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Keep dimensions stored in hyperopt class

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There is no point in regenerating them and it will cause some
overhead as all space classes will be recreated for every epoch.
This commit is contained in:
Matthias 2021-05-02 09:35:10 +02:00
parent ced5cc7ce2
commit 46f0f66039
2 changed files with 33 additions and 33 deletions
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63
freqtrade/optimize/hyperopt.py
View File

@ -65,6 +65,13 @@ class Hyperopt:
custom_hyperopt: IHyperOpt
def __init__(self, config: Dict[str, Any]) -> None:
self.buy_space: List[Dimension] = []
self.sell_space: List[Dimension] = []
self.roi_space: List[Dimension] = []
self.stoploss_space: List[Dimension] = []
self.trailing_space: List[Dimension] = []
self.dimensions: List[Dimension] = []
self.config = config
self.backtesting = Backtesting(self.config)
@ -139,9 +146,7 @@ class Hyperopt:
logger.info(f"Removing `{p}`.")
p.unlink()
def _get_params_dict(self, raw_params: List[Any]) -> Dict:
dimensions: List[Dimension] = self.dimensions
def _get_params_dict(self, dimensions: List[Dimension], raw_params: List[Any]) -> Dict:
# Ensure the number of dimensions match
# the number of parameters in the list.
@ -175,16 +180,13 @@ class Hyperopt:
result: Dict = {}
if HyperoptTools.has_space(self.config, 'buy'):
result['buy'] = {p.name: params.get(p.name)
for p in self.hyperopt_space('buy')}
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.hyperopt_space('sell')}
result['sell'] = {p.name: params.get(p.name) for p in self.sell_space}
if HyperoptTools.has_space(self.config, 'roi'):
result['roi'] = self.custom_hyperopt.generate_roi_table(params)
if HyperoptTools.has_space(self.config, 'stoploss'):
result['stoploss'] = {p.name: params.get(p.name)
for p in self.hyperopt_space('stoploss')}
result['stoploss'] = {p.name: params.get(p.name) for p in self.stoploss_space}
if HyperoptTools.has_space(self.config, 'trailing'):
result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
@ -207,38 +209,32 @@ class Hyperopt:
)
self.hyperopt_table_header = 2
def hyperopt_space(self, space: Optional[str] = None) -> List[Dimension]:
def init_spaces(self):
"""
Return the dimensions in the hyperoptimization space.
:param space: Defines hyperspace to return dimensions for.
If None, then the self.has_space() will be used to return dimensions
for all hyperspaces used.
Assign the dimensions in the hyperoptimization space.
"""
spaces: List[Dimension] = []
if space == 'buy' or (space is None and HyperoptTools.has_space(self.config, 'buy')):
if HyperoptTools.has_space(self.config, 'buy'):
logger.debug("Hyperopt has 'buy' space")
spaces += self.custom_hyperopt.indicator_space()
self.buy_space = self.custom_hyperopt.indicator_space()
if space == 'sell' or (space is None and HyperoptTools.has_space(self.config, 'sell')):
if HyperoptTools.has_space(self.config, 'sell'):
logger.debug("Hyperopt has 'sell' space")
spaces += self.custom_hyperopt.sell_indicator_space()
self.sell_space = self.custom_hyperopt.sell_indicator_space()
if space == 'roi' or (space is None and HyperoptTools.has_space(self.config, 'roi')):
if HyperoptTools.has_space(self.config, 'roi'):
logger.debug("Hyperopt has 'roi' space")
spaces += self.custom_hyperopt.roi_space()
self.roi_space = self.custom_hyperopt.roi_space()
if space == 'stoploss' or (space is None
and HyperoptTools.has_space(self.config, 'stoploss')):
if HyperoptTools.has_space(self.config, 'stoploss'):
logger.debug("Hyperopt has 'stoploss' space")
spaces += self.custom_hyperopt.stoploss_space()
self.stoploss_space = self.custom_hyperopt.stoploss_space()
if space == 'trailing' or (space is None
and HyperoptTools.has_space(self.config, 'trailing')):
if HyperoptTools.has_space(self.config, 'trailing'):
logger.debug("Hyperopt has 'trailing' space")
spaces += self.custom_hyperopt.trailing_space()
return spaces
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)
def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
"""
@ -246,9 +242,10 @@ class Hyperopt:
Keep this function as optimized as possible!
"""
backtest_start_time = datetime.now(timezone.utc)
params_dict = self._get_params_dict(raw_params)
params_dict = self._get_params_dict(self.dimensions, raw_params)
params_details = self._get_params_details(params_dict)
# Apply parameters
if HyperoptTools.has_space(self.config, 'roi'):
self.backtesting.strategy.minimal_roi = ( # type: ignore
self.custom_hyperopt.generate_roi_table(params_dict))
@ -294,7 +291,8 @@ class Hyperopt:
processed=processed)
def _get_results_dict(self, backtesting_results, min_date, max_date,
params_dict, params_details, processed: Dict[str, DataFrame]):
params_dict, params_details, processed: Dict[str, DataFrame]
) -> Dict[str, Any]:
strat_stats = generate_strategy_stats(
processed, self.backtesting.strategy.get_strategy_name(),
@ -347,6 +345,8 @@ class Hyperopt:
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
logger.info(f"Using optimizer random state: {self.random_state}")
self.hyperopt_table_header = -1
# Initialize spaces ...
self.init_spaces()
data, timerange = self.backtesting.load_bt_data()
logger.info("Dataload complete. Calculating indicators")
preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
@ -377,7 +377,6 @@ class Hyperopt:
config_jobs = self.config.get('hyperopt_jobs', -1)
logger.info(f'Number of parallel jobs set as: {config_jobs}')
self.dimensions: List[Dimension] = self.hyperopt_space()
self.opt = self.get_optimizer(self.dimensions, config_jobs)
if self.print_colorized:

3
tests/optimize/test_hyperopt.py
View File

@ -695,7 +695,8 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.min_date = Arrow(2017, 12, 10)
hyperopt.max_date = Arrow(2017, 12, 13)
hyperopt.dimensions = hyperopt.hyperopt_space()
hyperopt.init_spaces()
hyperopt.dimensions = hyperopt.dimensions
generate_optimizer_value = hyperopt.generate_optimizer(list(optimizer_param.values()))
assert generate_optimizer_value == response_expected