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.utils import init_instance_by_config
-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
+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,
+        for record in task["record"]:
-        # please refer to https://qlib.readthedocs.io/en/latest/component/recorder.html#record-template.
+            record = record.copy()
-        par = PortAnaRecord(recorder, port_analysis_config)
+            if record["class"] == "SignalRecord":
-        par.generate()
+                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__":

lib/trade_models/quant_transformer.py

@@ -41,8 +41,8 @@ class QuantTransformer(Model):
   def __init__(
     self,
     d_feat=6,
-    hidden_size=64,
+    hidden_size=48,
-    num_layers=2,
+    depth=5,
     dropout=0.0,
     n_epochs=200,
     lr=0.001,
@@ -62,7 +62,7 @@ class QuantTransformer(Model):
     # set hyper-parameters.
     self.d_feat = d_feat
     self.hidden_size = hidden_size
-    self.num_layers = num_layers
+    self.depth = depth
     self.dropout = dropout
     self.n_epochs = n_epochs
     self.lr = lr
@@ -79,7 +79,7 @@ class QuantTransformer(Model):
       "Transformer parameters setting:"
       "\nd_feat : {}"
       "\nhidden_size : {}"
-      "\nnum_layers : {}"
+      "\ndepth : {}"
       "\ndropout : {}"
       "\nn_epochs : {}"
       "\nlr : {}"
@@ -93,7 +93,7 @@ class QuantTransformer(Model):
       "\nseed : {}".format(
         d_feat,
         hidden_size,
-        num_layers,
+        depth,
         dropout,
         n_epochs,
         lr,
@@ -112,7 +112,9 @@ class QuantTransformer(Model):
       np.random.seed(self.seed)
       torch.manual_seed(self.seed)
 
-    self.model = TransformerModel(d_feat=self.d_feat)
+    self.model = TransformerModel(d_feat=self.d_feat,
+                                  embed_dim=self.hidden_size,
+                                  depth=self.depth)
     self.logger.info('model: {:}'.format(self.model))
     self.logger.info('model size: {:.3f} MB'.format(count_parameters_in_MB(self.model)))