File size: 5,492 Bytes
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 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import qlib
import fire
from datetime import datetime
from qlib.constant import REG_CN
from qlib.data.dataset.handler import DataHandlerLP
from qlib.utils import init_instance_by_config
from qlib.utils.pickle_utils import restricted_pickle_load
from qlib.tests.data import GetData
class RollingDataWorkflow:
MARKET = "csi300"
start_time = "2010-01-01"
end_time = "2019-12-31"
rolling_cnt = 5
def _init_qlib(self):
"""initialize qlib"""
provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
GetData().qlib_data(target_dir=provider_uri, region=REG_CN, exists_skip=True)
qlib.init(provider_uri=provider_uri, region=REG_CN)
def _dump_pre_handler(self, path):
handler_config = {
"class": "Alpha158",
"module_path": "qlib.contrib.data.handler",
"kwargs": {
"start_time": self.start_time,
"end_time": self.end_time,
"instruments": self.MARKET,
"infer_processors": [],
"learn_processors": [],
},
}
pre_handler = init_instance_by_config(handler_config)
pre_handler.config(dump_all=True)
pre_handler.to_pickle(path)
def _load_pre_handler(self, path):
with open(path, "rb") as file_dataset:
pre_handler = restricted_pickle_load(file_dataset)
return pre_handler
def rolling_process(self):
self._init_qlib()
self._dump_pre_handler("pre_handler.pkl")
pre_handler = self._load_pre_handler("pre_handler.pkl")
train_start_time = (2010, 1, 1)
train_end_time = (2012, 12, 31)
valid_start_time = (2013, 1, 1)
valid_end_time = (2013, 12, 31)
test_start_time = (2014, 1, 1)
test_end_time = (2014, 12, 31)
dataset_config = {
"class": "DatasetH",
"module_path": "qlib.data.dataset",
"kwargs": {
"handler": {
"class": "RollingDataHandler",
"module_path": "rolling_handler",
"kwargs": {
"start_time": datetime(*train_start_time),
"end_time": datetime(*test_end_time),
"fit_start_time": datetime(*train_start_time),
"fit_end_time": datetime(*train_end_time),
"infer_processors": [
{"class": "RobustZScoreNorm", "kwargs": {"fields_group": "feature"}},
],
"learn_processors": [
{"class": "DropnaLabel"},
{"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}},
],
"data_loader_kwargs": {
"handler_config": pre_handler,
},
},
},
"segments": {
"train": (datetime(*train_start_time), datetime(*train_end_time)),
"valid": (datetime(*valid_start_time), datetime(*valid_end_time)),
"test": (datetime(*test_start_time), datetime(*test_end_time)),
},
},
}
dataset = init_instance_by_config(dataset_config)
for rolling_offset in range(self.rolling_cnt):
print(f"===========rolling{rolling_offset} start===========")
if rolling_offset:
dataset.config(
handler_kwargs={
"start_time": datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]),
"end_time": datetime(test_end_time[0] + rolling_offset, *test_end_time[1:]),
"processor_kwargs": {
"fit_start_time": datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]),
"fit_end_time": datetime(train_end_time[0] + rolling_offset, *train_end_time[1:]),
},
},
segments={
"train": (
datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]),
datetime(train_end_time[0] + rolling_offset, *train_end_time[1:]),
),
"valid": (
datetime(valid_start_time[0] + rolling_offset, *valid_start_time[1:]),
datetime(valid_end_time[0] + rolling_offset, *valid_end_time[1:]),
),
"test": (
datetime(test_start_time[0] + rolling_offset, *test_start_time[1:]),
datetime(test_end_time[0] + rolling_offset, *test_end_time[1:]),
),
},
)
dataset.setup_data(
handler_kwargs={
"init_type": DataHandlerLP.IT_FIT_SEQ,
}
)
dtrain, dvalid, dtest = dataset.prepare(["train", "valid", "test"])
print(dtrain, dvalid, dtest)
## print or dump data
print(f"===========rolling{rolling_offset} end===========")
if __name__ == "__main__":
fire.Fire(RollingDataWorkflow)
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