mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-07-05 17:30:25 +08:00
Kevin Johnson
223 lines
7.4 KiB
Python
223 lines
7.4 KiB
Python
# Must run seperately from the rest of the tests
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# in order to successfully run
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from multiprocessing import cpu_count
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from time import perf_counter
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from .config import sample_data
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from .context import pandas_ta
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from unittest import skip, skipUnless, TestCase
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from pandas import DataFrame
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# Strategy Testing Parameters
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cores = cpu_count()
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cumulative = False
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speed_table = False
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strategy_timed = False
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timed = True
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verbose = False
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class TestStrategyMethods(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.ta.cores = cores
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cls.speed_test = DataFrame()
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@classmethod
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def tearDownClass(cls):
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cls.speed_test = cls.speed_test.T
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cls.speed_test.index.name = "Test"
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cls.speed_test.columns = ["Columns", "Seconds"]
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if cumulative:
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cls.speed_test["Cum. Seconds"] = cls.speed_test["Seconds"].cumsum()
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if speed_table:
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cls.speed_test.to_csv("tests/speed_test.csv")
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if timed:
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print(f"[i] Cores: {cls.data.ta.cores}")
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print(f"[i] Total Datapoints: {cls.data.shape[0]}")
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print(cls.speed_test)
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del cls.data
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def setUp(self):
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self.added_cols = 0
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self.category = ""
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self.init_cols = len(self.data.columns)
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self.time_diff = 0
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self.result = None
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if verbose: print()
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if timed: self.stime = perf_counter()
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def tearDown(self):
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if timed:
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self.time_diff = perf_counter() - self.stime
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self.added_cols = len(self.data.columns) - self.init_cols
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self.assertGreaterEqual(self.added_cols, 1)
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self.result = self.data[self.data.columns[-self.added_cols:]]
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self.assertIsInstance(self.result, DataFrame)
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self.data.drop(columns=self.result.columns, axis=1, inplace=True)
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self.speed_test[self.category] = [self.added_cols, self.time_diff]
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# @skip
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def test_all(self):
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self.category = "All"
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self.data.ta.strategy(verbose=verbose, timed=strategy_timed)
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@skipUnless(verbose, "verbose mode only")
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def test_all_strategy(self):
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self.data.ta.strategy(pandas_ta.AllStrategy, verbose=verbose, timed=strategy_timed)
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@skipUnless(verbose, "verbose mode only")
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def test_all_name_strategy(self):
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self.category = "All"
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self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
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# @skip
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def test_candles_category(self):
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self.category = "Candles"
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self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
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# @skip
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def test_common(self):
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self.category = "Common"
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self.data.ta.strategy(pandas_ta.CommonStrategy, verbose=verbose, timed=strategy_timed)
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def test_cycles_category(self):
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self.category = "Cycles"
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self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
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# @skip
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def test_custom_a(self):
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self.category = "Custom A"
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momo_bands_sma_ta = [
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{"kind": "rsi"}, # 1
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{"kind": "macd"}, # 3
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{"kind": "sma", "length": 50}, # 1
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{"kind": "sma", "length": 200 }, # 1
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{"kind": "bbands", "length": 20}, # 3
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{"kind": "log_return", "cumulative": True}, # 1
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{"kind": "ema", "close": "CUMLOGRET_1", "length": 5, "suffix": "CLR"}
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]
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custom = pandas_ta.Strategy(
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"Commons with Cumulative Log Return EMA Chain", # name
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momo_bands_sma_ta, # ta
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"Common indicators with specific lengths and a chained indicator", # description
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)
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self.data.ta.strategy(custom, verbose=verbose, timed=strategy_timed)
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# @skip
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def test_custom_args_tuple(self):
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self.category = "Custom B"
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custom_args_ta = [
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{"kind": "ema", "params": (5,)},
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{"kind": "fisher", "params": (13, 7)}
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]
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custom = pandas_ta.Strategy(
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"Custom Args Tuple",
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custom_args_ta,
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"Allow for easy filling in indicator arguments by argument placement."
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)
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self.data.ta.strategy(custom, verbose=verbose, timed=strategy_timed)
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def test_custom_col_names_tuple(self):
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self.category = "Custom C"
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custom_args_ta = [{"kind": "bbands", "col_names": ("LB", "MB", "UB", "BW")}]
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custom = pandas_ta.Strategy(
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"Custom Col Numbers Tuple",
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custom_args_ta,
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"Allow for easy renaming of resultant columns",
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)
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self.data.ta.strategy(custom, verbose=verbose, timed=strategy_timed)
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# @skip
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def test_custom_col_numbers_tuple(self):
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self.category = "Custom D"
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custom_args_ta = [{"kind": "macd", "col_numbers": (1,)}]
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custom = pandas_ta.Strategy(
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"Custom Col Numbers Tuple",
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custom_args_ta,
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"Allow for easy selection of resultant columns",
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)
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self.data.ta.strategy(custom, verbose=verbose, timed=strategy_timed)
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# @skip
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def test_momentum_category(self):
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self.category = "Momentum"
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self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
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# @skip
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def test_overlap_category(self):
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self.category = "Overlap"
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self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
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# @skip
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def test_performance_category(self):
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self.category = "Performance"
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self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
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# @skip
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def test_statistics_category(self):
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self.category = "Statistics"
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self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
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# @skip
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def test_trend_category(self):
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self.category = "Trend"
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self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
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# @skip
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def test_volatility_category(self):
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self.category = "Volatility"
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self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
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# @skip
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def test_volume_category(self):
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self.category = "Volume"
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self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
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# @skipUnless(verbose, "verbose mode only")
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def test_all_no_multiprocessing(self):
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self.category = "All with No Multiprocessing"
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cores = self.data.ta.cores
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self.data.ta.cores = 0
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self.data.ta.strategy(verbose=verbose, timed=strategy_timed)
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self.data.ta.cores = cores
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# @skipUnless(verbose, "verbose mode only")
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def test_custom_no_multiprocessing(self):
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self.category = "Custom A with No Multiprocessing"
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cores = self.data.ta.cores
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self.data.ta.cores = 0
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momo_bands_sma_ta = [
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{"kind": "rsi"}, # 1
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{"kind": "macd"}, # 3
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{"kind": "sma", "length": 50}, # 1
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{"kind": "sma", "length": 200 }, # 1
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{"kind": "bbands", "length": 20}, # 3
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{"kind": "log_return", "cumulative": True}, # 1
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{"kind": "ema", "close": "CUMLOGRET_1", "length": 5, "suffix": "CLR"}
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]
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custom = pandas_ta.Strategy(
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"Commons with Cumulative Log Return EMA Chain", # name
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momo_bands_sma_ta, # ta
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"Common indicators with specific lengths and a chained indicator", # description
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)
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self.data.ta.strategy(custom, verbose=verbose, timed=strategy_timed)
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self.data.ta.cores = cores
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