QLibDataHandlerClose is renamed to Alpha158

README.md

@@ -183,7 +183,7 @@ Your PR of new Quant models is highly welcomed.
 # Quant Dataset Zoo
 Dataset plays a very important role in Quant. Here is a list of the datasets build on `Qlib`.
 - [Alpha360](./qlib/contrib/estimator/handler.py)
-- [QLibDataHandlerClose](./qlib/contrib/estimator/handler.py)
+- [Alpha158](./qlib/contrib/estimator/handler.py)
 
 Here is a tutorial to build dataset with `Qlib`.
 Your PR to build new Quant dataset is highly welcomed.

docs/component/data.rst

@@ -207,14 +207,14 @@ Example
 
 Know more about how to run ``Data Handler`` with ``Estimator``, please refer to `Estimator: Workflow Management <estimator.html>`_
 
-Qlib provides implemented data handler `QLibDataHandlerClose`. The following example shows how to run `QLibDataHandlerV1` as a single module. 
+Qlib provides implemented data handler `Alpha158`. The following example shows how to run `QLibDataHandlerV1` as a single module.
 
 .. note:: Users need to initialize ``Qlib`` with `qlib.init` first, please refer to `initialization <../start/initialization.html>`_.
 
 
 .. code-block:: Python
 
-    from qlib.contrib.estimator.handler import QLibDataHandlerClose
+    from qlib.contrib.estimator.handler import Alpha158
     from qlib.contrib.model.gbdt import LGBModel
 
     DATA_HANDLER_CONFIG = {
@@ -233,7 +233,7 @@ Qlib provides implemented data handler `QLibDataHandlerClose`. The following exa
         "test_end_date": "2020-08-01",
     }
 
-    exampleDataHandler = QLibDataHandlerClose(**DATA_HANDLER_CONFIG)
+    exampleDataHandler = Alpha158(**DATA_HANDLER_CONFIG)
 
     # example of 'get_split_data'
     x_train, y_train, x_validate, y_validate, x_test, y_test = exampleDataHandler.get_split_data(**TRAINER_CONFIG)

docs/component/estimator.rst

@@ -50,7 +50,7 @@ Below is a typical config file of ``Estimator``.
         num_leaves: 210
         num_threads: 20
     data:
-      class: QLibDataHandlerClose
+      class: Alpha158
       args:
         dropna_label: True
       filter:
@@ -291,7 +291,7 @@ Users can use the specified data handler by config as follows.
 .. code-block:: YAML
 
     data:
-        class: QLibDataHandlerClose
+        class: Alpha158
         args:
             start_date: 2005-01-01
             end_date: 2018-04-30  

docs/component/model.rst

@@ -121,7 +121,7 @@ Example
 - Run the following code to get the `prediction score` `pred_score`
     .. code-block:: Python
 
-        from qlib.contrib.estimator.handler import QLibDataHandlerClose
+        from qlib.contrib.estimator.handler import Alpha158
         from qlib.contrib.model.gbdt import LGBModel
 
         DATA_HANDLER_CONFIG = {
@@ -140,7 +140,7 @@ Example
             "test_end_date": "2020-08-01",
         }
 
-        x_train, y_train, x_validate, y_validate, x_test, y_test = QLibDataHandlerClose(
+        x_train, y_train, x_validate, y_validate, x_test, y_test = Alpha158(
             **DATA_HANDLER_CONFIG
         ).get_split_data(**TRAINER_CONFIG)
 
@@ -163,7 +163,7 @@ Example
         pred_score = pd.DataFrame(index=_pred.index)
         pred_score["score"] = _pred.iloc(axis=1)[0]
 
-    .. note:: `QLibDataHandlerClose` is the data handler provided by ``Qlib``, please refer to `Data Handler <data.html#data-handler>`_.
+    .. note:: `Alpha158` is the data handler provided by ``Qlib``, please refer to `Data Handler <data.html#data-handler>`_.
 
 Also, the above example has been given in ``examples/train_backtest_analyze.ipynb``.
 

examples/estimator/estimator_config.yaml

@@ -17,7 +17,7 @@ model:
     num_leaves: 210
     num_threads: 20
 data:
-  class: QLibDataHandlerClose
+  class: Alpha158
   args:
     dropna_label: True
   filter:

examples/estimator/estimator_config_dnn.yaml

@@ -18,7 +18,7 @@ model:
         batch_size: 4096
         GPU: '0'
 data:
-  class: QLibDataHandlerClose
+  class: Alpha158
   args:
     dropna_label: True
     dropna_feature: True

examples/train_and_backtest.py

@@ -8,7 +8,7 @@ import qlib
 import pandas as pd
 from qlib.config import REG_CN
 from qlib.contrib.model.gbdt import LGBModel
-from qlib.contrib.estimator.handler import QLibDataHandlerClose
+from qlib.contrib.estimator.handler import Alpha158
 from qlib.contrib.strategy.strategy import TopkDropoutStrategy
 from qlib.contrib.evaluate import (
     backtest as normal_backtest,
@@ -54,7 +54,7 @@ if __name__ == "__main__":
 
     # use default DataHandler
     # custom DataHandler, refer to: TODO: DataHandler API url
-    x_train, y_train, x_validate, y_validate, x_test, y_test = QLibDataHandlerClose(
+    x_train, y_train, x_validate, y_validate, x_test, y_test = Alpha158(
         **DATA_HANDLER_CONFIG
     ).get_split_data(**TRAINER_CONFIG)
 

examples/train_backtest_analyze.ipynb

@@ -13,7 +13,7 @@
     "import pandas as pd\n",
     "from qlib.config import REG_CN\n",
     "from qlib.contrib.model.gbdt import LGBModel\n",
-    "from qlib.contrib.estimator.handler import QLibDataHandlerClose\n",
+    "from qlib.contrib.estimator.handler import Alpha158\n",
     "from qlib.contrib.strategy.strategy import TopkDropoutStrategy\n",
     "from qlib.contrib.evaluate import (\n",
     "    backtest as normal_backtest,\n",
@@ -87,7 +87,7 @@
     "\n",
     "# use default DataHandler\n",
     "# custom DataHandler, refer to: TODO: DataHandler api url\n",
-    "x_train, y_train, x_validate, y_validate, x_test, y_test = QLibDataHandlerClose(**DATA_HANDLER_CONFIG).get_split_data(**TRAINER_CONFIG)\n",
+    "x_train, y_train, x_validate, y_validate, x_test, y_test = Alpha158(**DATA_HANDLER_CONFIG).get_split_data(**TRAINER_CONFIG)\n",
     "\n",
     "\n",
     "MODEL_CONFIG = {\n",

qlib/contrib/estimator/handler.py

@@ -556,7 +556,7 @@ class QLibDataHandlerV1(ConfigQLibDataHandler):
         return df_labels
 
 
-class QLibDataHandlerClose(QLibDataHandlerV1):
+class Alpha158(QLibDataHandlerV1):
     config_template = {
         'kbar': {},
         'price': {
@@ -568,7 +568,7 @@ class QLibDataHandlerClose(QLibDataHandlerV1):
 
     def _init_kwargs(self, **kwargs):
         kwargs['labels'] = ["Ref($close, -2)/Ref($close, -1) - 1"]
-        super(QLibDataHandlerClose, self)._init_kwargs(**kwargs)
+        super(Alpha158, self)._init_kwargs(**kwargs)
 
 
 # if __name__ == '__main__':

tests/test_all_pipeline.py

@@ -13,7 +13,7 @@ import qlib
 from qlib.config import REG_CN
 from qlib.utils import drop_nan_by_y_index
 from qlib.contrib.model.gbdt import LGBModel
-from qlib.contrib.estimator.handler import QLibDataHandlerClose
+from qlib.contrib.estimator.handler import Alpha158
 from qlib.contrib.strategy.strategy import TopkDropoutStrategy
 from qlib.contrib.evaluate import (
     backtest as normal_backtest,
@@ -79,7 +79,7 @@ def train():
             model performance
     """
     # get data
-    x_train, y_train, x_validate, y_validate, x_test, y_test = QLibDataHandlerClose(
+    x_train, y_train, x_validate, y_validate, x_test, y_test = Alpha158(
         **DATA_HANDLER_CONFIG
     ).get_split_data(**TRAINER_CONFIG)
 

tests/test_dump_data.py

@@ -25,7 +25,7 @@ QLIB_DIR.mkdir(exist_ok=True, parents=True)
 
 
 class TestDumpData(unittest.TestCase):
-    FIELDS = "open,close,high,low,volume,factor,change".split(",")
+    FIELDS = "open,close,high,low,volume".split(",")
     QLIB_FIELDS = list(map(lambda x: f"${x}", FIELDS))
     DUMP_DATA = None
     STOCK_NAMES = None