trainer & group & collect & ensemble

examples/online_srv/update_online_pred.py

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+import fire
+import qlib
+from qlib.config import REG_CN
+from qlib.model.trainer import task_train
+from qlib.workflow.online.manager import OnlineManagerR
+from qlib.workflow.task.utils import list_recorders
+
+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",
+}
+
+task = {
+    "model": {
+        "class": "LGBModel",
+        "module_path": "qlib.contrib.model.gbdt",
+        "kwargs": {
+            "loss": "mse",
+            "colsample_bytree": 0.8879,
+            "learning_rate": 0.0421,
+            "subsample": 0.8789,
+            "lambda_l1": 205.6999,
+            "lambda_l2": 580.9768,
+            "max_depth": 8,
+            "num_leaves": 210,
+            "num_threads": 20,
+        },
+    },
+    "dataset": {
+        "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": {
+        "class": "SignalRecord",
+        "module_path": "qlib.workflow.record_temp",
+    },
+}
+
+
+def first_train(experiment_name="online_srv"):
+
+    rid = task_train(task_config=task, experiment_name=experiment_name)
+
+    online_manager = OnlineManagerR(experiment_name)
+    online_manager.reset_online_tag(rid)
+
+
+def update_online_pred(experiment_name="online_srv"):
+
+    online_manager = OnlineManagerR(experiment_name)
+
+    print("Here are the online models waiting for update:")
+    for rid, rec in list_recorders(experiment_name).items():
+        if online_manager.get_online_tag(rec) == OnlineManagerR.ONLINE_TAG:
+            print(rid)
+
+    online_manager.update_online_pred()
+
+
+if __name__ == "__main__":
+    ## to train a model and set it to online model, use the command below
+    # python update_online_pred.py first_train
+    ## to update online predictions once a day, use the command below
+    # python update_online_pred.py update_online_pred
+
+    provider_uri = "~/.qlib/qlib_data/cn_data"  # target_dir
+    qlib.init(provider_uri=provider_uri, region=REG_CN)
+    fire.Fire()