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ab8be8f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import sys
import qlib
import shutil
import unittest
import pytest
import pandas as pd
from pathlib import Path
from qlib.data import D
from qlib.tests.data import GetData
sys.path.append(str(Path(__file__).resolve().parent.parent.joinpath("scripts")))
from dump_pit import DumpPitData
sys.path.append(str(Path(__file__).resolve().parent.parent.joinpath("scripts/data_collector/pit")))
from collector import Run
pd.set_option("display.width", 1000)
pd.set_option("display.max_columns", None)
DATA_DIR = Path(__file__).parent.joinpath("test_pit_data")
SOURCE_DIR = DATA_DIR.joinpath("stock_data/source")
SOURCE_DIR.mkdir(exist_ok=True, parents=True)
QLIB_DIR = DATA_DIR.joinpath("qlib_data")
QLIB_DIR.mkdir(exist_ok=True, parents=True)
class TestPIT(unittest.TestCase):
@classmethod
def tearDownClass(cls) -> None:
shutil.rmtree(str(DATA_DIR.resolve()))
@classmethod
def setUpClass(cls) -> None:
cn_data_dir = str(QLIB_DIR.joinpath("cn_data").resolve())
pit_dir = str(SOURCE_DIR.joinpath("pit").resolve())
pit_normalized_dir = str(SOURCE_DIR.joinpath("pit_normalized").resolve())
GetData().qlib_data(
name="qlib_data_simple", target_dir=cn_data_dir, region="cn", delete_old=False, exists_skip=True
)
GetData().qlib_data(name="qlib_data", target_dir=pit_dir, region="pit", delete_old=False, exists_skip=True)
# NOTE: This code does the same thing as line 43, but since baostock is not stable in downloading data, we have chosen to download offline data.
# bs.login()
# Run(
# source_dir=pit_dir,
# interval="quarterly",
# ).download_data(start="2000-01-01", end="2020-01-01", symbol_regex="^(600519|000725).*")
# bs.logout()
Run(
source_dir=pit_dir,
normalize_dir=pit_normalized_dir,
interval="quarterly",
).normalize_data()
DumpPitData(
csv_path=pit_normalized_dir,
qlib_dir=cn_data_dir,
).dump(interval="quarterly")
def setUp(self):
# qlib.init(kernels=1) # NOTE: set kernel to 1 to make it debug easier
provider_uri = str(QLIB_DIR.joinpath("cn_data").resolve())
qlib.init(provider_uri=provider_uri)
def to_str(self, obj):
return "".join(str(obj).split())
def check_same(self, a, b):
self.assertEqual(self.to_str(a), self.to_str(b))
def test_query(self):
instruments = ["sh600519"]
fields = ["P($$roewa_q)", "P($$yoyni_q)"]
# Mao Tai published 2019Q2 report at 2019-07-13 & 2019-07-18
# - http://www.cninfo.com.cn/new/commonUrl/pageOfSearch?url=disclosure/list/search&lastPage=index
data = D.features(instruments, fields, start_time="2019-01-01", end_time="2019-07-19", freq="day")
res = """
P($$roewa_q) P($$yoyni_q)
count 133.000000 133.000000
mean 0.196412 0.277930
std 0.097591 0.030262
min 0.000000 0.243892
25% 0.094737 0.243892
50% 0.255220 0.304181
75% 0.255220 0.305041
max 0.344644 0.305041
"""
self.check_same(data.describe(), res)
res = """
P($$roewa_q) P($$yoyni_q)
instrument datetime
sh600519 2019-07-15 0.000000 0.305041
2019-07-16 0.000000 0.305041
2019-07-17 0.000000 0.305041
2019-07-18 0.175322 0.252650
2019-07-19 0.175322 0.252650
"""
self.check_same(data.tail(), res)
def test_no_exist_data(self):
fields = ["P($$roewa_q)", "P($$yoyni_q)", "$close"]
data = D.features(["sh600519", "sh601988"], fields, start_time="2019-01-01", end_time="2019-07-19", freq="day")
data["$close"] = 1 # in case of different dataset gives different values
expect = """
P($$roewa_q) P($$yoyni_q) $close
instrument datetime
sh600519 2019-01-02 0.25522 0.243892 1
2019-01-03 0.25522 0.243892 1
2019-01-04 0.25522 0.243892 1
2019-01-07 0.25522 0.243892 1
2019-01-08 0.25522 0.243892 1
... ... ... ...
sh601988 2019-07-15 NaN NaN 1
2019-07-16 NaN NaN 1
2019-07-17 NaN NaN 1
2019-07-18 NaN NaN 1
2019-07-19 NaN NaN 1
[266 rows x 3 columns]
"""
self.check_same(data, expect)
@pytest.mark.slow
def test_expr(self):
fields = [
"P(Mean($$roewa_q, 1))",
"P($$roewa_q)",
"P(Mean($$roewa_q, 2))",
"P(Ref($$roewa_q, 1))",
"P((Ref($$roewa_q, 1) +$$roewa_q) / 2)",
]
instruments = ["sh600519"]
data = D.features(instruments, fields, start_time="2019-01-01", end_time="2019-07-19", freq="day")
expect = """
P(Mean($$roewa_q, 1)) P($$roewa_q) P(Mean($$roewa_q, 2)) P(Ref($$roewa_q, 1)) P((Ref($$roewa_q, 1) +$$roewa_q) / 2)
instrument datetime
sh600519 2019-07-01 0.094737 0.094737 0.219691 0.344644 0.219691
2019-07-02 0.094737 0.094737 0.219691 0.344644 0.219691
2019-07-03 0.094737 0.094737 0.219691 0.344644 0.219691
2019-07-04 0.094737 0.094737 0.219691 0.344644 0.219691
2019-07-05 0.094737 0.094737 0.219691 0.344644 0.219691
2019-07-08 0.094737 0.094737 0.219691 0.344644 0.219691
2019-07-09 0.094737 0.094737 0.219691 0.344644 0.219691
2019-07-10 0.094737 0.094737 0.219691 0.344644 0.219691
2019-07-11 0.094737 0.094737 0.219691 0.344644 0.219691
2019-07-12 0.094737 0.094737 0.219691 0.344644 0.219691
2019-07-15 0.000000 0.000000 0.047369 0.094737 0.047369
2019-07-16 0.000000 0.000000 0.047369 0.094737 0.047369
2019-07-17 0.000000 0.000000 0.047369 0.094737 0.047369
2019-07-18 0.175322 0.175322 0.135029 0.094737 0.135029
2019-07-19 0.175322 0.175322 0.135029 0.094737 0.135029
"""
self.check_same(data.tail(15), expect)
def test_unlimit(self):
# fields = ["P(Mean($$roewa_q, 1))", "P($$roewa_q)", "P(Mean($$roewa_q, 2))", "P(Ref($$roewa_q, 1))", "P((Ref($$roewa_q, 1) +$$roewa_q) / 2)"]
fields = ["P($$roewa_q)"]
instruments = ["sh600519"]
_ = D.features(instruments, fields, freq="day") # this should not raise error
data = D.features(instruments, fields, end_time="2020-01-01", freq="day") # this should not raise error
s = data.iloc[:, 0]
# You can check the expected value based on the content in `docs/advanced/PIT.rst`
expect = """
instrument datetime
sh600519 2005-01-04 NaN
2007-04-30 0.090219
2007-08-17 0.139330
2007-10-23 0.245863
2008-03-03 0.347900
2008-03-13 0.395989
2008-04-22 0.100724
2008-08-28 0.249968
2008-10-27 0.334120
2009-03-25 0.390117
2009-04-21 0.102675
2009-08-07 0.230712
2009-10-26 0.300730
2010-04-02 0.335461
2010-04-26 0.083825
2010-08-12 0.200545
2010-10-29 0.260986
2011-03-21 0.307393
2011-04-25 0.097411
2011-08-31 0.248251
2011-10-18 0.318919
2012-03-23 0.403900
2012-04-11 0.403925
2012-04-26 0.112148
2012-08-10 0.264847
2012-10-26 0.370487
2013-03-29 0.450047
2013-04-18 0.099958
2013-09-02 0.210442
2013-10-16 0.304543
2014-03-25 0.394328
2014-04-25 0.083217
2014-08-29 0.164503
2014-10-30 0.234085
2015-04-21 0.078494
2015-08-28 0.137504
2015-10-23 0.201709
2016-03-24 0.264205
2016-04-21 0.073664
2016-08-29 0.136576
2016-10-31 0.188062
2017-04-17 0.244385
2017-04-25 0.080614
2017-07-28 0.151510
2017-10-26 0.254166
2018-03-28 0.329542
2018-05-02 0.088887
2018-08-02 0.170563
2018-10-29 0.255220
2019-03-29 0.344644
2019-04-25 0.094737
2019-07-15 0.000000
2019-07-18 0.175322
2019-10-16 0.255819
Name: P($$roewa_q), dtype: float32
"""
self.check_same(s[~s.duplicated().values], expect)
def test_expr2(self):
instruments = ["sh600519"]
fields = ["P($$roewa_q)", "P($$yoyni_q)"]
fields += ["P(($$roewa_q / $$yoyni_q) / Ref($$roewa_q / $$yoyni_q, 1) - 1)"]
fields += ["P(Sum($$yoyni_q, 4))"]
fields += ["$close", "P($$roewa_q) * $close"]
data = D.features(instruments, fields, start_time="2019-01-01", end_time="2020-01-01", freq="day")
except_data = """
P($$roewa_q) P($$yoyni_q) P(($$roewa_q / $$yoyni_q) / Ref($$roewa_q / $$yoyni_q, 1) - 1) P(Sum($$yoyni_q, 4)) $close P($$roewa_q) * $close
instrument datetime
sh600519 2019-01-02 0.255220 0.243892 1.484224 1.661578 63.595333 16.230801
2019-01-03 0.255220 0.243892 1.484224 1.661578 62.641907 15.987467
2019-01-04 0.255220 0.243892 1.484224 1.661578 63.915985 16.312637
2019-01-07 0.255220 0.243892 1.484224 1.661578 64.286530 16.407207
2019-01-08 0.255220 0.243892 1.484224 1.661578 64.212196 16.388237
... ... ... ... ... ... ...
2019-12-25 0.255819 0.219821 0.677052 1.081693 122.150467 31.248409
2019-12-26 0.255819 0.219821 0.677052 1.081693 122.301315 31.286999
2019-12-27 0.255819 0.219821 0.677052 1.081693 125.307404 32.056015
2019-12-30 0.255819 0.219821 0.677052 1.081693 127.763992 32.684456
2019-12-31 0.255819 0.219821 0.677052 1.081693 127.462303 32.607277
[244 rows x 6 columns]
"""
self.check_same(data, except_data)
def test_pref_operator(self):
instruments = ["sh600519"]
fields = [
"PRef($$roewa_q, 201902)",
"PRef($$yoyni_q, 201801)",
"P($$roewa_q)",
"P($$roewa_q) / PRef($$roewa_q, 201801)",
]
data = D.features(instruments, fields, start_time="2018-04-28", end_time="2019-07-19", freq="day")
except_data = """
PRef($$roewa_q, 201902) PRef($$yoyni_q, 201801) P($$roewa_q) P($$roewa_q) / PRef($$roewa_q, 201801)
instrument datetime
sh600519 2018-05-02 NaN 0.395075 0.088887 1.000000
2018-05-03 NaN 0.395075 0.088887 1.000000
2018-05-04 NaN 0.395075 0.088887 1.000000
2018-05-07 NaN 0.395075 0.088887 1.000000
2018-05-08 NaN 0.395075 0.088887 1.000000
... ... ... ... ...
2019-07-15 0.000000 0.395075 0.000000 0.000000
2019-07-16 0.000000 0.395075 0.000000 0.000000
2019-07-17 0.000000 0.395075 0.000000 0.000000
2019-07-18 0.175322 0.395075 0.175322 1.972414
2019-07-19 0.175322 0.395075 0.175322 1.972414
[299 rows x 4 columns]
"""
self.check_same(data, except_data)
if __name__ == "__main__":
unittest.main()
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