mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-07-10 00:30:23 +08:00
Kevin Johnson
270 lines
9.5 KiB
Python
270 lines
9.5 KiB
Python
# Must run seperately from the rest of the tests
|
|
# in order to successfully run
|
|
from multiprocessing import cpu_count
|
|
from time import perf_counter
|
|
|
|
from .config import sample_data
|
|
from .context import pandas_ta
|
|
|
|
from unittest import skip, skipUnless, TestCase
|
|
from pandas import DataFrame
|
|
|
|
# Strategy Testing Parameters
|
|
cores = cpu_count()
|
|
cumulative = False
|
|
speed_table = False
|
|
strategy_timed = False
|
|
timed = True
|
|
verbose = False
|
|
|
|
|
|
class TestStrategyMethods(TestCase):
|
|
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
cls.data = sample_data
|
|
cls.data.ta.cores = cores
|
|
cls.speed_test = DataFrame()
|
|
|
|
@classmethod
|
|
def tearDownClass(cls):
|
|
cls.speed_test = cls.speed_test.T
|
|
cls.speed_test.index.name = "Test"
|
|
cls.speed_test.columns = ["Columns", "Seconds"]
|
|
if cumulative:
|
|
cls.speed_test["Cum. Seconds"] = cls.speed_test["Seconds"].cumsum()
|
|
if speed_table:
|
|
cls.speed_test.to_csv("tests/speed_test.csv")
|
|
if timed:
|
|
tca = cls.speed_test['Columns'].sum()
|
|
tcs = cls.speed_test['Seconds'].sum()
|
|
cps = f"[i] Total Columns / Second for All Tests: { tca / tcs:.5f} "
|
|
print("=" * len(cps))
|
|
print(cls.speed_test)
|
|
print(f"[i] Cores: {cls.data.ta.cores}")
|
|
print(f"[i] Total Datapoints per run: {cls.data.shape[0]}")
|
|
print(f"[i] Total Columns added: {tca}")
|
|
print(f"[i] Total Seconds for All Tests: {tcs:.5f}")
|
|
print(cps)
|
|
print("=" * len(cps))
|
|
# tmp = concat([cls.speed_test, cls.speed_test["Columns"].sum(), cls.speed_test["Seconds"].sum()])
|
|
# print(tmp)
|
|
del cls.data
|
|
|
|
def setUp(self):
|
|
self.added_cols = 0
|
|
self.category = ""
|
|
self.init_cols = len(self.data.columns)
|
|
self.time_diff = 0
|
|
self.result = None
|
|
if verbose: print()
|
|
if timed: self.stime = perf_counter()
|
|
|
|
def tearDown(self):
|
|
if timed:
|
|
self.time_diff = perf_counter() - self.stime
|
|
self.added_cols = len(self.data.columns) - self.init_cols
|
|
self.assertGreaterEqual(self.added_cols, 1)
|
|
|
|
self.result = self.data[self.data.columns[-self.added_cols:]]
|
|
self.assertIsInstance(self.result, DataFrame)
|
|
self.data.drop(columns=self.result.columns, axis=1, inplace=True)
|
|
|
|
self.speed_test[self.category] = [self.added_cols, self.time_diff]
|
|
|
|
# @skip
|
|
def test_all(self):
|
|
self.category = "All"
|
|
self.data.ta.strategy(verbose=verbose, timed=strategy_timed)
|
|
|
|
def test_all_ordered(self):
|
|
self.category = "All"
|
|
self.data.ta.strategy(ordered=True, verbose=verbose, timed=strategy_timed)
|
|
self.category = "All Ordered" # Rename for Speed Table
|
|
|
|
@skipUnless(verbose, "verbose mode only")
|
|
def test_all_strategy(self):
|
|
self.data.ta.strategy(pandas_ta.AllStrategy, verbose=verbose, timed=strategy_timed)
|
|
|
|
@skipUnless(verbose, "verbose mode only")
|
|
def test_all_name_strategy(self):
|
|
self.category = "All"
|
|
self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
|
|
|
|
# @skipUnless(verbose, "verbose mode only")
|
|
def test_all_multiparams_strategy(self):
|
|
self.category = "All"
|
|
self.data.ta.strategy(self.category, length=10, verbose=verbose, timed=strategy_timed)
|
|
self.data.ta.strategy(self.category, length=50, verbose=verbose, timed=strategy_timed)
|
|
self.data.ta.strategy(self.category, fast=5, slow=10, verbose=verbose, timed=strategy_timed)
|
|
self.category = "All Multiruns with diff Args" # Rename for Speed Table
|
|
|
|
# @skip
|
|
def test_candles_category(self):
|
|
self.category = "Candles"
|
|
self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
|
|
|
|
# @skip
|
|
def test_common(self):
|
|
self.category = "Common"
|
|
self.data.ta.strategy(pandas_ta.CommonStrategy, verbose=verbose, timed=strategy_timed)
|
|
|
|
def test_cycles_category(self):
|
|
self.category = "Cycles"
|
|
self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
|
|
|
|
# @skip
|
|
def test_custom_a(self):
|
|
self.category = "Custom A"
|
|
print()
|
|
print(self.category)
|
|
|
|
momo_bands_sma_ta = [
|
|
{"kind": "cdl_pattern", "name": "tristar"}, # 1
|
|
{"kind": "rsi"}, # 1
|
|
{"kind": "macd"}, # 3
|
|
{"kind": "sma", "length": 50}, # 1
|
|
{"kind": "sma", "length": 200 }, # 1
|
|
{"kind": "bbands", "length": 20}, # 3
|
|
{"kind": "log_return", "cumulative": True}, # 1
|
|
{"kind": "ema", "close": "CUMLOGRET_1", "length": 5, "suffix": "CLR"} # 1
|
|
]
|
|
|
|
custom = pandas_ta.Strategy(
|
|
"Commons with Cumulative Log Return EMA Chain", # name
|
|
momo_bands_sma_ta, # ta
|
|
"Common indicators with specific lengths and a chained indicator", # description
|
|
)
|
|
self.data.ta.strategy(custom, verbose=verbose, timed=strategy_timed)
|
|
self.assertEqual(len(self.data.columns), 15)
|
|
|
|
# @skip
|
|
def test_custom_args_tuple(self):
|
|
self.category = "Custom B"
|
|
|
|
custom_args_ta = [
|
|
{"kind": "ema", "params": (5,)},
|
|
{"kind": "fisher", "params": (13, 7)}
|
|
]
|
|
|
|
custom = pandas_ta.Strategy(
|
|
"Custom Args Tuple",
|
|
custom_args_ta,
|
|
"Allow for easy filling in indicator arguments by argument placement."
|
|
)
|
|
self.data.ta.strategy(custom, verbose=verbose, timed=strategy_timed)
|
|
|
|
def test_custom_col_names_tuple(self):
|
|
self.category = "Custom C"
|
|
|
|
custom_args_ta = [{"kind": "bbands", "col_names": ("LB", "MB", "UB", "BW", "BP")}]
|
|
|
|
custom = pandas_ta.Strategy(
|
|
"Custom Col Numbers Tuple",
|
|
custom_args_ta,
|
|
"Allow for easy renaming of resultant columns",
|
|
)
|
|
self.data.ta.strategy(custom, verbose=verbose, timed=strategy_timed)
|
|
|
|
# @skip
|
|
def test_custom_col_numbers_tuple(self):
|
|
self.category = "Custom D"
|
|
|
|
custom_args_ta = [{"kind": "macd", "col_numbers": (1,)}]
|
|
|
|
custom = pandas_ta.Strategy(
|
|
"Custom Col Numbers Tuple",
|
|
custom_args_ta,
|
|
"Allow for easy selection of resultant columns",
|
|
)
|
|
self.data.ta.strategy(custom, verbose=verbose, timed=strategy_timed)
|
|
|
|
# @skip
|
|
def test_custom_a(self):
|
|
self.category = "Custom E"
|
|
|
|
amat_logret_ta = [
|
|
{"kind": "amat", "fast": 20, "slow": 50 }, # 2
|
|
{"kind": "log_return", "cumulative": True}, # 1
|
|
{"kind": "ema", "close": "CUMLOGRET_1", "length": 5} # 1
|
|
]
|
|
|
|
custom = pandas_ta.Strategy(
|
|
"AMAT Log Returns", # name
|
|
amat_logret_ta, # ta
|
|
"AMAT Log Returns", # description
|
|
)
|
|
self.data.ta.strategy(custom, verbose=verbose, timed=strategy_timed, ordered=True)
|
|
self.data.ta.tsignals(trend=self.data["AMATe_LR_20_50_2"], append=True)
|
|
self.assertEqual(len(self.data.columns), 13)
|
|
|
|
# @skip
|
|
def test_momentum_category(self):
|
|
self.category = "Momentum"
|
|
self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
|
|
|
|
# @skip
|
|
def test_overlap_category(self):
|
|
self.category = "Overlap"
|
|
self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
|
|
|
|
# @skip
|
|
def test_performance_category(self):
|
|
self.category = "Performance"
|
|
self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
|
|
|
|
# @skip
|
|
def test_statistics_category(self):
|
|
self.category = "Statistics"
|
|
self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
|
|
|
|
# @skip
|
|
def test_trend_category(self):
|
|
self.category = "Trend"
|
|
self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
|
|
|
|
# @skip
|
|
def test_volatility_category(self):
|
|
self.category = "Volatility"
|
|
self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
|
|
|
|
# @skip
|
|
def test_volume_category(self):
|
|
self.category = "Volume"
|
|
self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
|
|
|
|
# @skipUnless(verbose, "verbose mode only")
|
|
def test_all_no_multiprocessing(self):
|
|
self.category = "All with No Multiprocessing"
|
|
|
|
cores = self.data.ta.cores
|
|
self.data.ta.cores = 0
|
|
self.data.ta.strategy(verbose=verbose, timed=strategy_timed)
|
|
self.data.ta.cores = cores
|
|
|
|
# @skipUnless(verbose, "verbose mode only")
|
|
def test_custom_no_multiprocessing(self):
|
|
self.category = "Custom A with No Multiprocessing"
|
|
|
|
cores = self.data.ta.cores
|
|
self.data.ta.cores = 0
|
|
|
|
momo_bands_sma_ta = [
|
|
{"kind": "rsi"}, # 1
|
|
{"kind": "macd"}, # 3
|
|
{"kind": "sma", "length": 50}, # 1
|
|
{"kind": "sma", "length": 100, "col_names": "sma100"}, # 1
|
|
{"kind": "sma", "length": 200 }, # 1
|
|
{"kind": "bbands", "length": 20}, # 3
|
|
{"kind": "log_return", "cumulative": True}, # 1
|
|
{"kind": "ema", "close": "CUMLOGRET_1", "length": 5, "suffix": "CLR"}
|
|
]
|
|
|
|
custom = pandas_ta.Strategy(
|
|
"Commons with Cumulative Log Return EMA Chain", # name
|
|
momo_bands_sma_ta, # ta
|
|
"Common indicators with specific lengths and a chained indicator", # description
|
|
)
|
|
self.data.ta.strategy(custom, verbose=verbose, timed=strategy_timed)
|
|
self.data.ta.cores = cores
|