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Small type and typo fixes in freqai_interface

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This commit is contained in:
Matthias 2022-07-28 07:24:30 +02:00
parent a2a0d35a24
commit efbd83c56d
1 changed files with 11 additions and 9 deletions
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20
freqtrade/freqai/freqai_interface.py
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@ -61,19 +61,21 @@ class IFreqaiModel(ABC):
self.config = config self.config = config
self.assert_config(self.config) self.assert_config(self.config)
self.freqai_info = config["freqai"] self.freqai_info: Dict[str, Any] = config["freqai"]
self.data_split_parameters = config.get("freqai", {}).get("data_split_parameters") self.data_split_parameters: Dict[str, Any] = config.get("freqai", {}).get(
self.model_training_parameters = config.get("freqai", {}).get("model_training_parameters") "data_split_parameters", {})
self.model_training_parameters: Dict[str, Any] = config.get("freqai", {}).get(
"model_training_parameters", {})
self.feature_parameters = config.get("freqai", {}).get("feature_parameters") self.feature_parameters = config.get("freqai", {}).get("feature_parameters")
self.retrain = False self.retrain = False
self.first = True self.first = True
self.set_full_path() self.set_full_path()
self.follow_mode = self.freqai_info.get("follow_mode", False) self.follow_mode: bool = self.freqai_info.get("follow_mode", False)
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode) self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
self.lock = threading.Lock() self.lock = threading.Lock()
self.identifier = self.freqai_info.get("identifier", "no_id_provided") self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
self.scanning = False self.scanning = False
self.keras = self.freqai_info.get("keras", False) self.keras: bool = self.freqai_info.get("keras", False)
if self.keras and self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0): if self.keras and self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
self.freqai_info["feature_parameters"]["DI_threshold"] = 0 self.freqai_info["feature_parameters"]["DI_threshold"] = 0
logger.warning("DI threshold is not configured for Keras models yet. Deactivating.") logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
@ -253,7 +255,7 @@ class IFreqaiModel(ABC):
# get the model metadata associated with the current pair # get the model metadata associated with the current pair
(_, trained_timestamp, return_null_array) = self.dd.get_pair_dict_info(metadata["pair"]) (_, trained_timestamp, return_null_array) = self.dd.get_pair_dict_info(metadata["pair"])
# if the metadata doesnt exist, the follower returns null arrays to strategy # if the metadata doesn't exist, the follower returns null arrays to strategy
if self.follow_mode and return_null_array: if self.follow_mode and return_null_array:
logger.info("Returning null array from follower to strategy") logger.info("Returning null array from follower to strategy")
self.dd.return_null_values_to_strategy(dataframe, dk) self.dd.return_null_values_to_strategy(dataframe, dk)
@ -364,7 +366,7 @@ class IFreqaiModel(ABC):
raise OperationalException( raise OperationalException(
"Trying to access pretrained model with `identifier` " "Trying to access pretrained model with `identifier` "
"but found different features furnished by current strategy." "but found different features furnished by current strategy."
"Change `identifer` to train from scratch, or ensure the" "Change `identifier` to train from scratch, or ensure the"
"strategy is furnishing the same features as the pretrained" "strategy is furnishing the same features as the pretrained"
"model" "model"
) )
@ -457,7 +459,7 @@ class IFreqaiModel(ABC):
data_load_timerange: TimeRange, data_load_timerange: TimeRange,
): ):
""" """
Retreive data and train model in single threaded mode (only used if model directory is empty Retrieve data and train model in single threaded mode (only used if model directory is empty
upon startup for dry/live ) upon startup for dry/live )
:param new_trained_timerange: TimeRange = the timerange to train the model on :param new_trained_timerange: TimeRange = the timerange to train the model on
:param metadata: dict = strategy provided metadata :param metadata: dict = strategy provided metadata