mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
Kevin Johnson
125 lines
4.2 KiB
Python
125 lines
4.2 KiB
Python
from .config import error_analysis, sample_data, CORRELATION, CORRELATION_THRESHOLD, VERBOSE
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from .context import pandas_ta
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from unittest import skip, TestCase
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import pandas.testing as pdt
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from pandas import DataFrame, Series
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import talib as tal
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class TestStatistics(TestCase):
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@classmethod
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def setUpClass(cls):
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cls.data = sample_data
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cls.data.columns = cls.data.columns.str.lower()
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cls.open = cls.data["open"]
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cls.high = cls.data["high"]
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cls.low = cls.data["low"]
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cls.close = cls.data["close"]
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if "volume" in cls.data.columns:
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cls.volume = cls.data["volume"]
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@classmethod
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def tearDownClass(cls):
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del cls.open
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del cls.high
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del cls.low
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del cls.close
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if hasattr(cls, "volume"):
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del cls.volume
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del cls.data
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def setUp(self): pass
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def tearDown(self): pass
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def test_entropy(self):
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result = pandas_ta.entropy(self.close)
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self.assertIsInstance(result, Series)
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self.assertEqual(result.name, "ENTP_10")
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def test_kurtosis(self):
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result = pandas_ta.kurtosis(self.close)
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self.assertIsInstance(result, Series)
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self.assertEqual(result.name, "KURT_30")
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def test_mad(self):
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result = pandas_ta.mad(self.close)
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self.assertIsInstance(result, Series)
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self.assertEqual(result.name, "MAD_30")
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def test_median(self):
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result = pandas_ta.median(self.close)
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self.assertIsInstance(result, Series)
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self.assertEqual(result.name, "MEDIAN_30")
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def test_quantile(self):
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result = pandas_ta.quantile(self.close)
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self.assertIsInstance(result, Series)
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self.assertEqual(result.name, "QTL_30_0.5")
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def test_skew(self):
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result = pandas_ta.skew(self.close)
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self.assertIsInstance(result, Series)
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self.assertEqual(result.name, "SKEW_30")
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def test_stdev(self):
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result = pandas_ta.stdev(self.close, talib=False)
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self.assertIsInstance(result, Series)
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self.assertEqual(result.name, "STDEV_30")
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try:
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expected = tal.STDDEV(self.close, 30)
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pdt.assert_series_equal(result, expected, check_names=False)
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except AssertionError:
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try:
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corr = pandas_ta.utils.df_error_analysis(result, expected, col=CORRELATION)
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self.assertGreater(corr, CORRELATION_THRESHOLD)
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except Exception as ex:
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error_analysis(result, CORRELATION, ex)
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result = pandas_ta.stdev(self.close)
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self.assertIsInstance(result, Series)
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self.assertEqual(result.name, "STDEV_30")
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def test_tos_sdtevall(self):
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result = pandas_ta.tos_stdevall(self.close)
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self.assertIsInstance(result, DataFrame)
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self.assertEqual(result.name, "TOS_STDEVALL")
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self.assertEqual(len(result.columns), 7)
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result = pandas_ta.tos_stdevall(self.close, length=30)
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self.assertIsInstance(result, DataFrame)
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self.assertEqual(result.name, "TOS_STDEVALL_30")
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self.assertEqual(len(result.columns), 7)
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result = pandas_ta.tos_stdevall(self.close, length=30, stds=[1, 2])
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self.assertIsInstance(result, DataFrame)
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self.assertEqual(result.name, "TOS_STDEVALL_30")
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self.assertEqual(len(result.columns), 5)
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def test_variance(self):
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result = pandas_ta.variance(self.close, talib=False)
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self.assertIsInstance(result, Series)
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self.assertEqual(result.name, "VAR_30")
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try:
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expected = tal.VAR(self.close, 30)
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pdt.assert_series_equal(result, expected, check_names=False)
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except AssertionError:
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try:
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corr = pandas_ta.utils.df_error_analysis(result, expected, col=CORRELATION)
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self.assertGreater(corr, CORRELATION_THRESHOLD)
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except Exception as ex:
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error_analysis(result, CORRELATION, ex)
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result = pandas_ta.variance(self.close)
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self.assertIsInstance(result, Series)
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self.assertEqual(result.name, "VAR_30")
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def test_zscore(self):
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result = pandas_ta.zscore(self.close)
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self.assertIsInstance(result, Series)
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self.assertEqual(result.name, "ZS_30")
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