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Merge branch 'feat/short' into pr/samgermain/5378

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This commit is contained in:
Matthias
2021-09-17 19:24:47 +02:00
parent b752516f65 d680fdf33a
commit 4d558879e9
99 changed files with 1075 additions and 2863 deletions
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2
tests/optimize/conftest.py
View File

@@ -16,7 +16,7 @@ def hyperopt_conf(default_conf):
hyperconf.update({
'datadir': Path(default_conf['datadir']),
'runmode': RunMode.HYPEROPT,
'hyperopt': 'HyperoptTestSepFile',
'strategy': 'HyperoptableStrategy',
'hyperopt_loss': 'ShortTradeDurHyperOptLoss',
'hyperopt_path': str(Path(__file__).parent / 'hyperopts'),
'epochs': 1,

207
tests/optimize/hyperopts/hyperopt_test_sep_file.py
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@@ -1,207 +0,0 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
from functools import reduce
from typing import Any, Callable, Dict, List
import talib.abstract as ta
from pandas import DataFrame
from skopt.space import Categorical, Dimension, Integer
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.optimize.hyperopt_interface import IHyperOpt
class HyperoptTestSepFile(IHyperOpt):
"""
Default hyperopt provided by the Freqtrade bot.
You can override it with your own Hyperopt
"""
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Add several indicators needed for buy and sell strategies defined below.
"""
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
# Minus-DI
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
# SAR
dataframe['sar'] = ta.SAR(dataframe)
return dataframe
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by Hyperopt.
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] < params['mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] < params['rsi-value'])
# TRIGGERS
if 'trigger' in params:
if params['trigger'] == 'boll':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macd'],
dataframe['macdsignal']
))
if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['close'],
dataframe['sar']
))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching buy strategy parameters.
"""
return [
Integer(10, 25, name='mfi-value'),
Integer(15, 45, name='fastd-value'),
Integer(20, 50, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['boll', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
@staticmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the sell strategy parameters to be used by Hyperopt.
"""
def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Sell strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
conditions.append(dataframe['adx'] < params['sell-adx-value'])
if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
# TRIGGERS
if 'sell-trigger' in params:
if params['sell-trigger'] == 'sell-boll':
conditions.append(dataframe['close'] > dataframe['bb_upperband'])
if params['sell-trigger'] == 'sell-macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macdsignal'],
dataframe['macd']
))
if params['sell-trigger'] == 'sell-sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['sar'],
dataframe['close']
))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe
return populate_sell_trend
@staticmethod
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching sell strategy parameters.
"""
return [
Integer(75, 100, name='sell-mfi-value'),
Integer(50, 100, name='sell-fastd-value'),
Integer(50, 100, name='sell-adx-value'),
Integer(60, 100, name='sell-rsi-value'),
Categorical([True, False], name='sell-mfi-enabled'),
Categorical([True, False], name='sell-fastd-enabled'),
Categorical([True, False], name='sell-adx-enabled'),
Categorical([True, False], name='sell-rsi-enabled'),
Categorical(['sell-boll',
'sell-macd_cross_signal',
'sell-sar_reversal'],
name='sell-trigger')
]
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of same method from strategy.
Must align to populate_indicators in this file.
Only used when --spaces does not include buy space.
"""
dataframe.loc[
(
(dataframe['close'] < dataframe['bb_lowerband']) &
(dataframe['mfi'] < 16) &
(dataframe['adx'] > 25) &
(dataframe['rsi'] < 21)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of same method from strategy.
Must align to populate_indicators in this file.
Only used when --spaces does not include sell space.
"""
dataframe.loc[
(
(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd']
)) &
(dataframe['fastd'] > 54)
),
'sell'] = 1
return dataframe

271
tests/optimize/hyperopts/short_hyperopt.py
View File

@@ -1,271 +0,0 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
from functools import reduce
from typing import Any, Callable, Dict, List
import talib.abstract as ta
from pandas import DataFrame
from skopt.space import Categorical, Dimension, Integer
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.optimize.hyperopt_interface import IHyperOpt
class DefaultHyperOpt(IHyperOpt):
"""
Default hyperopt provided by the Freqtrade bot.
You can override it with your own Hyperopt
"""
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Add several indicators needed for buy and sell strategies defined below.
"""
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
# Minus-DI
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
# SAR
dataframe['sar'] = ta.SAR(dataframe)
return dataframe
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by Hyperopt.
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use.
"""
long_conditions = []
short_conditions = []
# GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']:
long_conditions.append(dataframe['mfi'] < params['mfi-value'])
short_conditions.append(dataframe['mfi'] > params['short-mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']:
long_conditions.append(dataframe['fastd'] < params['fastd-value'])
short_conditions.append(dataframe['fastd'] > params['short-fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
long_conditions.append(dataframe['adx'] > params['adx-value'])
short_conditions.append(dataframe['adx'] < params['short-adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
long_conditions.append(dataframe['rsi'] < params['rsi-value'])
short_conditions.append(dataframe['rsi'] > params['short-rsi-value'])
# TRIGGERS
if 'trigger' in params:
if params['trigger'] == 'boll':
long_conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
short_conditions.append(dataframe['close'] > dataframe['bb_upperband'])
if params['trigger'] == 'macd_cross_signal':
long_conditions.append(qtpylib.crossed_above(
dataframe['macd'],
dataframe['macdsignal']
))
short_conditions.append(qtpylib.crossed_below(
dataframe['macd'],
dataframe['macdsignal']
))
if params['trigger'] == 'sar_reversal':
long_conditions.append(qtpylib.crossed_above(
dataframe['close'],
dataframe['sar']
))
short_conditions.append(qtpylib.crossed_below(
dataframe['close'],
dataframe['sar']
))
if long_conditions:
dataframe.loc[
reduce(lambda x, y: x & y, long_conditions),
'buy'] = 1
if short_conditions:
dataframe.loc[
reduce(lambda x, y: x & y, short_conditions),
'enter_short'] = 1
return dataframe
return populate_buy_trend
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching buy strategy parameters.
"""
return [
Integer(10, 25, name='mfi-value'),
Integer(15, 45, name='fastd-value'),
Integer(20, 50, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Integer(75, 90, name='short-mfi-value'),
Integer(55, 85, name='short-fastd-value'),
Integer(50, 80, name='short-adx-value'),
Integer(60, 80, name='short-rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['boll', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
@staticmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the sell strategy parameters to be used by Hyperopt.
"""
def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Sell strategy Hyperopt will build and use.
"""
exit_long_conditions = []
exit_short_conditions = []
# GUARDS AND TRENDS
if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
exit_long_conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
exit_short_conditions.append(dataframe['mfi'] < params['exit-short-mfi-value'])
if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
exit_long_conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
exit_short_conditions.append(dataframe['fastd'] < params['exit-short-fastd-value'])
if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
exit_long_conditions.append(dataframe['adx'] < params['sell-adx-value'])
exit_short_conditions.append(dataframe['adx'] > params['exit-short-adx-value'])
if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
exit_long_conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
exit_short_conditions.append(dataframe['rsi'] < params['exit-short-rsi-value'])
# TRIGGERS
if 'sell-trigger' in params:
if params['sell-trigger'] == 'sell-boll':
exit_long_conditions.append(dataframe['close'] > dataframe['bb_upperband'])
exit_short_conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['sell-trigger'] == 'sell-macd_cross_signal':
exit_long_conditions.append(qtpylib.crossed_above(
dataframe['macdsignal'],
dataframe['macd']
))
exit_short_conditions.append(qtpylib.crossed_below(
dataframe['macdsignal'],
dataframe['macd']
))
if params['sell-trigger'] == 'sell-sar_reversal':
exit_long_conditions.append(qtpylib.crossed_above(
dataframe['sar'],
dataframe['close']
))
exit_short_conditions.append(qtpylib.crossed_below(
dataframe['sar'],
dataframe['close']
))
if exit_long_conditions:
dataframe.loc[
reduce(lambda x, y: x & y, exit_long_conditions),
'sell'] = 1
if exit_short_conditions:
dataframe.loc[
reduce(lambda x, y: x & y, exit_short_conditions),
'exit-short'] = 1
return dataframe
return populate_sell_trend
@staticmethod
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching sell strategy parameters.
"""
return [
Integer(75, 100, name='sell-mfi-value'),
Integer(50, 100, name='sell-fastd-value'),
Integer(50, 100, name='sell-adx-value'),
Integer(60, 100, name='sell-rsi-value'),
Integer(1, 25, name='exit-short-mfi-value'),
Integer(1, 50, name='exit-short-fastd-value'),
Integer(1, 50, name='exit-short-adx-value'),
Integer(1, 40, name='exit-short-rsi-value'),
Categorical([True, False], name='sell-mfi-enabled'),
Categorical([True, False], name='sell-fastd-enabled'),
Categorical([True, False], name='sell-adx-enabled'),
Categorical([True, False], name='sell-rsi-enabled'),
Categorical(['sell-boll',
'sell-macd_cross_signal',
'sell-sar_reversal'],
name='sell-trigger')
]
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of same method from strategy.
Must align to populate_indicators in this file.
Only used when --spaces does not include buy space.
"""
dataframe.loc[
(
(dataframe['close'] < dataframe['bb_lowerband']) &
(dataframe['mfi'] < 16) &
(dataframe['adx'] > 25) &
(dataframe['rsi'] < 21)
),
'buy'] = 1
dataframe.loc[
(
(dataframe['close'] > dataframe['bb_upperband']) &
(dataframe['mfi'] < 84) &
(dataframe['adx'] > 75) &
(dataframe['rsi'] < 79)
),
'enter_short'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of same method from strategy.
Must align to populate_indicators in this file.
Only used when --spaces does not include sell space.
"""
dataframe.loc[
(
(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd']
)) &
(dataframe['fastd'] > 54)
),
'sell'] = 1
dataframe.loc[
(
(qtpylib.crossed_below(
dataframe['macdsignal'], dataframe['macd']
)) &
(dataframe['fastd'] < 46)
),
'exit_short'] = 1
return dataframe

229
tests/optimize/test_hyperopt.py
View File

@@ -17,13 +17,10 @@ from freqtrade.optimize.hyperopt_auto import HyperOptAuto
from freqtrade.optimize.hyperopt_tools import HyperoptTools
from freqtrade.optimize.optimize_reports import generate_strategy_stats
from freqtrade.optimize.space import SKDecimal
from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
from freqtrade.strategy.hyper import IntParameter
from tests.conftest import (get_args, log_has, log_has_re, patch_exchange,
patched_configuration_load_config_file)
from .hyperopts.hyperopt_test_sep_file import HyperoptTestSepFile
# TODO-lev: This file
@@ -34,7 +31,7 @@ def test_setup_hyperopt_configuration_without_arguments(mocker, default_conf, ca
args = [
'hyperopt',
'--config', 'config.json',
'--hyperopt', 'HyperoptTestSepFile',
'--strategy', 'HyperoptableStrategy',
]
config = setup_optimize_configuration(get_args(args), RunMode.HYPEROPT)
@@ -66,7 +63,7 @@ def test_setup_hyperopt_configuration_with_arguments(mocker, default_conf, caplo
args = [
'hyperopt',
'--config', 'config.json',
'--hyperopt', 'HyperoptTestSepFile',
'--strategy', 'HyperoptableStrategy',
'--datadir', '/foo/bar',
'--timeframe', '1m',
'--timerange', ':100',
@@ -118,7 +115,7 @@ def test_setup_hyperopt_configuration_stake_amount(mocker, default_conf) -> None
args = [
'hyperopt',
'--config', 'config.json',
'--hyperopt', 'HyperoptTestSepFile',
'--strategy', 'HyperoptableStrategy',
'--stake-amount', '1',
'--starting-balance', '2'
]
@@ -136,47 +133,6 @@ def test_setup_hyperopt_configuration_stake_amount(mocker, default_conf) -> None
setup_optimize_configuration(get_args(args), RunMode.HYPEROPT)
def test_hyperoptresolver(mocker, default_conf, caplog) -> None:
patched_configuration_load_config_file(mocker, default_conf)
hyperopt = HyperoptTestSepFile
delattr(hyperopt, 'populate_indicators')
delattr(hyperopt, 'populate_buy_trend')
delattr(hyperopt, 'populate_sell_trend')
mocker.patch(
'freqtrade.resolvers.hyperopt_resolver.HyperOptResolver.load_object',
MagicMock(return_value=hyperopt(default_conf))
)
default_conf.update({'hyperopt': 'HyperoptTestSepFile'})
x = HyperOptResolver.load_hyperopt(default_conf)
assert not hasattr(x, 'populate_indicators')
assert not hasattr(x, 'populate_buy_trend')
assert not hasattr(x, 'populate_sell_trend')
assert log_has("Hyperopt class does not provide populate_indicators() method. "
"Using populate_indicators from the strategy.", caplog)
assert log_has("Hyperopt class does not provide populate_sell_trend() method. "
"Using populate_sell_trend from the strategy.", caplog)
assert log_has("Hyperopt class does not provide populate_buy_trend() method. "
"Using populate_buy_trend from the strategy.", caplog)
assert hasattr(x, "ticker_interval") # DEPRECATED
assert hasattr(x, "timeframe")
def test_hyperoptresolver_wrongname(default_conf) -> None:
default_conf.update({'hyperopt': "NonExistingHyperoptClass"})
with pytest.raises(OperationalException, match=r'Impossible to load Hyperopt.*'):
HyperOptResolver.load_hyperopt(default_conf)
def test_hyperoptresolver_noname(default_conf):
default_conf['hyperopt'] = ''
with pytest.raises(OperationalException,
match="No Hyperopt set. Please use `--hyperopt` to specify "
"the Hyperopt class to use."):
HyperOptResolver.load_hyperopt(default_conf)
def test_start_not_installed(mocker, default_conf, import_fails) -> None:
start_mock = MagicMock()
patched_configuration_load_config_file(mocker, default_conf)
@@ -187,9 +143,7 @@ def test_start_not_installed(mocker, default_conf, import_fails) -> None:
args = [
'hyperopt',
'--config', 'config.json',
'--hyperopt', 'HyperoptTestSepFile',
'--hyperopt-path',
str(Path(__file__).parent / "hyperopts"),
'--strategy', 'HyperoptableStrategy',
'--epochs', '5',
'--hyperopt-loss', 'SharpeHyperOptLossDaily',
]
@@ -199,7 +153,7 @@ def test_start_not_installed(mocker, default_conf, import_fails) -> None:
start_hyperopt(pargs)
def test_start(mocker, hyperopt_conf, caplog) -> None:
def test_start_no_hyperopt_allowed(mocker, hyperopt_conf, caplog) -> None:
start_mock = MagicMock()
patched_configuration_load_config_file(mocker, hyperopt_conf)
mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.start', start_mock)
@@ -213,10 +167,8 @@ def test_start(mocker, hyperopt_conf, caplog) -> None:
'--epochs', '5'
]
pargs = get_args(args)
start_hyperopt(pargs)
assert log_has('Starting freqtrade in Hyperopt mode', caplog)
assert start_mock.call_count == 1
with pytest.raises(OperationalException, match=r"Using separate Hyperopt files has been.*"):
start_hyperopt(pargs)
def test_start_no_data(mocker, hyperopt_conf) -> None:
@@ -228,11 +180,11 @@ def test_start_no_data(mocker, hyperopt_conf) -> None:
)
patch_exchange(mocker)
# TODO: migrate to strategy-based hyperopt
args = [
'hyperopt',
'--config', 'config.json',
'--hyperopt', 'HyperoptTestSepFile',
'--strategy', 'HyperoptableStrategy',
'--hyperopt-loss', 'SharpeHyperOptLossDaily',
'--epochs', '5'
]
@@ -250,7 +202,7 @@ def test_start_filelock(mocker, hyperopt_conf, caplog) -> None:
args = [
'hyperopt',
'--config', 'config.json',
'--hyperopt', 'HyperoptTestSepFile',
'--strategy', 'HyperoptableStrategy',
'--hyperopt-loss', 'SharpeHyperOptLossDaily',
'--epochs', '5'
]
@@ -430,74 +382,14 @@ def test_hyperopt_format_results(hyperopt):
def test_populate_indicators(hyperopt, testdatadir) -> None:
data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], fill_up_missing=True)
dataframes = hyperopt.backtesting.strategy.advise_all_indicators(data)
dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
{'pair': 'UNITTEST/BTC'})
dataframe = dataframes['UNITTEST/BTC']
# Check if some indicators are generated. We will not test all of them
assert 'adx' in dataframe
assert 'mfi' in dataframe
assert 'macd' in dataframe
assert 'rsi' in dataframe
def test_buy_strategy_generator(hyperopt, testdatadir) -> None:
data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], fill_up_missing=True)
dataframes = hyperopt.backtesting.strategy.advise_all_indicators(data)
dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
{'pair': 'UNITTEST/BTC'})
populate_buy_trend = hyperopt.custom_hyperopt.buy_strategy_generator(
{
'adx-value': 20,
'fastd-value': 20,
'mfi-value': 20,
'rsi-value': 20,
'short-adx-value': 80,
'short-fastd-value': 80,
'short-mfi-value': 80,
'short-rsi-value': 80,
'adx-enabled': True,
'fastd-enabled': True,
'mfi-enabled': True,
'rsi-enabled': True,
'trigger': 'bb_lower'
}
)
result = populate_buy_trend(dataframe, {'pair': 'UNITTEST/BTC'})
# Check if some indicators are generated. We will not test all of them
assert 'buy' in result
assert 1 in result['buy']
def test_sell_strategy_generator(hyperopt, testdatadir) -> None:
data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], fill_up_missing=True)
dataframes = hyperopt.backtesting.strategy.advise_all_indicators(data)
dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
{'pair': 'UNITTEST/BTC'})
populate_sell_trend = hyperopt.custom_hyperopt.sell_strategy_generator(
{
'sell-adx-value': 20,
'sell-fastd-value': 75,
'sell-mfi-value': 80,
'sell-rsi-value': 20,
'exit-short-adx-value': 80,
'exit-short-fastd-value': 25,
'exit-short-mfi-value': 20,
'exit-short-rsi-value': 80,
'sell-adx-enabled': True,
'sell-fastd-enabled': True,
'sell-mfi-enabled': True,
'sell-rsi-enabled': True,
'sell-trigger': 'sell-bb_upper'
}
)
result = populate_sell_trend(dataframe, {'pair': 'UNITTEST/BTC'})
# Check if some indicators are generated. We will not test all of them
print(result)
assert 'sell' in result
assert 1 in result['sell']
def test_generate_optimizer(mocker, hyperopt_conf) -> None:
hyperopt_conf.update({'spaces': 'all',
'hyperopt_min_trades': 1,
@@ -538,24 +430,12 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
mocker.patch('freqtrade.optimize.hyperopt.load', return_value={'XRP/BTC': None})
optimizer_param = {
'adx-value': 0,
'fastd-value': 35,
'mfi-value': 0,
'rsi-value': 0,
'adx-enabled': False,
'fastd-enabled': True,
'mfi-enabled': False,
'rsi-enabled': False,
'trigger': 'macd_cross_signal',
'sell-adx-value': 0,
'sell-fastd-value': 75,
'sell-mfi-value': 0,
'sell-rsi-value': 0,
'sell-adx-enabled': False,
'sell-fastd-enabled': True,
'sell-mfi-enabled': False,
'sell-rsi-enabled': False,
'sell-trigger': 'macd_cross_signal',
'buy_plusdi': 0.02,
'buy_rsi': 35,
'sell_minusdi': 0.02,
'sell_rsi': 75,
'protection_cooldown_lookback': 20,
'protection_enabled': True,
'roi_t1': 60.0,
'roi_t2': 30.0,
'roi_t3': 20.0,
@@ -575,29 +455,19 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
'0.00003100 BTC ( 0.00%). '
'Avg duration 0:50:00 min.'
),
'params_details': {'buy': {'adx-enabled': False,
'adx-value': 0,
'fastd-enabled': True,
'fastd-value': 35,
'mfi-enabled': False,
'mfi-value': 0,
'rsi-enabled': False,
'rsi-value': 0,
'trigger': 'macd_cross_signal'},
'params_details': {'buy': {'buy_plusdi': 0.02,
'buy_rsi': 35,
},
'roi': {"0": 0.12000000000000001,
"20.0": 0.02,
"50.0": 0.01,
"110.0": 0},
'protection': {},
'sell': {'sell-adx-enabled': False,
'sell-adx-value': 0,
'sell-fastd-enabled': True,
'sell-fastd-value': 75,
'sell-mfi-enabled': False,
'sell-mfi-value': 0,
'sell-rsi-enabled': False,
'sell-rsi-value': 0,
'sell-trigger': 'macd_cross_signal'},
'protection': {'protection_cooldown_lookback': 20,
'protection_enabled': True,
},
'sell': {'sell_minusdi': 0.02,
'sell_rsi': 75,
},
'stoploss': {'stoploss': -0.4},
'trailing': {'trailing_only_offset_is_reached': False,
'trailing_stop': True,
@@ -819,11 +689,6 @@ def test_simplified_interface_roi_stoploss(mocker, hyperopt_conf, capsys) -> Non
hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
del hyperopt.custom_hyperopt.__class__.buy_strategy_generator
del hyperopt.custom_hyperopt.__class__.sell_strategy_generator
del hyperopt.custom_hyperopt.__class__.indicator_space
del hyperopt.custom_hyperopt.__class__.sell_indicator_space
hyperopt.start()
parallel.assert_called_once()
@@ -854,16 +719,14 @@ def test_simplified_interface_all_failed(mocker, hyperopt_conf) -> None:
hyperopt_conf.update({'spaces': 'all', })
mocker.patch('freqtrade.optimize.hyperopt_auto.HyperOptAuto._generate_indicator_space',
return_value=[])
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
del hyperopt.custom_hyperopt.__class__.buy_strategy_generator
del hyperopt.custom_hyperopt.__class__.sell_strategy_generator
del hyperopt.custom_hyperopt.__class__.indicator_space
del hyperopt.custom_hyperopt.__class__.sell_indicator_space
with pytest.raises(OperationalException, match=r"The 'buy' space is included into *"):
with pytest.raises(OperationalException, match=r"The 'protection' space is included into *"):
hyperopt.start()
@@ -900,11 +763,6 @@ def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
# TODO: sell_strategy_generator() is actually not called because
# run_optimizer_parallel() is mocked
del hyperopt.custom_hyperopt.__class__.sell_strategy_generator
del hyperopt.custom_hyperopt.__class__.sell_indicator_space
hyperopt.start()
parallel.assert_called_once()
@@ -954,11 +812,6 @@ def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
# TODO: buy_strategy_generator() is actually not called because
# run_optimizer_parallel() is mocked
del hyperopt.custom_hyperopt.__class__.buy_strategy_generator
del hyperopt.custom_hyperopt.__class__.indicator_space
hyperopt.start()
parallel.assert_called_once()
@@ -975,13 +828,12 @@ def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
assert hasattr(hyperopt, "position_stacking")
@pytest.mark.parametrize("method,space", [
('buy_strategy_generator', 'buy'),
('indicator_space', 'buy'),
('sell_strategy_generator', 'sell'),
('sell_indicator_space', 'sell'),
@pytest.mark.parametrize("space", [
('buy'),
('sell'),
('protection'),
])
def test_simplified_interface_failed(mocker, hyperopt_conf, method, space) -> None:
def test_simplified_interface_failed(mocker, hyperopt_conf, space) -> None:
mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
@@ -990,6 +842,8 @@ def test_simplified_interface_failed(mocker, hyperopt_conf, method, space) -> No
'freqtrade.optimize.hyperopt.get_timerange',
MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
)
mocker.patch('freqtrade.optimize.hyperopt_auto.HyperOptAuto._generate_indicator_space',
return_value=[])
patch_exchange(mocker)
@@ -999,8 +853,6 @@ def test_simplified_interface_failed(mocker, hyperopt_conf, method, space) -> No
hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
delattr(hyperopt.custom_hyperopt.__class__, method)
with pytest.raises(OperationalException, match=f"The '{space}' space is included into *"):
hyperopt.start()
@@ -1010,7 +862,6 @@ def test_in_strategy_auto_hyperopt(mocker, hyperopt_conf, tmpdir, fee) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
(Path(tmpdir) / 'hyperopt_results').mkdir(parents=True)
# No hyperopt needed
del hyperopt_conf['hyperopt']
hyperopt_conf.update({
'strategy': 'HyperoptableStrategy',
'user_data_dir': Path(tmpdir),
@@ -1036,6 +887,10 @@ def test_in_strategy_auto_hyperopt(mocker, hyperopt_conf, tmpdir, fee) -> None:
assert hyperopt.backtesting.strategy.buy_rsi.value != 35
assert hyperopt.backtesting.strategy.sell_rsi.value != 74
hyperopt.custom_hyperopt.generate_estimator = lambda *args, **kwargs: 'ET1'
with pytest.raises(OperationalException, match="Estimator ET1 not supported."):
hyperopt.get_optimizer([], 2)
def test_SKDecimal():
space = SKDecimal(1, 2, decimals=2)