fill_placehorder

qlib/model/trainer.py

@@ -45,6 +45,35 @@ def begin_task_train(task_config: dict, experiment_name: str, recorder_name: str
     return recorder
 
 
+def fill_placeholder(kwargs, model, dataset):
+    """
+        Detect placeholder(<MODEL> and <DATASET>) in dict and fill them.
+
+        Args:
+            kwargs (Dict): the parameter dict will be filled
+            model (Model): fill <MODEL>
+            dataset (Dataset): fill <DATASET>
+
+        Returns:
+            Dict: the parameter dict
+    """
+    top = 0
+    tail = 1
+    dict_quene = [kwargs]
+    while(top < tail):
+        now_dict = dict_quene[top]
+        top += 1
+        for key in now_dict.keys():
+            if(isinstance(now_dict[key], dict)):
+                dict_quene.append(now_dict[key])
+                tail += 1
+            elif(now_dict[key] == "<MODEL>"):
+                now_dict[key] = model
+            elif(now_dict[key] == "<DATASET>"):
+                now_dict[key] = dataset 
+    return kwargs
+
+
 def end_task_train(rec: Recorder, experiment_name: str) -> Recorder:
     """
     Finish task training with real model fitting and saving.
@@ -73,13 +102,9 @@ def end_task_train(rec: Recorder, experiment_name: str) -> Recorder:
             records = [records]
         for record in records:
             cls, kwargs = get_cls_kwargs(record, default_module="qlib.workflow.record_temp")
-            if cls is SignalRecord:
-                rconf = {"model": model, "dataset": dataset, "recorder": rec}
-            else:
-                rconf = {"recorder": rec}
-            r = cls(**kwargs, **rconf)
+            kwargs = fill_placeholder(kwargs, model, dataset)
+            r = cls(**kwargs, **{"record", record})
             r.generate()
-
     return rec
 
 

test.yaml

@@ -0,0 +1,72 @@
+qlib_init:
+    provider_uri: "~/.qlib/qlib_data/cn_data"
+    region: cn
+market: &market csi300
+benchmark: &benchmark SH000300
+data_handler_config: &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
+port_analysis_config: &port_analysis_config
+    strategy:
+        class: TopkDropoutStrategy
+        module_path: qlib.contrib.strategy.strategy
+        kwargs:
+            model: <MODEL>
+            dataset: <DATASET>
+            topk: 50
+            n_drop: 5
+    backtest:
+        verbose: False
+        limit_threshold: 0.095
+        account: 100000000
+        benchmark: *benchmark
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
+task:
+    model:
+        class: LGBModel
+        module_path: qlib.contrib.model.gbdt
+        kwargs:
+            loss: mse
+            colsample_bytree: 0.8879
+            learning_rate: 0.2
+            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
+          kwargs: 
+            model: <MODEL>
+            dataset: <DATASET>
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: 
+            model: <MODEL>
+            dataset: <DATASET>
+            ana_long_short: False
+            ann_scaler: 252
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: 
+            config: *port_analysis_config