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Update alpha.rst

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2020-12-21 21:35:41 +08:00
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@@ -50,53 +50,33 @@ Users can use ``Data Handler`` to build formulaic alphas `MACD` in qlib:
.. code-block:: python
>> from qlib.data.dataset.handler import QLibDataHandler
>> from qlib.data.dataset.loader import QlibDataLoader
>> MACD_EXP = '(EMA($close, 12) - EMA($close, 26))/$close - EMA((EMA($close, 12) - EMA($close, 26))/$close, 9)/$close'
>> fields = [MACD_EXP] # MACD
>> names = ['MACD']
>> labels = ['$close'] # label
>> labels = ['Ref($close, -2)/Ref($close, -1) - 1'] # label
>> label_names = ['LABEL']
>> data_handler = QLibDataHandler(start_date='2010-01-01', end_date='2017-12-31', fields=fields, names=names, labels=labels, label_names=label_names)
>> TRAINER_CONFIG = {
.. "train_start_date": "2007-01-01",
.. "train_end_date": "2014-12-31",
.. "validate_start_date": "2015-01-01",
.. "validate_end_date": "2016-12-31",
.. "test_start_date": "2017-01-01",
.. "test_end_date": "2020-08-01",
>> data_loader_config = {
.. "feature": (fields, names),
.. "label": (labels, label_names)
.. }
>> feature_train, label_train, feature_validate, label_validate, feature_test, label_test = data_handler.get_split_data(**TRAINER_CONFIG)
>> print(feature_train, label_train)
MACD
instrument datetime
SH600000 2010-01-04 -0.008625
2010-01-05 -0.007234
2010-01-06 -0.007693
2010-01-07 -0.009633
2010-01-08 -0.009891
... ...
SZ300251 2014-12-25 0.043072
2014-12-26 0.041345
2014-12-29 0.042733
2014-12-30 0.042066
2014-12-31 0.036299
[322025 rows x 1 columns]
LABEL
instrument datetime
SH600000 2010-01-04 4.260015
2010-01-05 4.292182
2010-01-06 4.207747
2010-01-07 4.113258
2010-01-08 4.159496
... ...
SZ300251 2014-12-25 4.343212
2014-12-26 4.470587
2014-12-29 4.762474
2014-12-30 4.369748
2014-12-31 4.182222
[322025 rows x 1 columns]
>> data_handler = QlibDataLoader(config=data_loader_config)
>> df = data_handler.load(instruments='csi300', start_time='2010-01-01', end_time='2017-12-31')
>> print(df)
feature label
MACD LABEL
datetime instrument
2010-01-04 SH600000 -0.011547 -0.019672
SH600004 0.002745 -0.014721
SH600006 0.010133 0.002911
SH600008 -0.001113 0.009818
SH600009 0.025878 -0.017758
... ... ...
2017-12-29 SZ300124 0.007306 -0.005074
SZ300136 -0.013492 0.056352
SZ300144 -0.000966 0.011853
SZ300251 0.004383 0.021739
SZ300315 -0.030557 0.012455
Reference
===========