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3097dcc995
* fix(security): enforce RestrictedUnpickler for load_instance to prevent unsafe pickle deserialization * fix: lint error
73 lines
3.2 KiB
Python
73 lines
3.2 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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"""
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Qlib follow the logic below to supporting point-in-time database
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For each stock, the format of its data is <observe_time, feature>. Expression Engine support calculation on such format of data
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To calculate the feature value f_t at a specific observe time t, data with format <period_time, feature> will be used.
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For example, the average earning of last 4 quarters (period_time) on 20190719 (observe_time)
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The calculation of both <period_time, feature> and <observe_time, feature> data rely on expression engine. It consists of 2 phases.
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1) calculation <period_time, feature> at each observation time t and it will collasped into a point (just like a normal feature)
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2) concatenate all th collasped data, we will get data with format <observe_time, feature>.
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Qlib will use the operator `P` to perform the collapse.
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"""
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import numpy as np
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import pandas as pd
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from qlib.data.ops import ElemOperator
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from qlib.log import get_module_logger
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from .data import Cal
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class P(ElemOperator):
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def _load_internal(self, instrument, start_index, end_index, freq):
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_calendar = Cal.calendar(freq=freq)
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resample_data = np.empty(end_index - start_index + 1, dtype="float32")
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for cur_index in range(start_index, end_index + 1):
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cur_time = _calendar[cur_index]
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# To load expression accurately, more historical data are required
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start_ws, end_ws = self.feature.get_extended_window_size()
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if end_ws > 0:
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raise ValueError(
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"PIT database does not support referring to future period (e.g. expressions like `Ref('$$roewa_q', -1)` are not supported"
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)
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# The calculated value will always the last element, so the end_offset is zero.
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try:
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s = self._load_feature(instrument, -start_ws, 0, cur_time)
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resample_data[cur_index - start_index] = s.iloc[-1] if len(s) > 0 else np.nan
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except FileNotFoundError:
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get_module_logger("base").warning(f"WARN: period data not found for {str(self)}")
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return pd.Series(dtype="float32", name=str(self))
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resample_series = pd.Series(
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resample_data, index=pd.RangeIndex(start_index, end_index + 1), dtype="float32", name=str(self)
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)
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return resample_series
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def _load_feature(self, instrument, start_index, end_index, cur_time):
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return self.feature.load(instrument, start_index, end_index, cur_time)
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def get_longest_back_rolling(self):
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# The period data will collapse as a normal feature. So no extending and looking back
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return 0
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def get_extended_window_size(self):
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# The period data will collapse as a normal feature. So no extending and looking back
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return 0, 0
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class PRef(P):
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def __init__(self, feature, period):
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super().__init__(feature)
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self.period = period
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def __str__(self):
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return f"{super().__str__()}[{self.period}]"
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def _load_feature(self, instrument, start_index, end_index, cur_time):
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return self.feature.load(instrument, start_index, end_index, cur_time, self.period)
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