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104 lines
3.6 KiB
ReStructuredText
104 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 <initialization.rst>`_.
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.. code-block:: python
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>>> from qlib.contrib.estimator.handler import QLibDataHandler
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>>> fields = ['(EMA($close, 12) - EMA($close, 26))/$close - EMA((EMA($close, 12) - EMA($close, 26))/$close, 9)/$close'] # MACD
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>>> names = ['MACD']
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>>> labels = ['Ref($vwap, -2)/Ref($vwap, -1) - 1'] # 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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SH600004 2012-01-04 -0.030853
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2012-01-05 -0.030452
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2012-01-06 -0.028252
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2012-01-09 -0.024507
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2012-01-10 -0.019744
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... ...
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SZ300273 2014-12-25 0.031339
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2014-12-26 0.029695
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2014-12-29 0.025577
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2014-12-30 0.020493
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2014-12-31 0.017089
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[605882 rows x 1 columns]
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label
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instrument datetime
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SH600004 2012-01-04 0.003021
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2012-01-05 0.017434
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2012-01-06 0.015490
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2012-01-09 0.002324
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2012-01-10 -0.002542
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... ...
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SZ300273 2014-12-25 -0.032454
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2014-12-26 -0.016638
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2014-12-29 0.008263
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2014-12-30 -0.011985
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2014-12-31 0.047797
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[605882 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>`_ |