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105 lines
3.6 KiB
ReStructuredText
105 lines
3.6 KiB
ReStructuredText
.. _alpha:
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===========================
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Building Formulaic Alphas
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===========================
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.. currentmodule:: qlib
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Introduction
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===================
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In quantitative trading practice, designing novel factors that can explain and predict future asset returns are of vital importance to the profitability of a strategy. Such factors are usually called alpha factors, or alphas in short.
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A formulaic alpha, as the name suggests, is a kind of alpha that can be presented as a formula or a mathematical expression.
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Building Formulaic Alphas in ``Qlib``
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======================================
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In ``Qlib``, users can easily build formulaic alphas.
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Example
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-----------------
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`MACD`, short for moving average convergence/divergence, is a formulaic alpha used in technical analysis of stock prices. It is designed to reveal changes in the strength, direction, momentum, and duration of a trend in a stock's price.
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`MACD` can be presented as the following formula:
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.. math::
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MACD = 2\times (DIF-DEA)
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.. note::
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`DIF` means Differential value, which is 12-period EMA minus 26-period EMA.
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.. math::
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DIF = \frac{EMA(CLOSE, 12) - EMA(CLOSE, 26)}{CLOSE}
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`DEA`means a 9-period EMA of the DIF.
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.. math::
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DEA = \frac{EMA(DIF, 9)}{CLOSE}
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Users can use ``Data Handler`` to build formulaic alphas `MACD` in qlib:
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.. note:: Users need to initialize ``Qlib`` with `qlib.init` first. Please refer to `initialization <../start/initialization.html>`_.
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.. code-block:: python
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>> from qlib.contrib.estimator.handler import QLibDataHandler
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>> MACD_EXP = '(EMA($close, 12) - EMA($close, 26))/$close - EMA((EMA($close, 12) - EMA($close, 26))/$close, 9)/$close'
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>> fields = [MACD_EXP] # MACD
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>> names = ['MACD']
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>> labels = ['$close'] # label
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>> label_names = ['LABEL']
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>> data_handler = QLibDataHandler(start_date='2010-01-01', end_date='2017-12-31', fields=fields, names=names, labels=labels, label_names=label_names)
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>> TRAINER_CONFIG = {
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.. "train_start_date": "2007-01-01",
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.. "train_end_date": "2014-12-31",
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.. "validate_start_date": "2015-01-01",
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.. "validate_end_date": "2016-12-31",
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.. "test_start_date": "2017-01-01",
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.. "test_end_date": "2020-08-01",
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.. }
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>> feature_train, label_train, feature_validate, label_validate, feature_test, label_test = data_handler.get_split_data(**TRAINER_CONFIG)
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>> print(feature_train, label_train)
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MACD
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instrument datetime
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SH600000 2010-01-04 -0.008625
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2010-01-05 -0.007234
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2010-01-06 -0.007693
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2010-01-07 -0.009633
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2010-01-08 -0.009891
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... ...
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SZ300251 2014-12-25 0.043072
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2014-12-26 0.041345
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2014-12-29 0.042733
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2014-12-30 0.042066
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2014-12-31 0.036299
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[322025 rows x 1 columns]
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LABEL
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instrument datetime
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SH600000 2010-01-04 4.260015
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2010-01-05 4.292182
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2010-01-06 4.207747
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2010-01-07 4.113258
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2010-01-08 4.159496
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... ...
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SZ300251 2014-12-25 4.343212
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2014-12-26 4.470587
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2014-12-29 4.762474
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2014-12-30 4.369748
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2014-12-31 4.182222
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[322025 rows x 1 columns]
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Reference
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===========
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To kown more about ``Data Handler``, please refer to `Data Handler <../component/data.html>`_
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To kown more about ``Data Api``, please refer to `Data Api <../component/data.html>`_ |