def __init__(self, *args, strategy: str, instrument: str, **kwargs):
super(Andividual, self).__init__(*args, **kwargs)
self.args = args_for_strategy(strategy)
self.instruments = fuzz_product(instrument)
self.strategy = strategy
for _ in self.args:
self.append(50 + (random.random() - 0.5) * 100)
def __init__(self, *args, **kwargs):
super(Andividual, self).__init__(*args, **kwargs)
self.args = args_for_strategy(self.strategy)
# period and periodLength are the same and yield errors
# if both are used.
self.args = [a for a in self.args if a != 'periodLength']
for _ in self.args:
self.append(50 + (random.random() - 0.5) * 100)
def __init__(self, *args,**kwargs):
super(Andividual, self).__init__(*args, **kwargs)
self.args = args_for_strategy(self.strategy)
# period and periodLength are the same and yield errors
# if both are used.
self.args = [a for a in self.args if a != 'periodLength']
for _ in self.args:
self.append(50 + (random.random() - 0.5) * 100)
def __init__(self, *args, **kwargs):
super(Andividual, self).__init__(*args, **kwargs)
self.args = dict(args_for_strategy(self.strategy))
if self.strategy == 'trend_ema':
del self.args['neutral_rate']
for __ in self.args:
dmin = float(self.args[__]) - (float(self.args[__]) / 100 * rnddev)
dmax = float(self.args[__]) + (float(self.args[__]) / 100 * rnddev)
self.args[__] = random.uniform(dmin, dmax)
self.append(self.args[__])
self.args = self.args.keys()
def test_parse_strategies():
asd = b'''
macd
description:
Buy when (MACD - Signal > 0) and sell when (MACD - Signal < 0).
options:
--period=<value> period length (default: 1h)
--min_periods=<value> min. number of history periods (default: 52)
--ema_short_period=<value> number of periods for the shorter EMA (default: 12)
--ema_long_period=<value> number of periods for the longer EMA (default: 26)
--signal_period=<value> number of periods for the signal EMA (default: 9)
--up_trend_threshold=<value> threshold to trigger a buy signal (default: 0)
--down_trend_threshold=<value> threshold to trigger a sold signal (default: 0)
--overbought_rsi_periods=<value> number of periods for overbought RSI (default: 25)
--overbought_rsi=<value> sold when RSI exceeds this value (default: 70)
sar
description:
Parabolic SAR
options:
--period=<value> period length (default: 2m)
--min_periods=<value> min. number of history periods (default: 52)
--sar_af=<value> acceleration factor for parabolic SAR (default: 0.015)
--sar_max_af=<value> max acceleration factor for parabolic SAR (default: 0.3)
trend_ema (default)
description:
Buy when (EMA - last(EMA) > 0) and sell when (EMA - last(EMA) < 0). Optional buy on low RSI.
options:
--period=<value> period length (default: 10m)
--min_periods=<value> min. number of history periods (default: 52)
--trend_ema=<value> number of periods for trend EMA (default: 20)
--neutral_rate=<value> avoid trades if abs(trend_ema) under this float (0 to disable, "auto" for a variable filter) (default: 0.06)
--oversold_rsi_periods=<value> number of periods for oversold RSI (default: 20)
--oversold_rsi=<value> buy when RSI reaches this value (default: 30)
'''
with patch('evaluation.subprocess.check_output', lambda *x, **y: asd):
assert args_for_strategy('sar') == [
'period', 'min_periods', 'sar_af', 'sar_max_af'
]
def test_parse_strategies():
asd = b'''
macd
description:
Buy when (MACD - Signal > 0) and sell when (MACD - Signal < 0).
options:
--period=<value> period length (default: 1h)
--min_periods=<value> min. number of history periods (default: 52)
--ema_short_period=<value> number of periods for the shorter EMA (default: 12)
--ema_long_period=<value> number of periods for the longer EMA (default: 26)
--signal_period=<value> number of periods for the signal EMA (default: 9)
--up_trend_threshold=<value> threshold to trigger a buy signal (default: 0)
--down_trend_threshold=<value> threshold to trigger a sold signal (default: 0)
--overbought_rsi_periods=<value> number of periods for overbought RSI (default: 25)
--overbought_rsi=<value> sold when RSI exceeds this value (default: 70)
sar
description:
Parabolic SAR
options:
--period=<value> period length (default: 2m)
--min_periods=<value> min. number of history periods (default: 52)
--sar_af=<value> acceleration factor for parabolic SAR (default: 0.015)
--sar_max_af=<value> max acceleration factor for parabolic SAR (default: 0.3)
trend_ema (default)
description:
Buy when (EMA - last(EMA) > 0) and sell when (EMA - last(EMA) < 0). Optional buy on low RSI.
options:
--period=<value> period length (default: 10m)
--min_periods=<value> min. number of history periods (default: 52)
--trend_ema=<value> number of periods for trend EMA (default: 20)
--neutral_rate=<value> avoid trades if abs(trend_ema) under this float (0 to disable, "auto" for a variable filter) (default: 0.06)
--oversold_rsi_periods=<value> number of periods for oversold RSI (default: 20)
--oversold_rsi=<value> buy when RSI reaches this value (default: 30)
'''
with patch('evaluation.subprocess.check_output', lambda *x, **y: asd):
assert args_for_strategy('sar') == ['period', 'min_periods', 'sar_af', 'sar_max_af']
def __init__(self, *args, **kwargs):
super(Andividual, self).__init__(*args, **kwargs)
self.args = args_for_strategy(self.strategy)
for _ in self.args:
self.append(50 + (random.random() - 0.5) * 100)
def __init__(self, *args,**kwargs):
super(Andividual, self).__init__(*args, **kwargs)
self.args = args_for_strategy(self.strategy)
for _ in self.args:
self.append(50 + (random.random() - 0.5) * 100)