mirror of
https://github.com/microsoft/qlib.git
synced 2026-06-06 05:51:17 +08:00
38 lines
1.7 KiB
Python
38 lines
1.7 KiB
Python
import unittest
|
|
import numpy as np
|
|
from qlib.data import D
|
|
from qlib.tests import TestAutoData
|
|
|
|
|
|
class TestDataset(TestAutoData):
|
|
def testCSI300(self):
|
|
close_p = D.features(D.instruments("csi300"), ["$close"])
|
|
size = close_p.groupby("datetime").size()
|
|
cnt = close_p.groupby("datetime").count()["$close"]
|
|
size_desc = size.describe(percentiles=np.arange(0.1, 1.0, 0.1))
|
|
cnt_desc = cnt.describe(percentiles=np.arange(0.1, 1.0, 0.1))
|
|
|
|
print(size_desc)
|
|
print(cnt_desc)
|
|
|
|
self.assertLessEqual(size_desc.loc["max"], 305, "Excessive number of CSI300 constituent stocks")
|
|
self.assertGreaterEqual(size_desc.loc["80%"], 290, "Insufficient number of CSI300 constituent stocks")
|
|
|
|
self.assertLessEqual(cnt_desc.loc["max"], 305, "Excessive number of CSI300 constituent stocks")
|
|
# FIXME: Due to the low quality of data. Hard to make sure there are enough data
|
|
# self.assertEqual(cnt_desc.loc["80%"], 300, "Insufficient number of CSI300 constituent stocks")
|
|
|
|
def testClose(self):
|
|
close_p = D.features(D.instruments("csi300"), ["Ref($close, 1)/$close - 1"])
|
|
close_desc = close_p.describe(percentiles=np.arange(0.1, 1.0, 0.1))
|
|
print(close_desc)
|
|
self.assertLessEqual(abs(close_desc.loc["90%"][0]), 0.1, "Close value is abnormal")
|
|
self.assertLessEqual(abs(close_desc.loc["10%"][0]), 0.1, "Close value is abnormal")
|
|
# FIXME: The yahoo data is not perfect. We have to
|
|
# self.assertLessEqual(abs(close_desc.loc["max"][0]), 0.2, "Close value is abnormal")
|
|
# self.assertGreaterEqual(close_desc.loc["min"][0], -0.2, "Close value is abnormal")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|