Building Formulaic Alphas¶
Introduction¶
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.
A formulaic alpha, as the name suggests, is a kind of alpha that can be presented as a formula or a mathematical expression.
Building Formulaic Alphas in Qlib
¶
In Qlib
, users can easily build formulaic alphas.
Example¶
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.
MACD can be presented as the following formula:
Note
DIF means Differential value, which is 12-period EMA minus 26-period EMA.
`DEA`means a 9-period EMA of the DIF.
Users can use Data Handler
to build formulaic alphas MACD in qlib:
Note
Users need to initialize Qlib
with qlib.init first. Please refer to initialization.
>>> from qlib.contrib.estimator.handler import QLibDataHandler
>>> fields = ['(EMA($close, 12) - EMA($close, 26))/$close - EMA((EMA($close, 12) - EMA($close, 26))/$close, 9)/$close'] # MACD
>>> names = ['MACD']
>>> labels = ['Ref($vwap, -2)/Ref($vwap, -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",
... }
>>> 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
SH600004 2012-01-04 -0.030853
2012-01-05 -0.030452
2012-01-06 -0.028252
2012-01-09 -0.024507
2012-01-10 -0.019744
... ...
SZ300273 2014-12-25 0.031339
2014-12-26 0.029695
2014-12-29 0.025577
2014-12-30 0.020493
2014-12-31 0.017089
[605882 rows x 1 columns]
label
instrument datetime
SH600004 2012-01-04 0.003021
2012-01-05 0.017434
2012-01-06 0.015490
2012-01-09 0.002324
2012-01-10 -0.002542
... ...
SZ300273 2014-12-25 -0.032454
2014-12-26 -0.016638
2014-12-29 0.008263
2014-12-30 -0.011985
2014-12-31 0.047797
[605882 rows x 1 columns]
Reference¶
To kown more about Data Handler
, please refer to Data Handler
To kown more about Data Api
, please refer to Data Api