Start prototype

exps/trading/test-qlib.py

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-import os
-
-print('xxx123')

exps/trading/workflow_test.py

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+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+# Refer to:
+# - https://github.com/microsoft/qlib/blob/main/examples/workflow_by_code.ipynb
+# - https://github.com/microsoft/qlib/blob/main/examples/workflow_by_code.py
+# python exps/trading/workflow_test.py
+#####################################################
+import sys, site
+from pathlib import Path
+
+lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
+if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
+
+import qlib
+import pandas as pd
+from qlib.config import REG_CN
+from qlib.contrib.model.gbdt import LGBModel
+from qlib.contrib.data.handler import Alpha158
+from qlib.contrib.strategy.strategy import TopkDropoutStrategy
+from qlib.contrib.evaluate import (
+    backtest as normal_backtest,
+    risk_analysis,
+)
+from qlib.utils import exists_qlib_data, init_instance_by_config
+from qlib.workflow import R
+from qlib.workflow.record_temp import SignalRecord, PortAnaRecord
+from qlib.utils import flatten_dict
+
+
+# use default data
+# NOTE: need to download data from remote: python scripts/get_data.py qlib_data_cn --target_dir ~/.qlib/qlib_data/cn_data
+provider_uri = "~/.qlib/qlib_data/cn_data"  # target_dir
+if not exists_qlib_data(provider_uri):
+    print(f"Qlib data is not found in {provider_uri}")
+    sys.path.append(str(scripts_dir))
+    from get_data import GetData
+    GetData().qlib_data(target_dir=provider_uri, region=REG_CN)
+qlib.init(provider_uri=provider_uri, region=REG_CN)
+
+
+market = "csi300"
+benchmark = "SH000300"
+
+
+###################################
+# train model
+###################################
+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": market,
+}
+
+task = {
+    "model": {
+        "class": "QuantTransformer",
+        "module_path": "trade_models",
+        "kwargs": {
+            "loss": "mse",
+            "GPU": "0",
+            "metric": "loss",
+        },
+    },
+    "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"),
+            },
+        },
+    },
+}
+
+# model initiaiton
+model = init_instance_by_config(task["model"])
+dataset = init_instance_by_config(task["dataset"])
+
+# start exp to train model
+with R.start(experiment_name="train_model"):
+    R.log_params(**flatten_dict(task))
+    model.fit(dataset)
+    R.save_objects(trained_model=model)
+    rid = R.get_recorder().id