Update q-config and black for procedures/utils

exps/trading/baselines.py

@@ -4,6 +4,8 @@
 # python exps/trading/baselines.py --alg GRU
 # python exps/trading/baselines.py --alg LSTM
 # python exps/trading/baselines.py --alg ALSTM
+# python exps/trading/baselines.py --alg MLP
+# python exps/trading/baselines.py --alg SFM
 # python exps/trading/baselines.py --alg XGBoost
 # python exps/trading/baselines.py --alg LightGBM
 #####################################################
@@ -17,6 +19,10 @@ lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
 if str(lib_dir) not in sys.path:
     sys.path.insert(0, str(lib_dir))
 
+from procedures.q_exps import update_gpu
+from procedures.q_exps import update_market
+from procedures.q_exps import run_exp
+
 import qlib
 from qlib.utils import init_instance_by_config
 from qlib.workflow import R
@@ -31,15 +37,19 @@ def retrieve_configs():
     alg2names = OrderedDict()
     alg2names["GRU"] = "workflow_config_gru_Alpha360.yaml"
     alg2names["LSTM"] = "workflow_config_lstm_Alpha360.yaml"
+    alg2names["MLP"] = "workflow_config_mlp_Alpha360.yaml"
     # A dual-stage attention-based recurrent neural network for time series prediction, IJCAI-2017
     alg2names["ALSTM"] = "workflow_config_alstm_Alpha360.yaml"
     # XGBoost: A Scalable Tree Boosting System, KDD-2016
     alg2names["XGBoost"] = "workflow_config_xgboost_Alpha360.yaml"
     # LightGBM: A Highly Efficient Gradient Boosting Decision Tree, NeurIPS-2017
     alg2names["LightGBM"] = "workflow_config_lightgbm_Alpha360.yaml"
+    # State Frequency Memory (SFM): Stock Price Prediction via Discovering Multi-Frequency Trading Patterns, KDD-2017
+    alg2names["SFM"] = "workflow_config_sfm_Alpha360.yaml"
 
     # find the yaml paths
     alg2paths = OrderedDict()
+    print("Start retrieving the algorithm configurations")
     for idx, (alg, name) in enumerate(alg2names.items()):
         path = config_dir / name
         assert path.exists(), "{:} does not exist.".format(path)
@@ -48,56 +58,6 @@ def retrieve_configs():
     return alg2paths
 
 
-def update_gpu(config, gpu):
-    config = config.copy()
-    if "GPU" in config["task"]["model"]:
-        config["task"]["model"]["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()
-
-
 def main(xargs, exp_yaml):
     assert Path(exp_yaml).exists(), "{:} does not exist.".format(exp_yaml)