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Merge branch 'feat/short' of https://github.com/freqtrade/freqtrade into feat/short

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Sam Germain 2021-08-07 01:43:56 -06:00
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101
docs/hyperopt.md
View File

@ -253,7 +253,7 @@ We continue to define hyperoptable parameters:
class MyAwesomeStrategy(IStrategy):
buy_adx = DecimalParameter(20, 40, decimals=1, default=30.1, space="buy")
buy_rsi = IntParameter(20, 40, default=30, space="buy")
buy_adx_enabled = CategoricalParameter([True, False], default=True, space="buy")
buy_adx_enabled = BooleanParameter(default=True, space="buy")
buy_rsi_enabled = CategoricalParameter([True, False], default=False, space="buy")
buy_trigger = CategoricalParameter(["bb_lower", "macd_cross_signal"], default="bb_lower", space="buy")
```
@ -316,6 +316,7 @@ There are four parameter types each suited for different purposes.
* `DecimalParameter` - defines a floating point parameter with a limited number of decimals (default 3). Should be preferred instead of `RealParameter` in most cases.
* `RealParameter` - defines a floating point parameter with upper and lower boundaries and no precision limit. Rarely used as it creates a space with a near infinite number of possibilities.
* `CategoricalParameter` - defines a parameter with a predetermined number of choices.
* `BooleanParameter` - Shorthand for `CategoricalParameter([True, False])` - great for "enable" parameters.
!!! Tip "Disabling parameter optimization"
Each parameter takes two boolean parameters:
@ -326,7 +327,7 @@ There are four parameter types each suited for different purposes.
!!! Warning
Hyperoptable parameters cannot be used in `populate_indicators` - as hyperopt does not recalculate indicators for each epoch, so the starting value would be used in this case.
### Optimizing an indicator parameter
## Optimizing an indicator parameter
Assuming you have a simple strategy in mind - a EMA cross strategy (2 Moving averages crossing) - and you'd like to find the ideal parameters for this strategy.
@ -336,8 +337,8 @@ from functools import reduce
import talib.abstract as ta
from freqtrade.strategy import IStrategy
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter)
import freqtrade.vendor.qtpylib.indicators as qtpylib
class MyAwesomeStrategy(IStrategy):
@ -413,6 +414,98 @@ While this strategy is most likely too simple to provide consistent profit, it s
While this may slow down the hyperopt startup speed, the overall performance will increase as the Hyperopt execution itself may pick the same value for multiple epochs (changing other values).
You should however try to use space ranges as small as possible. Every new column will require more memory, and every possibility hyperopt can try will increase the search space.
## Optimizing protections
Freqtrade can also optimize protections. How you optimize protections is up to you, and the following should be considered as example only.
The strategy will simply need to define the "protections" entry as property returning a list of protection configurations.
``` python
from pandas import DataFrame
from functools import reduce
import talib.abstract as ta
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter)
import freqtrade.vendor.qtpylib.indicators as qtpylib
class MyAwesomeStrategy(IStrategy):
stoploss = -0.05
timeframe = '15m'
# Define the parameter spaces
cooldown_lookback = IntParameter(2, 48, default=5, space="protection", optimize=True)
stop_duration = IntParameter(12, 200, default=5, space="protection", optimize=True)
use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True)
@property
def protections(self):
prot = []
prot.append({
"method": "CooldownPeriod",
"stop_duration_candles": self.cooldown_lookback.value
})
if self.use_stop_protection.value:
prot.append({
"method": "StoplossGuard",
"lookback_period_candles": 24 * 3,
"trade_limit": 4,
"stop_duration_candles": self.stop_duration.value,
"only_per_pair": False
})
return protection
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# ...
```
You can then run hyperopt as follows:
`freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy MyAwesomeStrategy --spaces protection`
!!! Note
The protection space is not part of the default space, and is only available with the Parameters Hyperopt interface, not with the legacy hyperopt interface (which required separate hyperopt files).
Freqtrade will also automatically change the "--enable-protections" flag if the protection space is selected.
!!! Warning
If protections are defined as property, entries from the configuration will be ignored.
It is therefore recommended to not define protections in the configuration.
### Migrating from previous property setups
A migration from a previous setup is pretty simple, and can be accomplished by converting the protections entry to a property.
In simple terms, the following configuration will be converted to the below.
``` python
class MyAwesomeStrategy(IStrategy):
protections = [
{
"method": "CooldownPeriod",
"stop_duration_candles": 4
}
]
```
Result
``` python
class MyAwesomeStrategy(IStrategy):
@property
def protections(self):
return [
{
"method": "CooldownPeriod",
"stop_duration_candles": 4
}
]
```
You will then obviously also change potential interesting entries to parameters to allow hyper-optimization.
## Loss-functions
Each hyperparameter tuning requires a target. This is usually defined as a loss function (sometimes also called objective function), which should decrease for more desirable results, and increase for bad results.

149
docs/includes/protections.md
View File

@ -15,6 +15,10 @@ All protection end times are rounded up to the next candle to avoid sudden, unex
!!! Note "Backtesting"
Protections are supported by backtesting and hyperopt, but must be explicitly enabled by using the `--enable-protections` flag.
!!! Warning "Setting protections from the configuration"
Setting protections from the configuration via `"protections": [],` key should be considered deprecated and will be removed in a future version.
It is also no longer guaranteed that your protections apply to the strategy in cases where the strategy defines [protections as property](hyperopt.md#optimizing-protections).
### Available Protections
* [`StoplossGuard`](#stoploss-guard) Stop trading if a certain amount of stoploss occurred within a certain time window.
@ -47,15 +51,17 @@ This applies across all pairs, unless `only_per_pair` is set to true, which will
The below example stops trading for all pairs for 4 candles after the last trade if the bot hit stoploss 4 times within the last 24 candles.
``` python
protections = [
{
"method": "StoplossGuard",
"lookback_period_candles": 24,
"trade_limit": 4,
"stop_duration_candles": 4,
"only_per_pair": False
}
]
@property
def protections(self):
return [
{
"method": "StoplossGuard",
"lookback_period_candles": 24,
"trade_limit": 4,
"stop_duration_candles": 4,
"only_per_pair": False
}
]
```
!!! Note
@ -69,15 +75,17 @@ protections = [
The below sample stops trading for 12 candles if max-drawdown is > 20% considering all pairs - with a minimum of `trade_limit` trades - within the last 48 candles. If desired, `lookback_period` and/or `stop_duration` can be used.
``` python
protections = [
{
"method": "MaxDrawdown",
"lookback_period_candles": 48,
"trade_limit": 20,
"stop_duration_candles": 12,
"max_allowed_drawdown": 0.2
},
]
@property
def protections(self):
return [
{
"method": "MaxDrawdown",
"lookback_period_candles": 48,
"trade_limit": 20,
"stop_duration_candles": 12,
"max_allowed_drawdown": 0.2
},
]
```
#### Low Profit Pairs
@ -88,15 +96,17 @@ If that ratio is below `required_profit`, that pair will be locked for `stop_dur
The below example will stop trading a pair for 60 minutes if the pair does not have a required profit of 2% (and a minimum of 2 trades) within the last 6 candles.
``` python
protections = [
{
"method": "LowProfitPairs",
"lookback_period_candles": 6,
"trade_limit": 2,
"stop_duration": 60,
"required_profit": 0.02
}
]
@property
def protections(self):
return [
{
"method": "LowProfitPairs",
"lookback_period_candles": 6,
"trade_limit": 2,
"stop_duration": 60,
"required_profit": 0.02
}
]
```
#### Cooldown Period
@ -106,12 +116,14 @@ protections = [
The below example will stop trading a pair for 2 candles after closing a trade, allowing this pair to "cool down".
``` python
protections = [
{
"method": "CooldownPeriod",
"stop_duration_candles": 2
}
]
@property
def protections(self):
return [
{
"method": "CooldownPeriod",
"stop_duration_candles": 2
}
]
```
!!! Note
@ -136,39 +148,42 @@ from freqtrade.strategy import IStrategy
class AwesomeStrategy(IStrategy)
timeframe = '1h'
protections = [
{
"method": "CooldownPeriod",
"stop_duration_candles": 5
},
{
"method": "MaxDrawdown",
"lookback_period_candles": 48,
"trade_limit": 20,
"stop_duration_candles": 4,
"max_allowed_drawdown": 0.2
},
{
"method": "StoplossGuard",
"lookback_period_candles": 24,
"trade_limit": 4,
"stop_duration_candles": 2,
"only_per_pair": False
},
{
"method": "LowProfitPairs",
"lookback_period_candles": 6,
"trade_limit": 2,
"stop_duration_candles": 60,
"required_profit": 0.02
},
{
"method": "LowProfitPairs",
"lookback_period_candles": 24,
"trade_limit": 4,
"stop_duration_candles": 2,
"required_profit": 0.01
}
]
@property
def protections(self):
return [
{
"method": "CooldownPeriod",
"stop_duration_candles": 5
},
{
"method": "MaxDrawdown",
"lookback_period_candles": 48,
"trade_limit": 20,
"stop_duration_candles": 4,
"max_allowed_drawdown": 0.2
},
{
"method": "StoplossGuard",
"lookback_period_candles": 24,
"trade_limit": 4,
"stop_duration_candles": 2,
"only_per_pair": False
},
{
"method": "LowProfitPairs",
"lookback_period_candles": 6,
"trade_limit": 2,
"stop_duration_candles": 60,
"required_profit": 0.02
},
{
"method": "LowProfitPairs",
"lookback_period_candles": 24,
"trade_limit": 4,
"stop_duration_candles": 2,
"required_profit": 0.01
}
]
# ...
```

2
freqtrade/commands/cli_options.py
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@ -218,7 +218,7 @@ AVAILABLE_CLI_OPTIONS = {
"spaces": Arg(
'--spaces',
help='Specify which parameters to hyperopt. Space-separated list.',
choices=['all', 'buy', 'sell', 'roi', 'stoploss', 'trailing', 'default'],
choices=['all', 'buy', 'sell', 'roi', 'stoploss', 'trailing', 'protection', 'default'],
nargs='+',
default='default',
),

3
freqtrade/configuration/deprecated_settings.py
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@ -110,3 +110,6 @@ def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
"Please remove 'ticker_interval' from your configuration to continue operating."
)
config['timeframe'] = config['ticker_interval']
if 'protections' in config:
logger.warning("DEPRECATED: Setting 'protections' in the configuration is deprecated.")

3
freqtrade/optimize/backtesting.py
View File

@ -146,6 +146,8 @@ class Backtesting:
# since a "perfect" stoploss-sell is assumed anyway
# And the regular "stoploss" function would not apply to that case
self.strategy.order_types['stoploss_on_exchange'] = False
def _load_protections(self, strategy: IStrategy):
if self.config.get('enable_protections', False):
conf = self.config
if hasattr(strategy, 'protections'):
@ -194,6 +196,7 @@ class Backtesting:
Trade.reset_trades()
self.rejected_trades = 0
self.dataprovider.clear_cache()
self._load_protections(self.strategy)
def check_abort(self):
"""

19
freqtrade/optimize/hyperopt.py
View File

@ -66,6 +66,7 @@ class Hyperopt:
def __init__(self, config: Dict[str, Any]) -> None:
self.buy_space: List[Dimension] = []
self.sell_space: List[Dimension] = []
self.protection_space: List[Dimension] = []
self.roi_space: List[Dimension] = []
self.stoploss_space: List[Dimension] = []
self.trailing_space: List[Dimension] = []
@ -191,6 +192,8 @@ class Hyperopt:
result['buy'] = {p.name: params.get(p.name) for p in self.buy_space}
if HyperoptTools.has_space(self.config, 'sell'):
result['sell'] = {p.name: params.get(p.name) for p in self.sell_space}
if HyperoptTools.has_space(self.config, 'protection'):
result['protection'] = {p.name: params.get(p.name) for p in self.protection_space}
if HyperoptTools.has_space(self.config, 'roi'):
result['roi'] = {str(k): v for k, v in
self.custom_hyperopt.generate_roi_table(params).items()}
@ -241,6 +244,12 @@ class Hyperopt:
"""
Assign the dimensions in the hyperoptimization space.
"""
if self.auto_hyperopt and HyperoptTools.has_space(self.config, 'protection'):
# Protections can only be optimized when using the Parameter interface
logger.debug("Hyperopt has 'protection' space")
# Enable Protections if protection space is selected.
self.config['enable_protections'] = True
self.protection_space = self.custom_hyperopt.protection_space()
if HyperoptTools.has_space(self.config, 'buy'):
logger.debug("Hyperopt has 'buy' space")
@ -261,8 +270,8 @@ class Hyperopt:
if HyperoptTools.has_space(self.config, 'trailing'):
logger.debug("Hyperopt has 'trailing' space")
self.trailing_space = self.custom_hyperopt.trailing_space()
self.dimensions = (self.buy_space + self.sell_space + self.roi_space +
self.stoploss_space + self.trailing_space)
self.dimensions = (self.buy_space + self.sell_space + self.protection_space
+ self.roi_space + self.stoploss_space + self.trailing_space)
def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
"""
@ -282,6 +291,12 @@ class Hyperopt:
self.backtesting.strategy.advise_sell = ( # type: ignore
self.custom_hyperopt.sell_strategy_generator(params_dict))
if HyperoptTools.has_space(self.config, 'protection'):
for attr_name, attr in self.backtesting.strategy.enumerate_parameters('protection'):
if attr.optimize:
# noinspection PyProtectedMember
attr.value = params_dict[attr_name]
if HyperoptTools.has_space(self.config, 'roi'):
self.backtesting.strategy.minimal_roi = ( # type: ignore
self.custom_hyperopt.generate_roi_table(params_dict))

3
freqtrade/optimize/hyperopt_auto.py
View File

@ -73,6 +73,9 @@ class HyperOptAuto(IHyperOpt):
def sell_indicator_space(self) -> List['Dimension']:
return self._get_indicator_space('sell', 'sell_indicator_space')
def protection_space(self) -> List['Dimension']:
return self._get_indicator_space('protection', 'indicator_space')
def generate_roi_table(self, params: Dict) -> Dict[int, float]:
return self._get_func('generate_roi_table')(params)

7
freqtrade/optimize/hyperopt_interface.py
View File

@ -57,6 +57,13 @@ class IHyperOpt(ABC):
"""
raise OperationalException(_format_exception_message('sell_strategy_generator', 'sell'))
def protection_space(self) -> List[Dimension]:
"""
Create a protection space.
Only supported by the Parameter interface.
"""
raise OperationalException(_format_exception_message('indicator_space', 'protection'))
def indicator_space(self) -> List[Dimension]:
"""
Create an indicator space.

8
freqtrade/optimize/hyperopt_tools.py
View File

@ -82,8 +82,8 @@ class HyperoptTools():
"""
Tell if the space value is contained in the configuration
"""
# The 'trailing' space is not included in the 'default' set of spaces
if space == 'trailing':
# 'trailing' and 'protection spaces are not included in the 'default' set of spaces
if space in ('trailing', 'protection'):
return any(s in config['spaces'] for s in [space, 'all'])
else:
return any(s in config['spaces'] for s in [space, 'all', 'default'])
@ -149,7 +149,7 @@ class HyperoptTools():
if print_json:
result_dict: Dict = {}
for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
for s in ['buy', 'sell', 'protection', 'roi', 'stoploss', 'trailing']:
HyperoptTools._params_update_for_json(result_dict, params, non_optimized, s)
print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
@ -158,6 +158,8 @@ class HyperoptTools():
non_optimized)
HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:",
non_optimized)
HyperoptTools._params_pretty_print(params, 'protection',
"Protection hyperspace params:", non_optimized)
HyperoptTools._params_pretty_print(params, 'roi', "ROI table:", non_optimized)
HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:", non_optimized)
HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:", non_optimized)

9
freqtrade/plugins/protections/iprotection.py
View File

@ -25,19 +25,22 @@ class IProtection(LoggingMixin, ABC):
def __init__(self, config: Dict[str, Any], protection_config: Dict[str, Any]) -> None:
self._config = config
self._protection_config = protection_config
self._stop_duration_candles: Optional[int] = None
self._lookback_period_candles: Optional[int] = None
tf_in_min = timeframe_to_minutes(config['timeframe'])
if 'stop_duration_candles' in protection_config:
self._stop_duration_candles = protection_config.get('stop_duration_candles', 1)
self._stop_duration_candles = int(protection_config.get('stop_duration_candles', 1))
self._stop_duration = (tf_in_min * self._stop_duration_candles)
else:
self._stop_duration_candles = None
self._stop_duration = protection_config.get('stop_duration', 60)
if 'lookback_period_candles' in protection_config:
self._lookback_period_candles = protection_config.get('lookback_period_candles', 1)
self._lookback_period_candles = int(protection_config.get('lookback_period_candles', 1))
self._lookback_period = tf_in_min * self._lookback_period_candles
else:
self._lookback_period_candles = None
self._lookback_period = protection_config.get('lookback_period', 60)
self._lookback_period = int(protection_config.get('lookback_period', 60))
LoggingMixin.__init__(self, logger)

2
freqtrade/resolvers/strategy_resolver.py
View File

@ -119,7 +119,7 @@ class StrategyResolver(IResolver):
- default (if not None)
"""
if (attribute in config
and not isinstance(getattr(type(strategy), 'my_property', None), property)):
and not isinstance(getattr(type(strategy), attribute, None), property)):
# Ensure Properties are not overwritten
setattr(strategy, attribute, config[attribute])
logger.info("Override strategy '%s' with value in config file: %s.",

4
freqtrade/strategy/__init__.py
View File

@ -1,7 +1,7 @@
# flake8: noqa: F401
from freqtrade.exchange import (timeframe_to_minutes, timeframe_to_msecs, timeframe_to_next_date,
timeframe_to_prev_date, timeframe_to_seconds)
from freqtrade.strategy.hyper import (CategoricalParameter, DecimalParameter, IntParameter,
RealParameter)
from freqtrade.strategy.hyper import (BooleanParameter, CategoricalParameter, DecimalParameter,
IntParameter, RealParameter)
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.strategy_helper import merge_informative_pair, stoploss_from_open

39
freqtrade/strategy/hyper.py
View File

@ -270,6 +270,28 @@ class CategoricalParameter(BaseParameter):
return [self.value]
class BooleanParameter(CategoricalParameter):
def __init__(self, *, default: Optional[Any] = None,
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable Boolean Parameter.
It's a shortcut to `CategoricalParameter([True, False])`.
:param default: A default value. If not specified, first item from specified space will be
used.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter field
name is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.Categorical.
"""
categories = [True, False]
super().__init__(categories=categories, default=default, space=space, optimize=optimize,
load=load, **kwargs)
class HyperStrategyMixin(object):
"""
A helper base class which allows HyperOptAuto class to reuse implementations of buy/sell
@ -283,6 +305,7 @@ class HyperStrategyMixin(object):
self.config = config
self.ft_buy_params: List[BaseParameter] = []
self.ft_sell_params: List[BaseParameter] = []
self.ft_protection_params: List[BaseParameter] = []
self._load_hyper_params(config.get('runmode') == RunMode.HYPEROPT)
@ -292,11 +315,12 @@ class HyperStrategyMixin(object):
:param category:
:return:
"""
if category not in ('buy', 'sell', None):
raise OperationalException('Category must be one of: "buy", "sell", None.')
if category not in ('buy', 'sell', 'protection', None):
raise OperationalException(
'Category must be one of: "buy", "sell", "protection", None.')
if category is None:
params = self.ft_buy_params + self.ft_sell_params
params = self.ft_buy_params + self.ft_sell_params + self.ft_protection_params
else:
params = getattr(self, f"ft_{category}_params")
@ -324,9 +348,10 @@ class HyperStrategyMixin(object):
params: Dict = {
'buy': list(cls.detect_parameters('buy')),
'sell': list(cls.detect_parameters('sell')),
'protection': list(cls.detect_parameters('protection')),
}
params.update({
'count': len(params['buy'] + params['sell'])
'count': len(params['buy'] + params['sell'] + params['protection'])
})
return params
@ -340,9 +365,12 @@ class HyperStrategyMixin(object):
self._ft_params_from_file = params
buy_params = deep_merge_dicts(params.get('buy', {}), getattr(self, 'buy_params', {}))
sell_params = deep_merge_dicts(params.get('sell', {}), getattr(self, 'sell_params', {}))
protection_params = deep_merge_dicts(params.get('protection', {}),
getattr(self, 'protection_params', {}))
self._load_params(buy_params, 'buy', hyperopt)
self._load_params(sell_params, 'sell', hyperopt)
self._load_params(protection_params, 'protection', hyperopt)
def load_params_from_file(self) -> Dict:
filename_str = getattr(self, '__file__', '')
@ -397,7 +425,8 @@ class HyperStrategyMixin(object):
"""
params = {
'buy': {},
'sell': {}
'sell': {},
'protection': {},
}
for name, p in self.enumerate_parameters():
if not p.optimize or not p.in_space:

4
freqtrade/templates/base_strategy.py.j2
View File

@ -6,8 +6,8 @@ import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from freqtrade.strategy import IStrategy
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter)
# --------------------------------
# Add your lib to import here

4
freqtrade/templates/sample_strategy.py
View File

@ -6,8 +6,8 @@ import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from freqtrade.strategy import IStrategy
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter)
# --------------------------------
# Add your lib to import here

11
tests/optimize/test_hyperopt.py
View File

@ -577,6 +577,7 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
"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,
@ -592,7 +593,7 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
'trailing_stop_positive': 0.02,
'trailing_stop_positive_offset': 0.07}},
'params_dict': optimizer_param,
'params_not_optimized': {'buy': {}, 'sell': {}},
'params_not_optimized': {'buy': {}, 'protection': {}, 'sell': {}},
'results_metrics': ANY,
'total_profit': 3.1e-08
}
@ -1002,6 +1003,8 @@ def test_in_strategy_auto_hyperopt(mocker, hyperopt_conf, tmpdir, fee) -> None:
hyperopt_conf.update({
'strategy': 'HyperoptableStrategy',
'user_data_dir': Path(tmpdir),
'hyperopt_random_state': 42,
'spaces': ['all']
})
hyperopt = Hyperopt(hyperopt_conf)
assert isinstance(hyperopt.custom_hyperopt, HyperOptAuto)
@ -1009,12 +1012,18 @@ def test_in_strategy_auto_hyperopt(mocker, hyperopt_conf, tmpdir, fee) -> None:
assert hyperopt.backtesting.strategy.buy_rsi.in_space is True
assert hyperopt.backtesting.strategy.buy_rsi.value == 35
assert hyperopt.backtesting.strategy.sell_rsi.value == 74
assert hyperopt.backtesting.strategy.protection_cooldown_lookback.value == 30
buy_rsi_range = hyperopt.backtesting.strategy.buy_rsi.range
assert isinstance(buy_rsi_range, range)
# Range from 0 - 50 (inclusive)
assert len(list(buy_rsi_range)) == 51
hyperopt.start()
# All values should've changed.
assert hyperopt.backtesting.strategy.protection_cooldown_lookback.value != 30
assert hyperopt.backtesting.strategy.buy_rsi.value != 35
assert hyperopt.backtesting.strategy.sell_rsi.value != 74
def test_SKDecimal():

15
tests/strategy/strats/hyperoptable_strategy.py
View File

@ -4,7 +4,8 @@ import talib.abstract as ta
from pandas import DataFrame
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, RealParameter
from freqtrade.strategy import (BooleanParameter, DecimalParameter, IntParameter, IStrategy,
RealParameter)
class HyperoptableStrategy(IStrategy):
@ -64,6 +65,18 @@ class HyperoptableStrategy(IStrategy):
sell_rsi = IntParameter(low=50, high=100, default=70, space='sell')
sell_minusdi = DecimalParameter(low=0, high=1, default=0.5001, decimals=3, space='sell',
load=False)
protection_enabled = BooleanParameter(default=True)
protection_cooldown_lookback = IntParameter([0, 50], default=30)
@property
def protections(self):
prot = []
if self.protection_enabled.value:
prot.append({
"method": "CooldownPeriod",
"stop_duration_candles": self.protection_cooldown_lookback.value
})
return prot
def informative_pairs(self):
"""

18
tests/strategy/test_interface.py
View File

@ -16,8 +16,8 @@ from freqtrade.exceptions import OperationalException, StrategyError
from freqtrade.optimize.space import SKDecimal
from freqtrade.persistence import PairLocks, Trade
from freqtrade.resolvers import StrategyResolver
from freqtrade.strategy.hyper import (BaseParameter, CategoricalParameter, DecimalParameter,
IntParameter, RealParameter)
from freqtrade.strategy.hyper import (BaseParameter, BooleanParameter, CategoricalParameter,
DecimalParameter, IntParameter, RealParameter)
from freqtrade.strategy.interface import SellCheckTuple
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from tests.conftest import log_has, log_has_re
@ -717,6 +717,17 @@ def test_hyperopt_parameters():
assert len(list(catpar.range)) == 3
assert list(catpar.range) == ['buy_rsi', 'buy_macd', 'buy_none']
boolpar = BooleanParameter(default=True, space='buy')
assert boolpar.value is True
assert isinstance(boolpar.get_space(''), Categorical)
assert isinstance(boolpar.range, list)
assert len(list(boolpar.range)) == 1
boolpar.in_space = True
assert len(list(boolpar.range)) == 2
assert list(boolpar.range) == [True, False]
def test_auto_hyperopt_interface(default_conf):
default_conf.update({'strategy': 'HyperoptableStrategy'})
@ -734,7 +745,8 @@ def test_auto_hyperopt_interface(default_conf):
assert isinstance(all_params, dict)
assert len(all_params['buy']) == 2
assert len(all_params['sell']) == 2
assert all_params['count'] == 4
# Number of Hyperoptable parameters
assert all_params['count'] == 6
strategy.__class__.sell_rsi = IntParameter([0, 10], default=5, space='buy')

13
tests/test_configuration.py
View File

@ -1330,7 +1330,7 @@ def test_process_removed_setting(mocker, default_conf, caplog):
'sectionB', 'somesetting')
def test_process_deprecated_ticker_interval(mocker, default_conf, caplog):
def test_process_deprecated_ticker_interval(default_conf, caplog):
message = "DEPRECATED: Please use 'timeframe' instead of 'ticker_interval."
config = deepcopy(default_conf)
process_temporary_deprecated_settings(config)
@ -1352,6 +1352,17 @@ def test_process_deprecated_ticker_interval(mocker, default_conf, caplog):
process_temporary_deprecated_settings(config)
def test_process_deprecated_protections(default_conf, caplog):
message = "DEPRECATED: Setting 'protections' in the configuration is deprecated."
config = deepcopy(default_conf)
process_temporary_deprecated_settings(config)
assert not log_has(message, caplog)
config['protections'] = []
process_temporary_deprecated_settings(config)
assert log_has(message, caplog)
def test_flat_vars_to_nested_dict(caplog):
test_args = {