_config,
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"record": record_config,
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}
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provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
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qlib.init(provider_uri=provider_uri, region=REG_CN)
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C["mongo"] = {
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"task_url": "mongodb://localhost: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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tasks = task_generator(
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task_xgboost_config, # default task name
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RollingGen(step=550, rtype=RollingGen.ROLL_SD), # generate different date segment
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task_lgb=task_lgb_config, # use "task_lgb" as the task name
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)
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# Uncomment next two lines to see the generated tasks
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# from pprint import pprint
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# pprint(tasks)
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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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from qlib.workflow.task.manage import run_task
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from qlib.workflow.task.collect import TaskCollector
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from qlib.model.trainer import task_train
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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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def get_task_key(task_config):
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task_key = task_config["task_key"]
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rolling_end_timestamp = task_config["dataset"]["kwargs"]["segments"]["test"][1]
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return task_key, rolling_end_timestamp.strftime("%Y-%m-%d")
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def my_filter(task_config):
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# only choose the results of "task_lgb" and test in 2019 from all tasks
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task_key, rolling_end = get_task_key(task_config)
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if task_key == "task_lgb" and rolling_end.startswith("2019"):
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return True
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return False
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# name tasks by "get_task_key" and filter tasks by "my_filter"
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pred_rolling = TaskCollector.collect_predictions(exp_name, get_task_key, my_filter)
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pred_rolling
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