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f2361f1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import fire
import qlib
from qlib.constant import REG_CN
from qlib.config import HIGH_FREQ_CONFIG
from qlib.utils import init_instance_by_config
from qlib.utils.pickle_utils import restricted_pickle_load
from qlib.data.dataset.handler import DataHandlerLP
from qlib.data.ops import Operators
from qlib.data.data import Cal
from qlib.tests.data import GetData
from highfreq_ops import get_calendar_day, DayLast, FFillNan, BFillNan, Date, Select, IsNull, Cut
class HighfreqWorkflow:
SPEC_CONF = {"custom_ops": [DayLast, FFillNan, BFillNan, Date, Select, IsNull, Cut], "expression_cache": None}
MARKET = "all"
start_time = "2020-09-15 00:00:00"
end_time = "2021-01-18 16:00:00"
train_end_time = "2020-11-30 16:00:00"
test_start_time = "2020-12-01 00:00:00"
DATA_HANDLER_CONFIG0 = {
"start_time": start_time,
"end_time": end_time,
"fit_start_time": start_time,
"fit_end_time": train_end_time,
"instruments": MARKET,
"infer_processors": [{"class": "HighFreqNorm", "module_path": "highfreq_processor"}],
}
DATA_HANDLER_CONFIG1 = {
"start_time": start_time,
"end_time": end_time,
"instruments": MARKET,
}
task = {
"dataset": {
"class": "DatasetH",
"module_path": "qlib.data.dataset",
"kwargs": {
"handler": {
"class": "HighFreqHandler",
"module_path": "highfreq_handler",
"kwargs": DATA_HANDLER_CONFIG0,
},
"segments": {
"train": (start_time, train_end_time),
"test": (
test_start_time,
end_time,
),
},
},
},
"dataset_backtest": {
"class": "DatasetH",
"module_path": "qlib.data.dataset",
"kwargs": {
"handler": {
"class": "HighFreqBacktestHandler",
"module_path": "highfreq_handler",
"kwargs": DATA_HANDLER_CONFIG1,
},
"segments": {
"train": (start_time, train_end_time),
"test": (
test_start_time,
end_time,
),
},
},
},
}
def _init_qlib(self):
"""initialize qlib"""
# use cn_data_1min data
QLIB_INIT_CONFIG = {**HIGH_FREQ_CONFIG, **self.SPEC_CONF}
provider_uri = QLIB_INIT_CONFIG.get("provider_uri")
GetData().qlib_data(target_dir=provider_uri, interval="1min", region=REG_CN, exists_skip=True)
qlib.init(**QLIB_INIT_CONFIG)
def _prepare_calender_cache(self):
"""preload the calendar for cache"""
# This code used the copy-on-write feature of Linux to avoid calculating the calendar multiple times in the subprocess
# This code may accelerate, but may be not useful on Windows and Mac Os
Cal.calendar(freq="1min")
get_calendar_day(freq="1min")
def get_data(self):
"""use dataset to get highreq data"""
self._init_qlib()
self._prepare_calender_cache()
dataset = init_instance_by_config(self.task["dataset"])
xtrain, xtest = dataset.prepare(["train", "test"])
print(xtrain, xtest)
dataset_backtest = init_instance_by_config(self.task["dataset_backtest"])
backtest_train, backtest_test = dataset_backtest.prepare(["train", "test"])
print(backtest_train, backtest_test)
return
def dump_and_load_dataset(self):
"""dump and load dataset state on disk"""
self._init_qlib()
self._prepare_calender_cache()
dataset = init_instance_by_config(self.task["dataset"])
dataset_backtest = init_instance_by_config(self.task["dataset_backtest"])
##=============dump dataset=============
dataset.to_pickle(path="dataset.pkl")
dataset_backtest.to_pickle(path="dataset_backtest.pkl")
del dataset, dataset_backtest
##=============reload dataset=============
with open("dataset.pkl", "rb") as file_dataset:
dataset = restricted_pickle_load(file_dataset)
with open("dataset_backtest.pkl", "rb") as file_dataset_backtest:
dataset_backtest = restricted_pickle_load(file_dataset_backtest)
self._prepare_calender_cache()
##=============reinit dataset=============
dataset.config(
handler_kwargs={
"start_time": "2021-01-19 00:00:00",
"end_time": "2021-01-25 16:00:00",
},
segments={
"test": (
"2021-01-19 00:00:00",
"2021-01-25 16:00:00",
),
},
)
dataset.setup_data(
handler_kwargs={
"init_type": DataHandlerLP.IT_LS,
},
)
dataset_backtest.config(
handler_kwargs={
"start_time": "2021-01-19 00:00:00",
"end_time": "2021-01-25 16:00:00",
},
segments={
"test": (
"2021-01-19 00:00:00",
"2021-01-25 16:00:00",
),
},
)
dataset_backtest.setup_data(handler_kwargs={})
##=============get data=============
xtest = dataset.prepare("test")
backtest_test = dataset_backtest.prepare("test")
print(xtest, backtest_test)
return
if __name__ == "__main__":
fire.Fire(HighfreqWorkflow)
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