finished document and example

2026-06-30 17:41:18 +08:00 · 2021-03-09 17:22:36 +08:00
parent def132e140
commit 83dbdfb45e
4 changed files with 620 additions and 177 deletions
--- a/examples/taskmanager/task_manager_rolling.py
+++ b/examples/taskmanager/task_manager_rolling.py
@@ -0,0 +1,108 @@
+import qlib
+from qlib.config import REG_CN
+from qlib.workflow.task.gen import RollingGen, task_generator
+from qlib.workflow.task.manage import TaskManager
+from qlib.config import C
+
+data_handler_config = {
+    "start_time": "2008-01-01",
+    "end_time": "2020-08-01",
+    "fit_start_time": "2008-01-01",
+    "fit_end_time": "2014-12-31",
+    "instruments": 'csi100',
+}
+
+dataset_config = {
+        "class": "DatasetH",
+        "module_path": "qlib.data.dataset",
+        "kwargs": {
+            "handler": {
+                "class": "Alpha158",
+                "module_path": "qlib.contrib.data.handler",
+                "kwargs": data_handler_config,
+            },
+            "segments": {
+                "train": ("2008-01-01", "2014-12-31"),
+                "valid": ("2015-01-01", "2016-12-31"),
+                "test": ("2017-01-01", "2020-08-01"),
+            },
+        },
+    }
+
+record_config = [
+    {
+        "class": "SignalRecord",
+        "module_path": "qlib.workflow.record_temp",
+    },
+    {
+        "class": "SigAnaRecord",
+        "module_path": "qlib.workflow.record_temp",
+    }
+]
+
+# use lgb
+task_lgb_config = {
+    "model": {
+        "class": "LGBModel",
+        "module_path": "qlib.contrib.model.gbdt",
+    },
+    "dataset": dataset_config,
+    "record": record_config,
+}
+
+# use xgboost
+task_xgboost_config = {
+    "model": {
+        "class": "XGBModel",
+        "module_path": "qlib.contrib.model.xgboost",
+    },
+    "dataset": dataset_config,
+    "record": record_config,
+}
+
+provider_uri = "~/.qlib/qlib_data/cn_data"  # target_dir
+qlib.init(provider_uri=provider_uri, region=REG_CN)
+
+C["mongo"] = {
+    "task_url" : "mongodb://localhost:27017/", # maybe you need to change it to your url
+    "task_db_name" : "rolling_db"
+}
+
+exp_name = 'rolling_exp' # experiment name, will be used as the experiment in MLflow
+task_pool = 'rolling_task' # task pool name, will be used as the document in MongoDB
+
+tasks = task_generator(
+    task_xgboost_config, # default task name
+    RollingGen(step=550,rtype=RollingGen.ROLL_SD), # generate different date segment
+    task_lgb=task_lgb_config # use "task_lgb" as the task name
+)
+
+# Uncomment next two lines to see the generated tasks
+# from pprint import pprint
+# pprint(tasks)
+
+tm = TaskManager(task_pool=task_pool)
+tm.create_task(tasks) # all tasks will be saved to MongoDB
+
+from qlib.workflow.task.manage import run_task
+from qlib.workflow.task.collect import RollingCollector
+from qlib.model.trainer import task_train
+
+run_task(task_train, task_pool, experiment_name=exp_name) # all tasks will be trained using "task_train" method
+
+def get_task_key(task_config):
+    task_key = task_config["task_key"]
+    rolling_end_timestamp = task_config["dataset"]["kwargs"]["segments"]["test"][1]
+    #rolling_end_datatime = rolling_end_timestamp.to_pydatetime()
+    return task_key, rolling_end_timestamp.strftime('%Y-%m-%d')
+
+def my_filter(task_config):
+    # only choose the results of "task_lgb" and test in 2019 from all tasks
+    task_key, rolling_end = get_task_key(task_config)
+    if task_key=="task_lgb" and rolling_end.startswith('2019'):
+        return True
+    return False
+
+collector = RollingCollector(get_task_key, my_filter)
+pred_rolling = collector(exp_name) # name tasks by "get_task_key" and filter tasks by "my_filter"
+print(pred_rolling)