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https://github.com/wassname/pandas-ta.git
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Kevin Johnson
48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
from .config import sample_data
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from .context import pandas_ta
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from unittest import TestCase
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from pandas import Series
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class TestPerformace(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.close = cls.data["close"]
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cls.islong = (cls.close > pandas_ta.sma(cls.close, length=8)).astype(int)
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cls.pctret = pandas_ta.percent_return(cls.close, cumulative=False)
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cls.logret = pandas_ta.percent_return(cls.close, cumulative=False)
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@classmethod
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def tearDownClass(cls):
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del cls.data
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del cls.close
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del cls.islong
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del cls.pctret
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del cls.logret
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def setUp(self): pass
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def tearDown(self): pass
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def test_log_return(self):
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result = pandas_ta.log_return(self.close)
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self.assertIsInstance(result, Series)
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self.assertEqual(result.name, "LOGRET_1")
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def test_cum_log_return(self):
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result = pandas_ta.log_return(self.close, cumulative=True)
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self.assertIsInstance(result, Series)
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self.assertEqual(result.name, "CUMLOGRET_1")
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def test_percent_return(self):
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result = pandas_ta.percent_return(self.close, cumulative=False)
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self.assertIsInstance(result, Series)
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self.assertEqual(result.name, "PCTRET_1")
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def test_cum_percent_return(self):
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result = pandas_ta.percent_return(self.close, cumulative=True)
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self.assertEqual(result.name, "CUMPCTRET_1")
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