Refine TT workflow

lib/procedures/q_exps.py

@@ -0,0 +1,63 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.02 #
+#####################################################
+
+import qlib
+from qlib.utils import init_instance_by_config
+from qlib.workflow import R
+from qlib.utils import flatten_dict
+from qlib.log import set_log_basic_config
+
+
+def update_gpu(config, gpu):
+    config = config.copy()
+    if "task" in config and "GPU" in config["task"]["model"]:
+        config["task"]["model"]["GPU"] = gpu
+    elif "model" in config and "GPU" in config["model"]:
+        config["model"]["GPU"] = gpu
+    elif "GPU" in config:
+        config["GPU"] = gpu
+    return config
+
+
+def update_market(config, market):
+    config = config.copy()
+    config["market"] = market
+    config["data_handler_config"]["instruments"] = market
+    return config
+
+
+def run_exp(task_config, dataset, experiment_name, recorder_name, uri):
+
+    # model initiaiton
+    print("")
+    print("[{:}] - [{:}]: {:}".format(experiment_name, recorder_name, uri))
+    print("dataset={:}".format(dataset))
+
+    model = init_instance_by_config(task_config["model"])
+
+    # start exp
+    with R.start(experiment_name=experiment_name, recorder_name=recorder_name, uri=uri):
+
+        log_file = R.get_recorder().root_uri / "{:}.log".format(experiment_name)
+        set_log_basic_config(log_file)
+
+        # train model
+        R.log_params(**flatten_dict(task_config))
+        model.fit(dataset)
+        recorder = R.get_recorder()
+        R.save_objects(**{"model.pkl": model})
+
+        # generate records: prediction, backtest, and analysis
+        for record in task_config["record"]:
+            record = record.copy()
+            if record["class"] == "SignalRecord":
+                srconf = {"model": model, "dataset": dataset, "recorder": recorder}
+                record["kwargs"].update(srconf)
+                sr = init_instance_by_config(record)
+                sr.generate()
+            else:
+                rconf = {"recorder": recorder}
+                record["kwargs"].update(rconf)
+                ar = init_instance_by_config(record)
+                ar.generate()