mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-07-01 16:50:30 +08:00
Kevin Johnson
110 lines
3.7 KiB
Python
110 lines
3.7 KiB
Python
# -*- coding: utf-8 -*-
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from unittest import TestCase, skip
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from .config import sample_data
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from .context import pandas_ta
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class TestUtilityMetrics(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.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.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_cagr(self):
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"""Metrics: CAGR"""
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result = pandas_ta.utils.cagr(self.data.close)
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self.assertIsInstance(result, float)
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def test_calmar_ratio(self):
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"""Metrics: Calmar Ratio"""
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result = pandas_ta.calmar_ratio(self.close)
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self.assertIsInstance(result, float)
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result = pandas_ta.calmar_ratio(self.close, years=0)
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self.assertIsNone(result)
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result = pandas_ta.calmar_ratio(self.close, years=-2)
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self.assertIsNone(result)
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def test_downside_deviation(self):
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"""Metrics: Downside Deviation"""
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result = pandas_ta.downside_deviation(self.pctret)
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self.assertIsInstance(result, float)
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result = pandas_ta.downside_deviation(self.logret)
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self.assertIsInstance(result, float)
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def test_jensens_alpha(self):
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"""Metrics: Jensens Alpha"""
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bench_return = self.pctret.sample(n=self.close.shape[0], random_state=1)
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result = pandas_ta.jensens_alpha(self.close, bench_return)
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self.assertIsInstance(result, float)
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def test_log_max_drawdown(self):
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"""Metrics: Max Log Drawdown"""
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result = pandas_ta.log_max_drawdown(self.close)
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self.assertIsInstance(result, float)
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def test_max_drawdown(self):
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"""Metrics: Max Drawdown"""
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result = pandas_ta.max_drawdown(self.close)
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self.assertIsInstance(result, float)
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result = pandas_ta.max_drawdown(self.close, method="percent")
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self.assertIsInstance(result, float)
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result = pandas_ta.max_drawdown(self.close, method="log")
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self.assertIsInstance(result, float)
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result = pandas_ta.max_drawdown(self.close, all=True)
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self.assertIsInstance(result, dict)
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self.assertIsInstance(result["dollar"], float)
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self.assertIsInstance(result["percent"], float)
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self.assertIsInstance(result["log"], float)
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def test_optimal_leverage(self):
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"""Metrics: Optimal Leverage"""
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result = pandas_ta.optimal_leverage(self.close)
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self.assertIsInstance(result, int)
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result = pandas_ta.optimal_leverage(self.close, log=True)
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self.assertIsInstance(result, int)
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def test_pure_profit_score(self):
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"""Metrics: Pure Profit Score"""
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result = pandas_ta.pure_profit_score(self.close)
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self.assertIsInstance(result, float)
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def test_sharpe_ratio(self):
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"""Metrics: Sharpe Ratio"""
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result = pandas_ta.sharpe_ratio(self.close)
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self.assertIsInstance(result, float)
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def test_sortino_ratio(self):
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"""Metrics: Sortino Ratio"""
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result = pandas_ta.sortino_ratio(self.close)
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self.assertIsInstance(result, float)
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def test_volatility(self):
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"""Metrics: Volatility"""
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returns_ = pandas_ta.percent_return(self.close)
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result = pandas_ta.utils.volatility(returns_, returns=True)
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self.assertIsInstance(result, float)
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for tf in ["years", "months", "weeks", "days", "hours", "minutes", "seconds"]:
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result = pandas_ta.utils.volatility(self.close, tf)
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with self.subTest(tf=tf):
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self.assertIsInstance(result, float)
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