Update handler interface round2

qlib/contrib/data/handler.py

@@ -1,41 +1,73 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
-from ...data.dataset.handler import ConfigQLibDataHandler
-from ...data.dataset.processor import Processor, MinMaxNorm, ZscoreNorm, get_cls_kwargs
+from ...data.dataset.handler import DataHandlerLP
+from ...data.dataset.processor import Processor, MinMaxNorm, ZscoreNorm
+from ...utils import get_cls_kwargs
+from ...data.dataset import processor as processor_module
 from ...log import TimeInspector
 import copy
 
 
-class ALPHA360(ConfigQLibDataHandler):
-    config_template = {
-        "price": {"windows": range(60)},
-        "volume": {"windows": range(60)},
-    }
+class ALPHA360(DataHandlerLP):
+    def __init__(self, instruments="csi500", start_time=None, end_time=None):
+        data_loader = {
+            "class": "QlibDataLoader",
+            "kwargs": {
+                "config": {
+                    "feature": {
+                        "price": {
+                            "windows": range(60)
+                        },
+                        "volume": {
+                            "windows": range(60)
+                        },
+                    },
+                    "label": self.get_label_config()
+                },
+                "group_fields": True,
+            }
+        }
+        infer_processors = ["ConfigSectionProcessor"]  # ConfigSectionProcessor will normalize LABEL0
+        super().__init__(instruments, start_time, end_time, data_loader=data_loader, infer_processors=infer_processors)
+
+    def get_label_config(self):
+        return (["Ref($close, -2)/Ref($close, -1) - 1"], ["LABEL0"])
 
 
-class QLibDataHandlerV1(ConfigQLibDataHandler):
-    config_template = {
-        "kbar": {},
-        "price": {
-            "windows": [0],
-            "feature": ["OPEN", "HIGH", "LOW", "VWAP"],
-        },
-        "rolling": {},
-    }
+class ALPHA360vwap(ALPHA360):
+    def get_label_config(self):
+        return (["Ref($vwap, -2)/Ref($vwap, -1) - 1"], ["LABEL0"])
 
-    def __init__(self, start_date, end_date, infer_processors=[], learn_processors=["DropnaLabel"], fit_start_time=None, fit_end_time=None, **kwargs):
+
+class Alpha158(DataHandlerLP):
+    def __init__(
+        self,
+        instruments="csi500",
+        start_time=None,
+        end_time=None,
+        infer_processors=[],
+        learn_processors=["DropnaLabel", {
+            "class": "CSZScoreNorm",
+            "kwargs": {
+                "fields_group": "label"
+            }
+        }],
+        fit_start_time=None,
+        fit_end_time=None,
+    ):
         def check_transform_proc(proc_l):
             new_l = []
             for p in proc_l:
                 if not isinstance(p, Processor):
-                    klass, pkwargs = get_cls_kwargs(p)
+                    klass, pkwargs = get_cls_kwargs(p, processor_module)
+                    # FIXME: It's hard code here!!!!!
                     if isinstance(klass, (MinMaxNorm, ZscoreNorm)):
-                        assert(fit_start_time is not None and fit_end_time is not None)
+                        assert (fit_start_time is not None and fit_end_time is not None)
                         pkwargs.update({
                             "fit_start_time": fit_start_time,
                             "fit_end_time": fit_end_time,
-                            })
+                        })
                     new_l.append({"class": klass.__name__, "kwargs": pkwargs})
                 else:
                     new_l.append(p)
@@ -44,37 +76,37 @@ class QLibDataHandlerV1(ConfigQLibDataHandler):
         infer_processors = check_transform_proc(infer_processors)
         learn_processors = check_transform_proc(learn_processors)
 
-        super().__init__(start_date, end_date, infer_processors=infer_processors, learn_processors=learn_processors, **kwargs)
+        data_loader = {
+            "class": "QlibDataLoader",
+            "kwargs": {
+                "config": {
+                    "feature": self.get_feature_config(),
+                    "label": self.get_label_config()
+                },
+                "group_fields": True,
+            }
+        }
+        super().__init__(instruments,
+                         start_time,
+                         end_time,
+                         data_loader=data_loader,
+                         infer_processors=infer_processors,
+                         learn_processors=learn_processors)
 
-    def load_label(self):
-        """
-        load the labels df
-        :return:  df_labels
-        """
-        TimeInspector.set_time_mark()
+    def get_feature_config(self):
+        return {
+            "kbar": {},
+            "price": {
+                "windows": [0],
+                "feature": ["OPEN", "HIGH", "LOW", "VWAP"],
+            },
+            "rolling": {},
+        }
 
-        df_labels = super().load_label()
-
-        ## calculate new labels
-        df_labels["LABEL1"] = df_labels["LABEL0"].groupby(level="datetime").apply(lambda x: (x - x.mean()) / x.std())
-
-        df_labels = df_labels.drop(["LABEL0"], axis=1)
-
-        TimeInspector.log_cost_time("Finished loading labels.")
-
-        return df_labels
+    def get_label_config(self):
+        return (["Ref($close, -2)/Ref($close, -1) - 1"], ["LABEL0"])
 
 
-class Alpha158(QLibDataHandlerV1):
-    config_template = {
-        "kbar": {},
-        "price": {
-            "windows": [0],
-            "feature": ["OPEN", "HIGH", "LOW", "CLOSE"],
-        },
-        "rolling": {},
-    }
-
-    def __init__(self, *args, **kwargs):
-        kwargs["labels"] = ["Ref($close, -2)/Ref($close, -1) - 1"]
-        super().__init__(*args, **kwargs)
+class Alpha158vwap(Alpha158):
+    def get_label_config(self):
+        return (["Ref($vwap, -2)/Ref($vwap, -1) - 1"], ["LABEL0"])