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Merge pull request #2492 from hroff-1902/hyperopt-trailing-space

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Add trailing stoploss hyperspace
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61
docs/hyperopt.md
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@ -46,8 +46,9 @@ Optional - can also be loaded from a strategy:
Rarely you may also need to override:
* `roi_space` - for custom ROI optimization (if you need the ranges for the ROI parameters in the optimization hyperspace that differ from default)
* `generate_roi_table` - for custom ROI optimization (if you need more than 4 entries in the ROI table)
* `generate_roi_table` - for custom ROI optimization (if you need the ranges for the values in the ROI table that differ from default or the number of entries (steps) in the ROI table which differs from the default 4 steps)
* `stoploss_space` - for custom stoploss optimization (if you need the range for the stoploss parameter in the optimization hyperspace that differs from default)
* `trailing_space` - for custom trailing stop optimization (if you need the ranges for the trailing stop parameters in the optimization hyperspace that differ from default)
### 1. Install a Custom Hyperopt File
@ -253,10 +254,10 @@ freqtrade hyperopt --config config.json --hyperopt <hyperoptname> -e 5000 --spac
Use `<hyperoptname>` as the name of the custom hyperopt used.
The `-e` flag will set how many evaluations hyperopt will do. We recommend
The `-e` option will set how many evaluations hyperopt will do. We recommend
running at least several thousand evaluations.
The `--spaces all` flag determines that all possible parameters should be optimized. Possibilities are listed below.
The `--spaces all` option determines that all possible parameters should be optimized. Possibilities are listed below.
!!! Note
By default, hyperopt will erase previous results and start from scratch. Continuation can be archived by using `--continue`.
@ -289,7 +290,7 @@ freqtrade hyperopt --strategy SampleStrategy --customhyperopt SampleHyperopt
### Running Hyperopt with Smaller Search Space
Use the `--spaces` argument to limit the search space used by hyperopt.
Use the `--spaces` option to limit the search space used by hyperopt.
Letting Hyperopt optimize everything is a huuuuge search space. Often it
might make more sense to start by just searching for initial buy algorithm.
Or maybe you just want to optimize your stoploss or roi table for that awesome
@ -302,8 +303,12 @@ Legal values are:
* `sell`: just search for a new sell strategy
* `roi`: just optimize the minimal profit table for your strategy
* `stoploss`: search for the best stoploss value
* `trailing`: search for the best trailing stop values
* `default`: `all` except `trailing`
* space-separated list of any of the above values for example `--spaces roi stoploss`
The default Hyperopt Search Space, used when no `--space` command line option is specified, does not include the `trailing` hyperspace. We recommend you to run optimization for the `trailing` hyperspace separately, when the best parameters for other hyperspaces were found, validated and pasted into your custom strategy.
### Position stacking and disabling max market positions
In some situations, you may need to run Hyperopt (and Backtesting) with the
@ -377,19 +382,13 @@ You can use the `--print-all` command line option if you would like to see all r
### Understand Hyperopt ROI results
If you are optimizing ROI (i.e. if optimization search-space contains 'all' or 'roi'), your result will look as follows and include a ROI table:
If you are optimizing ROI (i.e. if optimization search-space contains 'all', 'default' or 'roi'), your result will look as follows and include a ROI table:
```
Best result:
44/100: 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. Objective: 1.94367
Buy hyperspace params:
{ 'adx-value': 44,
'rsi-value': 29,
'adx-enabled': False,
'rsi-enabled': True,
'trigger': 'bb_lower'}
ROI table:
{ 0: 0.10674,
21: 0.09158,
@ -413,7 +412,7 @@ As stated in the comment, you can also use it as the value of the `minimal_roi`
#### Default ROI Search Space
If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the ticker_interval used. By default the values can vary in the following ranges (for some of the most used ticker intervals, values are rounded to 5 digits after the decimal point):
If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the ticker_interval used. By default the values vary in the following ranges (for some of the most used ticker intervals, values are rounded to 5 digits after the decimal point):
| # step | 1m | | 5m | | 1h | | 1d | |
|---|---|---|---|---|---|---|---|---|
@ -430,7 +429,7 @@ Override the `roi_space()` method if you need components of the ROI tables to va
### Understand Hyperopt Stoploss results
If you are optimizing stoploss values (i.e. if optimization search-space contains 'all' or 'stoploss'), your result will look as follows and include stoploss:
If you are optimizing stoploss values (i.e. if optimization search-space contains 'all', 'default' or 'stoploss'), your result will look as follows and include stoploss:
```
Best result:
@ -457,12 +456,46 @@ As stated in the comment, you can also use it as the value of the `stoploss` set
#### Default Stoploss Search Space
If you are optimizing stoploss values, Freqtrade creates the 'stoploss' optimization hyperspace for you. By default, the stoploss values in that hyperspace can vary in the range -0.35...-0.02, which is sufficient in most cases.
If you are optimizing stoploss values, Freqtrade creates the 'stoploss' optimization hyperspace for you. By default, the stoploss values in that hyperspace vary in the range -0.35...-0.02, which is sufficient in most cases.
If you have the `stoploss_space()` method in your custom hyperopt file, remove it in order to utilize Stoploss hyperoptimization space generated by Freqtrade by default.
Override the `stoploss_space()` method and define the desired range in it if you need stoploss values to vary in other range during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
### Understand Hyperopt Trailing Stop results
If you are optimizing trailing stop values (i.e. if optimization search-space contains 'all' or 'trailing'), your result will look as follows and include trailing stop parameters:
```
Best result:
45/100: 606 trades. Avg profit 1.04%. Total profit 0.31555614 BTC ( 630.48Σ%). Avg duration 150.3 mins. Objective: -1.10161
Trailing stop:
{ 'trailing_only_offset_is_reached': True,
'trailing_stop': True,
'trailing_stop_positive': 0.02001,
'trailing_stop_positive_offset': 0.06038}
```
In order to use these best trailing stop parameters found by Hyperopt in backtesting and for live trades/dry-run, copy-paste them as the values of the corresponding attributes of your custom strategy:
```
# Trailing stop
# These attributes will be overridden if the config file contains corresponding values.
trailing_stop = True
trailing_stop_positive = 0.02001
trailing_stop_positive_offset = 0.06038
trailing_only_offset_is_reached = True
```
As stated in the comment, you can also use it as the values of the corresponding settings in the configuration file.
#### Default Trailing Stop Search Space
If you are optimizing trailing stop values, Freqtrade creates the 'trailing' optimization hyperspace for you. By default, the `trailing_stop` parameter is always set to True in that hyperspace, the value of the `trailing_only_offset_is_reached` vary between True and False, the values of the `trailing_stop_positive` and `trailing_stop_positive_offset` parameters vary in the ranges 0.02...0.35 and 0.01...0.1 correspondingly, which is sufficient in most cases.
Override the `trailing_space()` method and define the desired range in it if you need values of the trailing stop parameters to vary in other ranges during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py).
### Validate backtesting results
Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected.

7
freqtrade/configuration/cli_options.py
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@ -184,11 +184,10 @@ AVAILABLE_CLI_OPTIONS = {
),
"spaces": Arg(
'--spaces',
help='Specify which parameters to hyperopt. Space-separated list. '
'Default: `%(default)s`.',
choices=['all', 'buy', 'sell', 'roi', 'stoploss'],
help='Specify which parameters to hyperopt. Space-separated list.',
choices=['all', 'buy', 'sell', 'roi', 'stoploss', 'trailing', 'default'],
nargs='+',
default='all',
default='default',
),
"print_all": Arg(
'--print-all',

89
freqtrade/optimize/hyperopt.py
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@ -170,45 +170,43 @@ class Hyperopt:
best_result = results[0]
params = best_result['params']
log_str = self.format_results_logstring(best_result)
print(f"\nBest result:\n\n{log_str}\n")
if self.config.get('print_json'):
result_dict: Dict = {}
if self.has_space('buy') or self.has_space('sell'):
result_dict['params'] = {}
if self.has_space('buy'):
result_dict['params'].update({p.name: params.get(p.name)
for p in self.hyperopt_space('buy')})
if self.has_space('sell'):
result_dict['params'].update({p.name: params.get(p.name)
for p in self.hyperopt_space('sell')})
if self.has_space('roi'):
for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
self._params_update_for_json(result_dict, params, s)
print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
else:
self._params_pretty_print(params, 'buy', "Buy hyperspace params:")
self._params_pretty_print(params, 'sell', "Sell hyperspace params:")
self._params_pretty_print(params, 'roi', "ROI table:")
self._params_pretty_print(params, 'stoploss', "Stoploss:")
self._params_pretty_print(params, 'trailing', "Trailing stop:")
def _params_update_for_json(self, result_dict, params, space: str):
if self.has_space(space):
space_params = self.space_params(params, space)
if space in ['buy', 'sell']:
result_dict.setdefault('params', {}).update(space_params)
elif space == 'roi':
# Convert keys in min_roi dict to strings because
# rapidjson cannot dump dicts with integer keys...
# OrderedDict is used to keep the numeric order of the items
# in the dict.
result_dict['minimal_roi'] = OrderedDict(
(str(k), v) for k, v in self.custom_hyperopt.generate_roi_table(params).items()
(str(k), v) for k, v in space_params.items()
)
if self.has_space('stoploss'):
result_dict['stoploss'] = params.get('stoploss')
print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
else:
if self.has_space('buy'):
print('Buy hyperspace params:')
pprint({p.name: params.get(p.name) for p in self.hyperopt_space('buy')},
indent=4)
if self.has_space('sell'):
print('Sell hyperspace params:')
pprint({p.name: params.get(p.name) for p in self.hyperopt_space('sell')},
indent=4)
if self.has_space('roi'):
print("ROI table:")
# Round printed values to 5 digits after the decimal point
pprint(round_dict(self.custom_hyperopt.generate_roi_table(params), 5), indent=4)
if self.has_space('stoploss'):
# Also round to 5 digits after the decimal point
print(f"Stoploss: {round(params.get('stoploss'), 5)}")
else: # 'stoploss', 'trailing'
result_dict.update(space_params)
def _params_pretty_print(self, params, space: str, header: str):
if self.has_space(space):
space_params = self.space_params(params, space, 5)
print(header)
pprint(space_params, indent=4)
def is_best(self, results) -> bool:
return results['loss'] < self.current_best_loss
@ -250,9 +248,13 @@ class Hyperopt:
def has_space(self, space: str) -> bool:
"""
Tell if a space value is contained in the configuration
Tell if the space value is contained in the configuration
"""
return any(s in self.config['spaces'] for s in [space, 'all'])
# The 'trailing' space is not included in the 'default' set of spaces
if space == 'trailing':
return any(s in self.config['spaces'] for s in [space, 'all'])
else:
return any(s in self.config['spaces'] for s in [space, 'all', 'default'])
def hyperopt_space(self, space: Optional[str] = None) -> List[Dimension]:
"""
@ -262,20 +264,37 @@ class Hyperopt:
for all hyperspaces used.
"""
spaces: List[Dimension] = []
if space == 'buy' or (space is None and self.has_space('buy')):
logger.debug("Hyperopt has 'buy' space")
spaces += self.custom_hyperopt.indicator_space()
if space == 'sell' or (space is None and self.has_space('sell')):
logger.debug("Hyperopt has 'sell' space")
spaces += self.custom_hyperopt.sell_indicator_space()
if space == 'roi' or (space is None and self.has_space('roi')):
logger.debug("Hyperopt has 'roi' space")
spaces += self.custom_hyperopt.roi_space()
if space == 'stoploss' or (space is None and self.has_space('stoploss')):
logger.debug("Hyperopt has 'stoploss' space")
spaces += self.custom_hyperopt.stoploss_space()
if space == 'trailing' or (space is None and self.has_space('trailing')):
logger.debug("Hyperopt has 'trailing' space")
spaces += self.custom_hyperopt.trailing_space()
return spaces
def space_params(self, params, space: str, r: int = None) -> Dict:
if space == 'roi':
d = self.custom_hyperopt.generate_roi_table(params)
else:
d = {p.name: params.get(p.name) for p in self.hyperopt_space(space)}
# Round floats to `r` digits after the decimal point if requested
return round_dict(d, r) if r else d
def generate_optimizer(self, _params: Dict, iteration=None) -> Dict:
"""
Used Optimize function. Called once per epoch to optimize whatever is configured.
@ -298,6 +317,14 @@ class Hyperopt:
if self.has_space('stoploss'):
self.backtesting.strategy.stoploss = params['stoploss']
if self.has_space('trailing'):
self.backtesting.strategy.trailing_stop = params['trailing_stop']
self.backtesting.strategy.trailing_stop_positive = params['trailing_stop_positive']
self.backtesting.strategy.trailing_stop_positive_offset = \
params['trailing_stop_positive_offset']
self.backtesting.strategy.trailing_only_offset_is_reached = \
params['trailing_only_offset_is_reached']
processed = load(self.tickerdata_pickle)
min_date, max_date = get_timeframe(processed)

23
freqtrade/optimize/hyperopt_interface.py
View File

@ -8,7 +8,7 @@ import math
from abc import ABC
from typing import Dict, Any, Callable, List
from skopt.space import Dimension, Integer, Real
from skopt.space import Categorical, Dimension, Integer, Real
from freqtrade import OperationalException
from freqtrade.exchange import timeframe_to_minutes
@ -174,6 +174,27 @@ class IHyperOpt(ABC):
Real(-0.35, -0.02, name='stoploss'),
]
@staticmethod
def trailing_space() -> List[Dimension]:
"""
Create a trailing stoploss space.
You may override it in your custom Hyperopt class.
"""
return [
# It was decided to always set trailing_stop is to True if the 'trailing' hyperspace
# is used. Otherwise hyperopt will vary other parameters that won't have effect if
# trailing_stop is set False.
# This parameter is included into the hyperspace dimensions rather than assigning
# it explicitly in the code in order to have it printed in the results along with
# other 'trailing' hyperspace parameters.
Categorical([True], name='trailing_stop'),
Real(0.02, 0.35, name='trailing_stop_positive'),
Real(0.01, 0.1, name='trailing_stop_positive_offset'),
Categorical([True, False], name='trailing_only_offset_is_reached'),
]
# This is needed for proper unpickling the class attribute ticker_interval
# which is set to the actual value by the resolver.
# Why do I still need such shamanic mantras in modern python?

21
freqtrade/templates/sample_hyperopt_advanced.py
View File

@ -233,6 +233,27 @@ class AdvancedSampleHyperOpt(IHyperOpt):
Real(-0.5, -0.02, name='stoploss'),
]
@staticmethod
def trailing_space() -> List[Dimension]:
"""
Create a trailing stoploss space.
You may override it in your custom Hyperopt class.
"""
return [
# It was decided to always set trailing_stop is to True if the 'trailing' hyperspace
# is used. Otherwise hyperopt will vary other parameters that won't have effect if
# trailing_stop is set False.
# This parameter is included into the hyperspace dimensions rather than assigning
# it explicitly in the code in order to have it printed in the results along with
# other 'trailing' hyperspace parameters.
Categorical([True], name='trailing_stop'),
Real(0.02, 0.35, name='trailing_stop_positive'),
Real(0.01, 0.1, name='trailing_stop_positive_offset'),
Categorical([True, False], name='trailing_only_offset_is_reached'),
]
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators.

93
tests/optimize/test_hyperopt.py
View File

@ -27,7 +27,7 @@ from tests.conftest import (get_args, log_has, log_has_re, patch_exchange,
@pytest.fixture(scope='function')
def hyperopt(default_conf, mocker):
default_conf.update({
'spaces': ['all'],
'spaces': ['default'],
'hyperopt': 'DefaultHyperOpt',
})
patch_exchange(mocker)
@ -113,7 +113,7 @@ def test_setup_hyperopt_configuration_with_arguments(mocker, default_conf, caplo
'--enable-position-stacking',
'--disable-max-market-positions',
'--epochs', '1000',
'--spaces', 'all',
'--spaces', 'default',
'--print-all'
]
@ -440,7 +440,7 @@ def test_start_calls_optimizer(mocker, default_conf, caplog, capsys) -> None:
'hyperopt': 'DefaultHyperOpt',
'epochs': 1,
'timerange': None,
'spaces': 'all',
'spaces': 'default',
'hyperopt_jobs': 1, })
hyperopt = Hyperopt(default_conf)
@ -489,14 +489,38 @@ def test_format_results(hyperopt):
assert result.find('Total profit 1.00000000 EUR')
def test_has_space(hyperopt):
hyperopt.config.update({'spaces': ['buy', 'roi']})
assert hyperopt.has_space('roi')
assert hyperopt.has_space('buy')
assert not hyperopt.has_space('stoploss')
hyperopt.config.update({'spaces': ['all']})
assert hyperopt.has_space('buy')
@pytest.mark.parametrize("spaces, expected_results", [
(['buy'],
{'buy': True, 'sell': False, 'roi': False, 'stoploss': False, 'trailing': False}),
(['sell'],
{'buy': False, 'sell': True, 'roi': False, 'stoploss': False, 'trailing': False}),
(['roi'],
{'buy': False, 'sell': False, 'roi': True, 'stoploss': False, 'trailing': False}),
(['stoploss'],
{'buy': False, 'sell': False, 'roi': False, 'stoploss': True, 'trailing': False}),
(['trailing'],
{'buy': False, 'sell': False, 'roi': False, 'stoploss': False, 'trailing': True}),
(['buy', 'sell', 'roi', 'stoploss'],
{'buy': True, 'sell': True, 'roi': True, 'stoploss': True, 'trailing': False}),
(['buy', 'sell', 'roi', 'stoploss', 'trailing'],
{'buy': True, 'sell': True, 'roi': True, 'stoploss': True, 'trailing': True}),
(['buy', 'roi'],
{'buy': True, 'sell': False, 'roi': True, 'stoploss': False, 'trailing': False}),
(['all'],
{'buy': True, 'sell': True, 'roi': True, 'stoploss': True, 'trailing': True}),
(['default'],
{'buy': True, 'sell': True, 'roi': True, 'stoploss': True, 'trailing': False}),
(['default', 'trailing'],
{'buy': True, 'sell': True, 'roi': True, 'stoploss': True, 'trailing': True}),
(['all', 'buy'],
{'buy': True, 'sell': True, 'roi': True, 'stoploss': True, 'trailing': True}),
(['default', 'buy'],
{'buy': True, 'sell': True, 'roi': True, 'stoploss': True, 'trailing': False}),
])
def test_has_space(hyperopt, spaces, expected_results):
for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
hyperopt.config.update({'spaces': spaces})
assert hyperopt.has_space(s) == expected_results[s]
def test_populate_indicators(hyperopt, testdatadir) -> None:
@ -591,6 +615,10 @@ def test_generate_optimizer(mocker, default_conf) -> None:
'roi_p2': 0.01,
'roi_p3': 0.1,
'stoploss': -0.4,
'trailing_stop': True,
'trailing_stop_positive': 0.02,
'trailing_stop_positive_offset': 0.1,
'trailing_only_offset_is_reached': False,
}
response_expected = {
'loss': 1.9840569076926293,
@ -613,7 +641,7 @@ def test_clean_hyperopt(mocker, default_conf, caplog):
'hyperopt': 'DefaultHyperOpt',
'epochs': 1,
'timerange': None,
'spaces': 'all',
'spaces': 'default',
'hyperopt_jobs': 1,
})
mocker.patch("freqtrade.optimize.hyperopt.Path.is_file", MagicMock(return_value=True))
@ -630,7 +658,7 @@ def test_continue_hyperopt(mocker, default_conf, caplog):
'hyperopt': 'DefaultHyperOpt',
'epochs': 1,
'timerange': None,
'spaces': 'all',
'spaces': 'default',
'hyperopt_jobs': 1,
'hyperopt_continue': True
})
@ -674,6 +702,45 @@ def test_print_json_spaces_all(mocker, default_conf, caplog, capsys) -> None:
parallel.assert_called_once()
out, err = capsys.readouterr()
assert '{"params":{"mfi-value":null,"fastd-value":null,"adx-value":null,"rsi-value":null,"mfi-enabled":null,"fastd-enabled":null,"adx-enabled":null,"rsi-enabled":null,"trigger":null,"sell-mfi-value":null,"sell-fastd-value":null,"sell-adx-value":null,"sell-rsi-value":null,"sell-mfi-enabled":null,"sell-fastd-enabled":null,"sell-adx-enabled":null,"sell-rsi-enabled":null,"sell-trigger":null},"minimal_roi":{},"stoploss":null,"trailing_stop":null,"trailing_stop_positive":null,"trailing_stop_positive_offset":null,"trailing_only_offset_is_reached":null}' in out # noqa: E501
assert dumper.called
# Should be called twice, once for tickerdata, once to save evaluations
assert dumper.call_count == 2
def test_print_json_spaces_default(mocker, default_conf, caplog, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None)))
mocker.patch(
'freqtrade.optimize.hyperopt.get_timeframe',
MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
)
parallel = mocker.patch(
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result', 'params': {}}])
)
patch_exchange(mocker)
default_conf.update({'config': 'config.json.example',
'hyperopt': 'DefaultHyperOpt',
'epochs': 1,
'timerange': None,
'spaces': 'default',
'hyperopt_jobs': 1,
'print_json': True,
})
hyperopt = Hyperopt(default_conf)
hyperopt.backtesting.strategy.tickerdata_to_dataframe = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
hyperopt.start()
parallel.assert_called_once()
out, err = capsys.readouterr()
assert '{"params":{"mfi-value":null,"fastd-value":null,"adx-value":null,"rsi-value":null,"mfi-enabled":null,"fastd-enabled":null,"adx-enabled":null,"rsi-enabled":null,"trigger":null,"sell-mfi-value":null,"sell-fastd-value":null,"sell-adx-value":null,"sell-rsi-value":null,"sell-mfi-enabled":null,"sell-fastd-enabled":null,"sell-adx-enabled":null,"sell-rsi-enabled":null,"sell-trigger":null},"minimal_roi":{},"stoploss":null}' in out # noqa: E501
assert dumper.called