mirror of
https://github.com/microsoft/qlib.git
synced 2026-07-01 18:11:18 +08:00
3db22452fb
* add ChangeInstrument to ops Adding Change instrument OP. This op allows one to use features of a different instrument. * Update __init__.py update parse_field to accommodate ChangeInstrument * Propose test * Add test case and fix bug * Update ops.py * Update ops.py * simplify the operator further * implement abstract method * fix arg bug * clean test Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
1683 lines
44 KiB
Python
1683 lines
44 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import pandas as pd
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from typing import Union, List, Type
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from scipy.stats import percentileofscore
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from .base import Expression, ExpressionOps, Feature, PFeature
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from ..log import get_module_logger
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from ..utils import get_callable_kwargs
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try:
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from ._libs.rolling import rolling_slope, rolling_rsquare, rolling_resi
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from ._libs.expanding import expanding_slope, expanding_rsquare, expanding_resi
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except ImportError:
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print(
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"#### Do not import qlib package in the repository directory in case of importing qlib from . without compiling #####"
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)
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raise
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except ValueError:
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print("!!!!!!!! A error occurs when importing operators implemented based on Cython.!!!!!!!!")
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print("!!!!!!!! They will be disabled. Please Upgrade your numpy to enable them !!!!!!!!")
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# We catch this error because some platform can't upgrade there package (e.g. Kaggle)
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# https://www.kaggle.com/general/293387
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# https://www.kaggle.com/product-feedback/98562
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np.seterr(invalid="ignore")
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#################### Element-Wise Operator ####################
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class ElemOperator(ExpressionOps):
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"""Element-wise Operator
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Parameters
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----------
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feature : Expression
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feature instance
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Returns
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----------
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Expression
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feature operation output
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"""
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def __init__(self, feature):
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self.feature = feature
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def __str__(self):
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return "{}({})".format(type(self).__name__, self.feature)
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def get_longest_back_rolling(self):
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return self.feature.get_longest_back_rolling()
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def get_extended_window_size(self):
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return self.feature.get_extended_window_size()
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class ChangeInstrument(ElemOperator):
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"""Change Instrument Operator
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In some case, one may want to change to another instrument when calculating, for example, to
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calculate beta of a stock with respect to a market index.
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This would require changing the calculation of features from the stock (original instrument) to
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the index (reference instrument)
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Parameters
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----------
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instrument: new instrument for which the downstream operations should be performed upon.
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i.e., SH000300 (CSI300 index), or ^GPSC (SP500 index).
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feature: the feature to be calculated for the new instrument.
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Returns
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----------
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Expression
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feature operation output
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"""
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def __init__(self, instrument, feature):
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self.instrument = instrument
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self.feature = feature
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def __str__(self):
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return "{}('{}',{})".format(type(self).__name__, self.instrument, self.feature)
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def load(self, instrument, start_index, end_index, *args):
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# the first `instrument` is ignored
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return super().load(self.instrument, start_index, end_index, *args)
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def _load_internal(self, instrument, start_index, end_index, *args):
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return self.feature.load(instrument, start_index, end_index, *args)
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class NpElemOperator(ElemOperator):
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"""Numpy Element-wise Operator
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Parameters
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----------
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feature : Expression
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feature instance
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func : str
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numpy feature operation method
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Returns
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----------
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Expression
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feature operation output
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"""
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def __init__(self, feature, func):
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self.func = func
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super(NpElemOperator, self).__init__(feature)
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def _load_internal(self, instrument, start_index, end_index, *args):
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series = self.feature.load(instrument, start_index, end_index, *args)
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return getattr(np, self.func)(series)
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class Abs(NpElemOperator):
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"""Feature Absolute Value
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Parameters
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----------
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feature : Expression
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feature instance
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Returns
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----------
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Expression
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a feature instance with absolute output
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"""
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def __init__(self, feature):
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super(Abs, self).__init__(feature, "abs")
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class Sign(NpElemOperator):
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"""Feature Sign
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Parameters
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----------
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feature : Expression
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feature instance
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Returns
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----------
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Expression
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a feature instance with sign
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"""
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def __init__(self, feature):
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super(Sign, self).__init__(feature, "sign")
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def _load_internal(self, instrument, start_index, end_index, *args):
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"""
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To avoid error raised by bool type input, we transform the data into float32.
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"""
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series = self.feature.load(instrument, start_index, end_index, *args)
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# TODO: More precision types should be configurable
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series = series.astype(np.float32)
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return getattr(np, self.func)(series)
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class Log(NpElemOperator):
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"""Feature Log
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Parameters
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----------
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feature : Expression
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feature instance
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Returns
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----------
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Expression
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a feature instance with log
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"""
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def __init__(self, feature):
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super(Log, self).__init__(feature, "log")
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class Mask(NpElemOperator):
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"""Feature Mask
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Parameters
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----------
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feature : Expression
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feature instance
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instrument : str
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instrument mask
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Returns
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----------
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Expression
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a feature instance with masked instrument
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"""
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def __init__(self, feature, instrument):
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super(Mask, self).__init__(feature, "mask")
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self.instrument = instrument
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def __str__(self):
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return "{}({},{})".format(type(self).__name__, self.feature, self.instrument.lower())
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def _load_internal(self, instrument, start_index, end_index, *args):
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return self.feature.load(self.instrument, start_index, end_index, *args)
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class Not(NpElemOperator):
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"""Not Operator
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Parameters
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----------
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feature_left : Expression
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feature instance
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feature_right : Expression
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feature instance
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Returns
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----------
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Feature:
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feature elementwise not output
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"""
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def __init__(self, feature):
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super(Not, self).__init__(feature, "bitwise_not")
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#################### Pair-Wise Operator ####################
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class PairOperator(ExpressionOps):
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"""Pair-wise operator
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Parameters
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----------
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feature_left : Expression
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feature instance or numeric value
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feature_right : Expression
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feature instance or numeric value
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func : str
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operator function
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Returns
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----------
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Feature:
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two features' operation output
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"""
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def __init__(self, feature_left, feature_right):
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self.feature_left = feature_left
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self.feature_right = feature_right
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def __str__(self):
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return "{}({},{})".format(type(self).__name__, self.feature_left, self.feature_right)
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def get_longest_back_rolling(self):
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if isinstance(self.feature_left, (Expression,)):
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left_br = self.feature_left.get_longest_back_rolling()
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else:
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left_br = 0
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if isinstance(self.feature_right, (Expression,)):
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right_br = self.feature_right.get_longest_back_rolling()
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else:
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right_br = 0
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return max(left_br, right_br)
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def get_extended_window_size(self):
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if isinstance(self.feature_left, (Expression,)):
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ll, lr = self.feature_left.get_extended_window_size()
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else:
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ll, lr = 0, 0
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if isinstance(self.feature_right, (Expression,)):
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rl, rr = self.feature_right.get_extended_window_size()
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else:
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rl, rr = 0, 0
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return max(ll, rl), max(lr, rr)
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class NpPairOperator(PairOperator):
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"""Numpy Pair-wise operator
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Parameters
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----------
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feature_left : Expression
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feature instance or numeric value
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feature_right : Expression
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feature instance or numeric value
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func : str
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operator function
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Returns
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----------
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Feature:
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two features' operation output
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"""
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def __init__(self, feature_left, feature_right, func):
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self.func = func
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super(NpPairOperator, self).__init__(feature_left, feature_right)
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def _load_internal(self, instrument, start_index, end_index, *args):
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assert any(
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[isinstance(self.feature_left, (Expression,)), self.feature_right, Expression]
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), "at least one of two inputs is Expression instance"
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if isinstance(self.feature_left, (Expression,)):
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series_left = self.feature_left.load(instrument, start_index, end_index, *args)
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else:
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series_left = self.feature_left # numeric value
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if isinstance(self.feature_right, (Expression,)):
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series_right = self.feature_right.load(instrument, start_index, end_index, *args)
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else:
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series_right = self.feature_right
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check_length = isinstance(series_left, (np.ndarray, pd.Series)) and isinstance(
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series_right, (np.ndarray, pd.Series)
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)
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if check_length:
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warning_info = (
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f"Loading {instrument}: {str(self)}; np.{self.func}(series_left, series_right), "
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f"The length of series_left and series_right is different: ({len(series_left)}, {len(series_right)}), "
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f"series_left is {str(self.feature_left)}, series_right is {str(self.feature_right)}. Please check the data"
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)
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else:
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warning_info = (
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f"Loading {instrument}: {str(self)}; np.{self.func}(series_left, series_right), "
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f"series_left is {str(self.feature_left)}, series_right is {str(self.feature_right)}. Please check the data"
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)
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try:
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res = getattr(np, self.func)(series_left, series_right)
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except ValueError as e:
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get_module_logger("ops").debug(warning_info)
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raise ValueError(f"{str(e)}. \n\t{warning_info}") from e
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else:
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if check_length and len(series_left) != len(series_right):
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get_module_logger("ops").debug(warning_info)
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return res
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class Power(NpPairOperator):
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"""Power Operator
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Parameters
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----------
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feature_left : Expression
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feature instance
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feature_right : Expression
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feature instance
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Returns
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----------
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Feature:
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The bases in feature_left raised to the exponents in feature_right
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"""
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def __init__(self, feature_left, feature_right):
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super(Power, self).__init__(feature_left, feature_right, "power")
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class Add(NpPairOperator):
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"""Add Operator
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Parameters
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----------
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feature_left : Expression
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feature instance
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feature_right : Expression
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feature instance
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Returns
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----------
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Feature:
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two features' sum
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"""
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def __init__(self, feature_left, feature_right):
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super(Add, self).__init__(feature_left, feature_right, "add")
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class Sub(NpPairOperator):
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"""Subtract Operator
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Parameters
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----------
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feature_left : Expression
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feature instance
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feature_right : Expression
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feature instance
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Returns
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----------
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Feature:
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two features' subtraction
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"""
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def __init__(self, feature_left, feature_right):
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super(Sub, self).__init__(feature_left, feature_right, "subtract")
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class Mul(NpPairOperator):
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"""Multiply Operator
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Parameters
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----------
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feature_left : Expression
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feature instance
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feature_right : Expression
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feature instance
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Returns
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----------
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Feature:
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two features' product
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"""
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def __init__(self, feature_left, feature_right):
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super(Mul, self).__init__(feature_left, feature_right, "multiply")
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class Div(NpPairOperator):
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"""Division Operator
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Parameters
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----------
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feature_left : Expression
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feature instance
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feature_right : Expression
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feature instance
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Returns
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----------
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Feature:
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two features' division
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"""
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def __init__(self, feature_left, feature_right):
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super(Div, self).__init__(feature_left, feature_right, "divide")
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class Greater(NpPairOperator):
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"""Greater Operator
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Parameters
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----------
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feature_left : Expression
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feature instance
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feature_right : Expression
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feature instance
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Returns
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----------
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Feature:
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greater elements taken from the input two features
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"""
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def __init__(self, feature_left, feature_right):
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super(Greater, self).__init__(feature_left, feature_right, "maximum")
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class Less(NpPairOperator):
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"""Less Operator
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Parameters
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----------
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feature_left : Expression
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feature instance
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feature_right : Expression
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feature instance
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Returns
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----------
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Feature:
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smaller elements taken from the input two features
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"""
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def __init__(self, feature_left, feature_right):
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super(Less, self).__init__(feature_left, feature_right, "minimum")
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class Gt(NpPairOperator):
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"""Greater Than Operator
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Parameters
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----------
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feature_left : Expression
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feature instance
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feature_right : Expression
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feature instance
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Returns
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----------
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Feature:
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bool series indicate `left > right`
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"""
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def __init__(self, feature_left, feature_right):
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super(Gt, self).__init__(feature_left, feature_right, "greater")
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class Ge(NpPairOperator):
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"""Greater Equal Than Operator
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Parameters
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----------
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feature_left : Expression
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feature instance
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feature_right : Expression
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feature instance
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Returns
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----------
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Feature:
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bool series indicate `left >= right`
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"""
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def __init__(self, feature_left, feature_right):
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super(Ge, self).__init__(feature_left, feature_right, "greater_equal")
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class Lt(NpPairOperator):
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"""Less Than Operator
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Parameters
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----------
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feature_left : Expression
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feature instance
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feature_right : Expression
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feature instance
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Returns
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----------
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Feature:
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bool series indicate `left < right`
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"""
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def __init__(self, feature_left, feature_right):
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super(Lt, self).__init__(feature_left, feature_right, "less")
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class Le(NpPairOperator):
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"""Less Equal Than Operator
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Parameters
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----------
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feature_left : Expression
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feature instance
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feature_right : Expression
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feature instance
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Returns
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----------
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Feature:
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bool series indicate `left <= right`
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"""
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def __init__(self, feature_left, feature_right):
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super(Le, self).__init__(feature_left, feature_right, "less_equal")
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class Eq(NpPairOperator):
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"""Equal Operator
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Parameters
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----------
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feature_left : Expression
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feature instance
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feature_right : Expression
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feature instance
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Returns
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----------
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Feature:
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bool series indicate `left == right`
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"""
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def __init__(self, feature_left, feature_right):
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super(Eq, self).__init__(feature_left, feature_right, "equal")
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class Ne(NpPairOperator):
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"""Not Equal Operator
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Parameters
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----------
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feature_left : Expression
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feature instance
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feature_right : Expression
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feature instance
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Returns
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----------
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Feature:
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bool series indicate `left != right`
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"""
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def __init__(self, feature_left, feature_right):
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super(Ne, self).__init__(feature_left, feature_right, "not_equal")
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class And(NpPairOperator):
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"""And Operator
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Parameters
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----------
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feature_left : Expression
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feature instance
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feature_right : Expression
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feature instance
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Returns
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----------
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Feature:
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two features' row by row & output
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"""
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def __init__(self, feature_left, feature_right):
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super(And, self).__init__(feature_left, feature_right, "bitwise_and")
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class Or(NpPairOperator):
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"""Or Operator
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Parameters
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----------
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feature_left : Expression
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feature instance
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feature_right : Expression
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feature instance
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Returns
|
|
----------
|
|
Feature:
|
|
two features' row by row | outputs
|
|
"""
|
|
|
|
def __init__(self, feature_left, feature_right):
|
|
super(Or, self).__init__(feature_left, feature_right, "bitwise_or")
|
|
|
|
|
|
#################### Triple-wise Operator ####################
|
|
class If(ExpressionOps):
|
|
"""If Operator
|
|
|
|
Parameters
|
|
----------
|
|
condition : Expression
|
|
feature instance with bool values as condition
|
|
feature_left : Expression
|
|
feature instance
|
|
feature_right : Expression
|
|
feature instance
|
|
"""
|
|
|
|
def __init__(self, condition, feature_left, feature_right):
|
|
self.condition = condition
|
|
self.feature_left = feature_left
|
|
self.feature_right = feature_right
|
|
|
|
def __str__(self):
|
|
return "If({},{},{})".format(self.condition, self.feature_left, self.feature_right)
|
|
|
|
def _load_internal(self, instrument, start_index, end_index, *args):
|
|
series_cond = self.condition.load(instrument, start_index, end_index, *args)
|
|
if isinstance(self.feature_left, (Expression,)):
|
|
series_left = self.feature_left.load(instrument, start_index, end_index, *args)
|
|
else:
|
|
series_left = self.feature_left
|
|
if isinstance(self.feature_right, (Expression,)):
|
|
series_right = self.feature_right.load(instrument, start_index, end_index, *args)
|
|
else:
|
|
series_right = self.feature_right
|
|
series = pd.Series(np.where(series_cond, series_left, series_right), index=series_cond.index)
|
|
return series
|
|
|
|
def get_longest_back_rolling(self):
|
|
if isinstance(self.feature_left, (Expression,)):
|
|
left_br = self.feature_left.get_longest_back_rolling()
|
|
else:
|
|
left_br = 0
|
|
|
|
if isinstance(self.feature_right, (Expression,)):
|
|
right_br = self.feature_right.get_longest_back_rolling()
|
|
else:
|
|
right_br = 0
|
|
|
|
if isinstance(self.condition, (Expression,)):
|
|
c_br = self.condition.get_longest_back_rolling()
|
|
else:
|
|
c_br = 0
|
|
return max(left_br, right_br, c_br)
|
|
|
|
def get_extended_window_size(self):
|
|
if isinstance(self.feature_left, (Expression,)):
|
|
ll, lr = self.feature_left.get_extended_window_size()
|
|
else:
|
|
ll, lr = 0, 0
|
|
|
|
if isinstance(self.feature_right, (Expression,)):
|
|
rl, rr = self.feature_right.get_extended_window_size()
|
|
else:
|
|
rl, rr = 0, 0
|
|
|
|
if isinstance(self.condition, (Expression,)):
|
|
cl, cr = self.condition.get_extended_window_size()
|
|
else:
|
|
cl, cr = 0, 0
|
|
return max(ll, rl, cl), max(lr, rr, cr)
|
|
|
|
|
|
#################### Rolling ####################
|
|
# NOTE: methods like `rolling.mean` are optimized with cython,
|
|
# and are super faster than `rolling.apply(np.mean)`
|
|
|
|
|
|
class Rolling(ExpressionOps):
|
|
"""Rolling Operator
|
|
The meaning of rolling and expanding is the same in pandas.
|
|
When the window is set to 0, the behaviour of the operator should follow `expanding`
|
|
Otherwise, it follows `rolling`
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
func : str
|
|
rolling method
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
rolling outputs
|
|
"""
|
|
|
|
def __init__(self, feature, N, func):
|
|
self.feature = feature
|
|
self.N = N
|
|
self.func = func
|
|
|
|
def __str__(self):
|
|
return "{}({},{})".format(type(self).__name__, self.feature, self.N)
|
|
|
|
def _load_internal(self, instrument, start_index, end_index, *args):
|
|
series = self.feature.load(instrument, start_index, end_index, *args)
|
|
# NOTE: remove all null check,
|
|
# now it's user's responsibility to decide whether use features in null days
|
|
# isnull = series.isnull() # NOTE: isnull = NaN, inf is not null
|
|
if isinstance(self.N, int) and self.N == 0:
|
|
series = getattr(series.expanding(min_periods=1), self.func)()
|
|
elif isinstance(self.N, float) and 0 < self.N < 1:
|
|
series = series.ewm(alpha=self.N, min_periods=1).mean()
|
|
else:
|
|
series = getattr(series.rolling(self.N, min_periods=1), self.func)()
|
|
# series.iloc[:self.N-1] = np.nan
|
|
# series[isnull] = np.nan
|
|
return series
|
|
|
|
def get_longest_back_rolling(self):
|
|
if self.N == 0:
|
|
return np.inf
|
|
if 0 < self.N < 1:
|
|
return int(np.log(1e-6) / np.log(1 - self.N)) # (1 - N)**window == 1e-6
|
|
return self.feature.get_longest_back_rolling() + self.N - 1
|
|
|
|
def get_extended_window_size(self):
|
|
if self.N == 0:
|
|
# FIXME: How to make this accurate and efficiently? Or should we
|
|
# remove such support for N == 0?
|
|
get_module_logger(self.__class__.__name__).warning("The Rolling(ATTR, 0) will not be accurately calculated")
|
|
return self.feature.get_extended_window_size()
|
|
elif 0 < self.N < 1:
|
|
lft_etd, rght_etd = self.feature.get_extended_window_size()
|
|
size = int(np.log(1e-6) / np.log(1 - self.N))
|
|
lft_etd = max(lft_etd + size - 1, lft_etd)
|
|
return lft_etd, rght_etd
|
|
else:
|
|
lft_etd, rght_etd = self.feature.get_extended_window_size()
|
|
lft_etd = max(lft_etd + self.N - 1, lft_etd)
|
|
return lft_etd, rght_etd
|
|
|
|
|
|
class Ref(Rolling):
|
|
"""Feature Reference
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
N = 0, retrieve the first data; N > 0, retrieve data of N periods ago; N < 0, future data
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with target reference
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
super(Ref, self).__init__(feature, N, "ref")
|
|
|
|
def _load_internal(self, instrument, start_index, end_index, *args):
|
|
series = self.feature.load(instrument, start_index, end_index, *args)
|
|
# N = 0, return first day
|
|
if series.empty:
|
|
return series # Pandas bug, see: https://github.com/pandas-dev/pandas/issues/21049
|
|
elif self.N == 0:
|
|
series = pd.Series(series.iloc[0], index=series.index)
|
|
else:
|
|
series = series.shift(self.N) # copy
|
|
return series
|
|
|
|
def get_longest_back_rolling(self):
|
|
if self.N == 0:
|
|
return np.inf
|
|
return self.feature.get_longest_back_rolling() + self.N
|
|
|
|
def get_extended_window_size(self):
|
|
if self.N == 0:
|
|
get_module_logger(self.__class__.__name__).warning("The Ref(ATTR, 0) will not be accurately calculated")
|
|
return self.feature.get_extended_window_size()
|
|
else:
|
|
lft_etd, rght_etd = self.feature.get_extended_window_size()
|
|
lft_etd = max(lft_etd + self.N, lft_etd)
|
|
rght_etd = max(rght_etd - self.N, rght_etd)
|
|
return lft_etd, rght_etd
|
|
|
|
|
|
class Mean(Rolling):
|
|
"""Rolling Mean (MA)
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with rolling average
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
super(Mean, self).__init__(feature, N, "mean")
|
|
|
|
|
|
class Sum(Rolling):
|
|
"""Rolling Sum
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with rolling sum
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
super(Sum, self).__init__(feature, N, "sum")
|
|
|
|
|
|
class Std(Rolling):
|
|
"""Rolling Std
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with rolling std
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
super(Std, self).__init__(feature, N, "std")
|
|
|
|
|
|
class Var(Rolling):
|
|
"""Rolling Variance
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with rolling variance
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
super(Var, self).__init__(feature, N, "var")
|
|
|
|
|
|
class Skew(Rolling):
|
|
"""Rolling Skewness
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with rolling skewness
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
if N != 0 and N < 3:
|
|
raise ValueError("The rolling window size of Skewness operation should >= 3")
|
|
super(Skew, self).__init__(feature, N, "skew")
|
|
|
|
|
|
class Kurt(Rolling):
|
|
"""Rolling Kurtosis
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with rolling kurtosis
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
if N != 0 and N < 4:
|
|
raise ValueError("The rolling window size of Kurtosis operation should >= 5")
|
|
super(Kurt, self).__init__(feature, N, "kurt")
|
|
|
|
|
|
class Max(Rolling):
|
|
"""Rolling Max
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with rolling max
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
super(Max, self).__init__(feature, N, "max")
|
|
|
|
|
|
class IdxMax(Rolling):
|
|
"""Rolling Max Index
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with rolling max index
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
super(IdxMax, self).__init__(feature, N, "idxmax")
|
|
|
|
def _load_internal(self, instrument, start_index, end_index, *args):
|
|
series = self.feature.load(instrument, start_index, end_index, *args)
|
|
if self.N == 0:
|
|
series = series.expanding(min_periods=1).apply(lambda x: x.argmax() + 1, raw=True)
|
|
else:
|
|
series = series.rolling(self.N, min_periods=1).apply(lambda x: x.argmax() + 1, raw=True)
|
|
return series
|
|
|
|
|
|
class Min(Rolling):
|
|
"""Rolling Min
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with rolling min
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
super(Min, self).__init__(feature, N, "min")
|
|
|
|
|
|
class IdxMin(Rolling):
|
|
"""Rolling Min Index
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with rolling min index
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
super(IdxMin, self).__init__(feature, N, "idxmin")
|
|
|
|
def _load_internal(self, instrument, start_index, end_index, *args):
|
|
series = self.feature.load(instrument, start_index, end_index, *args)
|
|
if self.N == 0:
|
|
series = series.expanding(min_periods=1).apply(lambda x: x.argmin() + 1, raw=True)
|
|
else:
|
|
series = series.rolling(self.N, min_periods=1).apply(lambda x: x.argmin() + 1, raw=True)
|
|
return series
|
|
|
|
|
|
class Quantile(Rolling):
|
|
"""Rolling Quantile
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with rolling quantile
|
|
"""
|
|
|
|
def __init__(self, feature, N, qscore):
|
|
super(Quantile, self).__init__(feature, N, "quantile")
|
|
self.qscore = qscore
|
|
|
|
def __str__(self):
|
|
return "{}({},{},{})".format(type(self).__name__, self.feature, self.N, self.qscore)
|
|
|
|
def _load_internal(self, instrument, start_index, end_index, *args):
|
|
series = self.feature.load(instrument, start_index, end_index, *args)
|
|
if self.N == 0:
|
|
series = series.expanding(min_periods=1).quantile(self.qscore)
|
|
else:
|
|
series = series.rolling(self.N, min_periods=1).quantile(self.qscore)
|
|
return series
|
|
|
|
|
|
class Med(Rolling):
|
|
"""Rolling Median
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with rolling median
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
super(Med, self).__init__(feature, N, "median")
|
|
|
|
|
|
class Mad(Rolling):
|
|
"""Rolling Mean Absolute Deviation
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with rolling mean absolute deviation
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
super(Mad, self).__init__(feature, N, "mad")
|
|
|
|
def _load_internal(self, instrument, start_index, end_index, *args):
|
|
series = self.feature.load(instrument, start_index, end_index, *args)
|
|
# TODO: implement in Cython
|
|
|
|
def mad(x):
|
|
x1 = x[~np.isnan(x)]
|
|
return np.mean(np.abs(x1 - x1.mean()))
|
|
|
|
if self.N == 0:
|
|
series = series.expanding(min_periods=1).apply(mad, raw=True)
|
|
else:
|
|
series = series.rolling(self.N, min_periods=1).apply(mad, raw=True)
|
|
return series
|
|
|
|
|
|
class Rank(Rolling):
|
|
"""Rolling Rank (Percentile)
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with rolling rank
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
super(Rank, self).__init__(feature, N, "rank")
|
|
|
|
def _load_internal(self, instrument, start_index, end_index, *args):
|
|
series = self.feature.load(instrument, start_index, end_index, *args)
|
|
# TODO: implement in Cython
|
|
|
|
def rank(x):
|
|
if np.isnan(x[-1]):
|
|
return np.nan
|
|
x1 = x[~np.isnan(x)]
|
|
if x1.shape[0] == 0:
|
|
return np.nan
|
|
return percentileofscore(x1, x1[-1]) / len(x1)
|
|
|
|
if self.N == 0:
|
|
series = series.expanding(min_periods=1).apply(rank, raw=True)
|
|
else:
|
|
series = series.rolling(self.N, min_periods=1).apply(rank, raw=True)
|
|
return series
|
|
|
|
|
|
class Count(Rolling):
|
|
"""Rolling Count
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with rolling count of number of non-NaN elements
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
super(Count, self).__init__(feature, N, "count")
|
|
|
|
|
|
class Delta(Rolling):
|
|
"""Rolling Delta
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with end minus start in rolling window
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
super(Delta, self).__init__(feature, N, "delta")
|
|
|
|
def _load_internal(self, instrument, start_index, end_index, *args):
|
|
series = self.feature.load(instrument, start_index, end_index, *args)
|
|
if self.N == 0:
|
|
series = series - series.iloc[0]
|
|
else:
|
|
series = series - series.shift(self.N)
|
|
return series
|
|
|
|
|
|
# TODO:
|
|
# support pair-wise rolling like `Slope(A, B, N)`
|
|
class Slope(Rolling):
|
|
"""Rolling Slope
|
|
This operator calculate the slope between `idx` and `feature`.
|
|
(e.g. [<feature_t1>, <feature_t2>, <feature_t3>] and [1, 2, 3])
|
|
|
|
Usage Example:
|
|
- "Slope($close, %d)/$close"
|
|
|
|
# TODO:
|
|
# Some users may want pair-wise rolling like `Slope(A, B, N)`
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with linear regression slope of given window
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
super(Slope, self).__init__(feature, N, "slope")
|
|
|
|
def _load_internal(self, instrument, start_index, end_index, *args):
|
|
series = self.feature.load(instrument, start_index, end_index, *args)
|
|
if self.N == 0:
|
|
series = pd.Series(expanding_slope(series.values), index=series.index)
|
|
else:
|
|
series = pd.Series(rolling_slope(series.values, self.N), index=series.index)
|
|
return series
|
|
|
|
|
|
class Rsquare(Rolling):
|
|
"""Rolling R-value Square
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with linear regression r-value square of given window
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
super(Rsquare, self).__init__(feature, N, "rsquare")
|
|
|
|
def _load_internal(self, instrument, start_index, end_index, *args):
|
|
_series = self.feature.load(instrument, start_index, end_index, *args)
|
|
if self.N == 0:
|
|
series = pd.Series(expanding_rsquare(_series.values), index=_series.index)
|
|
else:
|
|
series = pd.Series(rolling_rsquare(_series.values, self.N), index=_series.index)
|
|
series.loc[np.isclose(_series.rolling(self.N, min_periods=1).std(), 0, atol=2e-05)] = np.nan
|
|
return series
|
|
|
|
|
|
class Resi(Rolling):
|
|
"""Rolling Regression Residuals
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with regression residuals of given window
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
super(Resi, self).__init__(feature, N, "resi")
|
|
|
|
def _load_internal(self, instrument, start_index, end_index, *args):
|
|
series = self.feature.load(instrument, start_index, end_index, *args)
|
|
if self.N == 0:
|
|
series = pd.Series(expanding_resi(series.values), index=series.index)
|
|
else:
|
|
series = pd.Series(rolling_resi(series.values, self.N), index=series.index)
|
|
return series
|
|
|
|
|
|
class WMA(Rolling):
|
|
"""Rolling WMA
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with weighted moving average output
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
super(WMA, self).__init__(feature, N, "wma")
|
|
|
|
def _load_internal(self, instrument, start_index, end_index, *args):
|
|
series = self.feature.load(instrument, start_index, end_index, *args)
|
|
# TODO: implement in Cython
|
|
|
|
def weighted_mean(x):
|
|
w = np.arange(len(x))
|
|
w = w / w.sum()
|
|
return np.nanmean(w * x)
|
|
|
|
if self.N == 0:
|
|
series = series.expanding(min_periods=1).apply(weighted_mean, raw=True)
|
|
else:
|
|
series = series.rolling(self.N, min_periods=1).apply(weighted_mean, raw=True)
|
|
return series
|
|
|
|
|
|
class EMA(Rolling):
|
|
"""Rolling Exponential Mean (EMA)
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
feature instance
|
|
N : int, float
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with regression r-value square of given window
|
|
"""
|
|
|
|
def __init__(self, feature, N):
|
|
super(EMA, self).__init__(feature, N, "ema")
|
|
|
|
def _load_internal(self, instrument, start_index, end_index, *args):
|
|
series = self.feature.load(instrument, start_index, end_index, *args)
|
|
|
|
def exp_weighted_mean(x):
|
|
a = 1 - 2 / (1 + len(x))
|
|
w = a ** np.arange(len(x))[::-1]
|
|
w /= w.sum()
|
|
return np.nansum(w * x)
|
|
|
|
if self.N == 0:
|
|
series = series.expanding(min_periods=1).apply(exp_weighted_mean, raw=True)
|
|
elif 0 < self.N < 1:
|
|
series = series.ewm(alpha=self.N, min_periods=1).mean()
|
|
else:
|
|
series = series.ewm(span=self.N, min_periods=1).mean()
|
|
return series
|
|
|
|
|
|
#################### Pair-Wise Rolling ####################
|
|
class PairRolling(ExpressionOps):
|
|
"""Pair Rolling Operator
|
|
|
|
Parameters
|
|
----------
|
|
feature_left : Expression
|
|
feature instance
|
|
feature_right : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with rolling output of two input features
|
|
"""
|
|
|
|
def __init__(self, feature_left, feature_right, N, func):
|
|
# TODO: in what case will a const be passed into `__init__` as `feature_left` or `feature_right`
|
|
self.feature_left = feature_left
|
|
self.feature_right = feature_right
|
|
self.N = N
|
|
self.func = func
|
|
|
|
def __str__(self):
|
|
return "{}({},{},{})".format(type(self).__name__, self.feature_left, self.feature_right, self.N)
|
|
|
|
def _load_internal(self, instrument, start_index, end_index, *args):
|
|
assert any(
|
|
[isinstance(self.feature_left, Expression), self.feature_right, Expression]
|
|
), "at least one of two inputs is Expression instance"
|
|
|
|
if isinstance(self.feature_left, Expression):
|
|
series_left = self.feature_left.load(instrument, start_index, end_index, *args)
|
|
else:
|
|
series_left = self.feature_left # numeric value
|
|
if isinstance(self.feature_right, Expression):
|
|
series_right = self.feature_right.load(instrument, start_index, end_index, *args)
|
|
else:
|
|
series_right = self.feature_right
|
|
|
|
if self.N == 0:
|
|
series = getattr(series_left.expanding(min_periods=1), self.func)(series_right)
|
|
else:
|
|
series = getattr(series_left.rolling(self.N, min_periods=1), self.func)(series_right)
|
|
return series
|
|
|
|
def get_longest_back_rolling(self):
|
|
if self.N == 0:
|
|
return np.inf
|
|
if isinstance(self.feature_left, Expression):
|
|
left_br = self.feature_left.get_longest_back_rolling()
|
|
else:
|
|
left_br = 0
|
|
|
|
if isinstance(self.feature_right, Expression):
|
|
right_br = self.feature_right.get_longest_back_rolling()
|
|
else:
|
|
right_br = 0
|
|
return max(left_br, right_br)
|
|
|
|
def get_extended_window_size(self):
|
|
if isinstance(self.feature_left, Expression):
|
|
ll, lr = self.feature_left.get_extended_window_size()
|
|
else:
|
|
ll, lr = 0, 0
|
|
if isinstance(self.feature_right, Expression):
|
|
rl, rr = self.feature_right.get_extended_window_size()
|
|
else:
|
|
rl, rr = 0, 0
|
|
if self.N == 0:
|
|
get_module_logger(self.__class__.__name__).warning(
|
|
"The PairRolling(ATTR, 0) will not be accurately calculated"
|
|
)
|
|
return -np.inf, max(lr, rr)
|
|
else:
|
|
return max(ll, rl) + self.N - 1, max(lr, rr)
|
|
|
|
|
|
class Corr(PairRolling):
|
|
"""Rolling Correlation
|
|
|
|
Parameters
|
|
----------
|
|
feature_left : Expression
|
|
feature instance
|
|
feature_right : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with rolling correlation of two input features
|
|
"""
|
|
|
|
def __init__(self, feature_left, feature_right, N):
|
|
super(Corr, self).__init__(feature_left, feature_right, N, "corr")
|
|
|
|
def _load_internal(self, instrument, start_index, end_index, *args):
|
|
res: pd.Series = super(Corr, self)._load_internal(instrument, start_index, end_index, *args)
|
|
|
|
# NOTE: Load uses MemCache, so calling load again will not cause performance degradation
|
|
series_left = self.feature_left.load(instrument, start_index, end_index, *args)
|
|
series_right = self.feature_right.load(instrument, start_index, end_index, *args)
|
|
res.loc[
|
|
np.isclose(series_left.rolling(self.N, min_periods=1).std(), 0, atol=2e-05)
|
|
| np.isclose(series_right.rolling(self.N, min_periods=1).std(), 0, atol=2e-05)
|
|
] = np.nan
|
|
return res
|
|
|
|
|
|
class Cov(PairRolling):
|
|
"""Rolling Covariance
|
|
|
|
Parameters
|
|
----------
|
|
feature_left : Expression
|
|
feature instance
|
|
feature_right : Expression
|
|
feature instance
|
|
N : int
|
|
rolling window size
|
|
|
|
Returns
|
|
----------
|
|
Expression
|
|
a feature instance with rolling max of two input features
|
|
"""
|
|
|
|
def __init__(self, feature_left, feature_right, N):
|
|
super(Cov, self).__init__(feature_left, feature_right, N, "cov")
|
|
|
|
|
|
#################### Operator which only support data with time index ####################
|
|
# Convention
|
|
# - The name of the operators in this section will start with "T"
|
|
|
|
|
|
class TResample(ElemOperator):
|
|
def __init__(self, feature, freq, func):
|
|
"""
|
|
Resampling the data to target frequency.
|
|
The resample function of pandas is used.
|
|
- the timestamp will be at the start of the time span after resample.
|
|
|
|
Parameters
|
|
----------
|
|
feature : Expression
|
|
An expression for calculating the feature
|
|
freq : str
|
|
It will be passed into the resample method for resampling basedn on given frequency
|
|
func : method
|
|
The method to get the resampled values
|
|
Some expression are high frequently used
|
|
"""
|
|
self.feature = feature
|
|
self.freq = freq
|
|
self.func = func
|
|
|
|
def __str__(self):
|
|
return "{}({},{})".format(type(self).__name__, self.feature, self.freq)
|
|
|
|
def _load_internal(self, instrument, start_index, end_index, *args):
|
|
series = self.feature.load(instrument, start_index, end_index, *args)
|
|
|
|
if series.empty:
|
|
return series
|
|
else:
|
|
if self.func == "sum":
|
|
return getattr(series.resample(self.freq), self.func)(min_count=1)
|
|
else:
|
|
return getattr(series.resample(self.freq), self.func)()
|
|
|
|
|
|
TOpsList = [TResample]
|
|
OpsList = [
|
|
ChangeInstrument,
|
|
Rolling,
|
|
Ref,
|
|
Max,
|
|
Min,
|
|
Sum,
|
|
Mean,
|
|
Std,
|
|
Var,
|
|
Skew,
|
|
Kurt,
|
|
Med,
|
|
Mad,
|
|
Slope,
|
|
Rsquare,
|
|
Resi,
|
|
Rank,
|
|
Quantile,
|
|
Count,
|
|
EMA,
|
|
WMA,
|
|
Corr,
|
|
Cov,
|
|
Delta,
|
|
Abs,
|
|
Sign,
|
|
Log,
|
|
Power,
|
|
Add,
|
|
Sub,
|
|
Mul,
|
|
Div,
|
|
Greater,
|
|
Less,
|
|
And,
|
|
Or,
|
|
Not,
|
|
Gt,
|
|
Ge,
|
|
Lt,
|
|
Le,
|
|
Eq,
|
|
Ne,
|
|
Mask,
|
|
IdxMax,
|
|
IdxMin,
|
|
If,
|
|
Feature,
|
|
PFeature,
|
|
] + [TResample]
|
|
|
|
|
|
class OpsWrapper:
|
|
"""Ops Wrapper"""
|
|
|
|
def __init__(self):
|
|
self._ops = {}
|
|
|
|
def reset(self):
|
|
self._ops = {}
|
|
|
|
def register(self, ops_list: List[Union[Type[ExpressionOps], dict]]):
|
|
"""register operator
|
|
|
|
Parameters
|
|
----------
|
|
ops_list : List[Union[Type[ExpressionOps], dict]]
|
|
- if type(ops_list) is List[Type[ExpressionOps]], each element of ops_list represents the operator class, which should be the subclass of `ExpressionOps`.
|
|
- if type(ops_list) is List[dict], each element of ops_list represents the config of operator, which has the following format:
|
|
{
|
|
"class": class_name,
|
|
"module_path": path,
|
|
}
|
|
Note: `class` should be the class name of operator, `module_path` should be a python module or path of file.
|
|
"""
|
|
for _operator in ops_list:
|
|
if isinstance(_operator, dict):
|
|
_ops_class, _ = get_callable_kwargs(_operator)
|
|
else:
|
|
_ops_class = _operator
|
|
|
|
if not issubclass(_ops_class, (Expression,)):
|
|
raise TypeError("operator must be subclass of ExpressionOps, not {}".format(_ops_class))
|
|
|
|
if _ops_class.__name__ in self._ops:
|
|
get_module_logger(self.__class__.__name__).warning(
|
|
"The custom operator [{}] will override the qlib default definition".format(_ops_class.__name__)
|
|
)
|
|
self._ops[_ops_class.__name__] = _ops_class
|
|
|
|
def __getattr__(self, key):
|
|
if key not in self._ops:
|
|
raise AttributeError("The operator [{0}] is not registered".format(key))
|
|
return self._ops[key]
|
|
|
|
|
|
Operators = OpsWrapper()
|
|
|
|
|
|
def register_all_ops(C):
|
|
"""register all operator"""
|
|
logger = get_module_logger("ops")
|
|
|
|
from qlib.data.pit import P, PRef # pylint: disable=C0415
|
|
|
|
Operators.reset()
|
|
Operators.register(OpsList + [P, PRef])
|
|
|
|
if getattr(C, "custom_ops", None) is not None:
|
|
Operators.register(C.custom_ops)
|
|
logger.debug("register custom operator {}".format(C.custom_ops))
|