Update Q workflow

exps/trading/workflow_tt.py

@@ -1,12 +1,12 @@
 #####################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.02 #
 #####################################################
 # 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_tt.py
 #####################################################
-import sys, site, argparse
+import sys, argparse
 from pathlib import Path
 
 lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
@@ -15,19 +15,11 @@ if str(lib_dir) not in sys.path:
 
 import qlib
 from qlib.config import C
-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.utils import init_instance_by_config
 from qlib.workflow import R
-from qlib.workflow.record_temp import SignalRecord, PortAnaRecord
 from qlib.utils import flatten_dict
+from qlib.log import set_log_basic_config
 
 
 def main(xargs):
@@ -73,13 +65,51 @@ def main(xargs):
         },
     }
 
-    task = {"model": model_config, "dataset": dataset_config}
+    port_analysis_config = {
+        "strategy": {
+            "class": "TopkDropoutStrategy",
+            "module_path": "qlib.contrib.strategy.strategy",
+            "kwargs": {
+                "topk": 50,
+                "n_drop": 5,
+            },
+        },
+        "backtest": {
+            "verbose": False,
+            "limit_threshold": 0.095,
+            "account": 100000000,
+            "benchmark": "SH000300",
+            "deal_price": "close",
+            "open_cost": 0.0005,
+            "close_cost": 0.0015,
+            "min_cost": 5,
+        },
+    }
+
+    record_config = [
+        {"class": "SignalRecord", "module_path": "qlib.workflow.record_temp", "kwargs": dict()},
+        {
+            "class": "SigAnaRecord",
+            "module_path": "qlib.workflow.record_temp",
+            "kwargs": dict(ana_long_short=False, ann_scaler=252),
+        },
+        {
+            "class": "PortAnaRecord",
+            "module_path": "qlib.workflow.record_temp",
+            "kwargs": dict(config=port_analysis_config),
+        },
+    ]
+
+    task = dict(model=model_config, dataset=dataset_config, record=record_config)
 
-    model = init_instance_by_config(model_config)
-    dataset = init_instance_by_config(dataset_config)
 
     # start exp to train model
     with R.start(experiment_name="train_tt_model"):
+        set_log_basic_config(R.get_recorder().root_uri / 'log.log')
+
+        model = init_instance_by_config(model_config)
+        dataset = init_instance_by_config(dataset_config)
+
         R.log_params(**flatten_dict(task))
         model.fit(dataset)
         R.save_objects(trained_model=model)
@@ -87,14 +117,19 @@ def main(xargs):
         # prediction
         recorder = R.get_recorder()
         print(recorder)
-        sr = SignalRecord(model, dataset, recorder)
-        sr.generate()
 
-        # backtest. If users want to use backtest based on their own prediction,
-        # please refer to https://qlib.readthedocs.io/en/latest/component/recorder.html#record-template.
-        par = PortAnaRecord(recorder, port_analysis_config)
-        par.generate()
-    
+        for record in task["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()
 
 
 if __name__ == "__main__":