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Minor stylistic fixes

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This commit is contained in:
Matthias 2022-08-06 14:55:46 +02:00
parent c172ce1011
commit b16f57cb0d
2 changed files with 27 additions and 36 deletions
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53
freqtrade/freqai/data_kitchen.py
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@ -61,7 +61,7 @@ class FreqaiDataKitchen:
self.data: Dict[Any, Any] = {} self.data: Dict[Any, Any] = {}
self.data_dictionary: Dict[Any, Any] = {} self.data_dictionary: Dict[Any, Any] = {}
self.config = config self.config = config
self.freqai_config = config["freqai"] self.freqai_config: Dict[str, Any] = config["freqai"]
self.full_df: DataFrame = DataFrame() self.full_df: DataFrame = DataFrame()
self.append_df: DataFrame = DataFrame() self.append_df: DataFrame = DataFrame()
self.data_path = Path() self.data_path = Path()
@ -71,14 +71,14 @@ class FreqaiDataKitchen:
self.live = live self.live = live
self.pair = pair self.pair = pair
self.svm_model: linear_model.SGDOneClassSVM = None self.svm_model: linear_model.SGDOneClassSVM = None
self.keras = self.freqai_config.get("keras", False) self.keras: bool = self.freqai_config.get("keras", False)
self.set_all_pairs() self.set_all_pairs()
if not self.live: if not self.live:
if not self.config["timerange"]: if not self.config["timerange"]:
raise OperationalException( raise OperationalException(
'Please pass --timerange if you intend to use FreqAI for backtesting.') 'Please pass --timerange if you intend to use FreqAI for backtesting.')
self.full_timerange = self.create_fulltimerange( self.full_timerange = self.create_fulltimerange(
self.config["timerange"], self.freqai_config.get("train_period_days") self.config["timerange"], self.freqai_config.get("train_period_days", 0)
) )
(self.training_timeranges, self.backtesting_timeranges) = self.split_timerange( (self.training_timeranges, self.backtesting_timeranges) = self.split_timerange(
@ -203,9 +203,8 @@ class FreqaiDataKitchen:
drop_index = pd.isnull(filtered_dataframe).any(1) # get the rows that have NaNs, drop_index = pd.isnull(filtered_dataframe).any(1) # get the rows that have NaNs,
drop_index = drop_index.replace(True, 1).replace(False, 0) # pep8 requirement. drop_index = drop_index.replace(True, 1).replace(False, 0) # pep8 requirement.
if ( if (training_filter):
training_filter # we don't care about total row number (total no. datapoints) in training, we only care
): # we don't care about total row number (total no. datapoints) in training, we only care
# about removing any row with NaNs # about removing any row with NaNs
# if labels has multiple columns (user wants to train multiple models), we detect here # if labels has multiple columns (user wants to train multiple models), we detect here
labels = unfiltered_dataframe.filter(label_list, axis=1) labels = unfiltered_dataframe.filter(label_list, axis=1)
@ -320,8 +319,7 @@ class FreqaiDataKitchen:
""" """
Normalize a set of data using the mean and standard deviation from Normalize a set of data using the mean and standard deviation from
the associated training data. the associated training data.
:params: :param df: Dataframe to be standardized
:df: Dataframe to be standardized
""" """
for item in df.keys(): for item in df.keys():
@ -338,8 +336,7 @@ class FreqaiDataKitchen:
""" """
Normalize a set of data using the mean and standard deviation from Normalize a set of data using the mean and standard deviation from
the associated training data. the associated training data.
:params: :param df: Dataframe of predictions to be denormalized
:df: Dataframe of predictions to be denormalized
""" """
for label in df.columns: for label in df.columns:
@ -367,7 +364,7 @@ class FreqaiDataKitchen:
if not isinstance(train_split, int) or train_split < 1: if not isinstance(train_split, int) or train_split < 1:
raise OperationalException( raise OperationalException(
"train_period_days must be an integer greater than 0. " f"Got {train_split}." f"train_period_days must be an integer greater than 0. Got {train_split}."
) )
train_period_days = train_split * SECONDS_IN_DAY train_period_days = train_split * SECONDS_IN_DAY
bt_period = bt_split * SECONDS_IN_DAY bt_period = bt_split * SECONDS_IN_DAY
@ -423,9 +420,8 @@ class FreqaiDataKitchen:
def slice_dataframe(self, timerange: TimeRange, df: DataFrame) -> DataFrame: def slice_dataframe(self, timerange: TimeRange, df: DataFrame) -> DataFrame:
""" """
Given a full dataframe, extract the user desired window Given a full dataframe, extract the user desired window
:params: :param tr: timerange string that we wish to extract from df
:tr: timerange string that we wish to extract from df :param df: Dataframe containing all candles to run the entire backtest. Here
:df: Dataframe containing all candles to run the entire backtest. Here
it is sliced down to just the present training period. it is sliced down to just the present training period.
""" """
@ -536,9 +532,7 @@ class FreqaiDataKitchen:
do_predict = np.where(y_pred == -1, 0, y_pred) do_predict = np.where(y_pred == -1, 0, y_pred)
if (len(do_predict) - do_predict.sum()) > 0: if (len(do_predict) - do_predict.sum()) > 0:
logger.info( logger.info(f"SVM tossed {len(do_predict) - do_predict.sum()} predictions.")
f"SVM tossed {len(do_predict) - do_predict.sum()} predictions."
)
self.do_predict += do_predict self.do_predict += do_predict
self.do_predict -= 1 self.do_predict -= 1
@ -568,6 +562,8 @@ class FreqaiDataKitchen:
) )
# same for test data # same for test data
# TODO: This (and the part above) could be refactored into a separate function
# to reduce code duplication
if self.freqai_config['data_split_parameters'].get('test_size', 0.1) != 0: if self.freqai_config['data_split_parameters'].get('test_size', 0.1) != 0:
y_pred = self.svm_model.predict(self.data_dictionary["test_features"]) y_pred = self.svm_model.predict(self.data_dictionary["test_features"])
dropped_points = np.where(y_pred == -1, 0, y_pred) dropped_points = np.where(y_pred == -1, 0, y_pred)
@ -604,17 +600,14 @@ class FreqaiDataKitchen:
pred_ft_df = self.data_dictionary['prediction_features'] pred_ft_df = self.data_dictionary['prediction_features']
num_preds = len(pred_ft_df) num_preds = len(pred_ft_df)
df = pd.concat([train_ft_df, pred_ft_df], axis=0, ignore_index=True) df = pd.concat([train_ft_df, pred_ft_df], axis=0, ignore_index=True)
clustering = DBSCAN( clustering = DBSCAN(eps=self.data['DBSCAN_eps'],
eps=self.data['DBSCAN_eps'],
min_samples=self.data['DBSCAN_min_samples'], min_samples=self.data['DBSCAN_min_samples'],
n_jobs=self.thread_count n_jobs=self.thread_count
).fit(df) ).fit(df)
do_predict = np.where(clustering.labels_[-num_preds:] == -1, 0, 1) do_predict = np.where(clustering.labels_[-num_preds:] == -1, 0, 1)
if (len(do_predict) - do_predict.sum()) > 0: if (len(do_predict) - do_predict.sum()) > 0:
logger.info( logger.info(f"DBSCAN tossed {len(do_predict) - do_predict.sum()} predictions")
f"DBSCAN tossed {len(do_predict) - do_predict.sum()} predictions"
)
self.do_predict += do_predict self.do_predict += do_predict
self.do_predict -= 1 self.do_predict -= 1
@ -662,9 +655,8 @@ class FreqaiDataKitchen:
def find_features(self, dataframe: DataFrame) -> None: def find_features(self, dataframe: DataFrame) -> None:
""" """
Find features in the strategy provided dataframe Find features in the strategy provided dataframe
:params: :param dataframe: DataFrame = strategy provided dataframe
dataframe: DataFrame = strategy provided dataframe :return:
:returns:
features: list = the features to be used for training/prediction features: list = the features to be used for training/prediction
""" """
column_names = dataframe.columns column_names = dataframe.columns
@ -772,7 +764,7 @@ class FreqaiDataKitchen:
if backtest_timerange.stopts == 0: if backtest_timerange.stopts == 0:
# typically open ended time ranges do work, however, there are some edge cases where # typically open ended time ranges do work, however, there are some edge cases where
# it does not. accomodating these kinds of edge cases just to allow open-ended # it does not. accommodating these kinds of edge cases just to allow open-ended
# timerange is not high enough priority to warrant the effort. It is safer for now # timerange is not high enough priority to warrant the effort. It is safer for now
# to simply ask user to add their end date # to simply ask user to add their end date
raise OperationalException("FreqAI backtesting does not allow open ended timeranges. " raise OperationalException("FreqAI backtesting does not allow open ended timeranges. "
@ -808,9 +800,8 @@ class FreqaiDataKitchen:
""" """
A model age checker to determine if the model is trustworthy based on user defined A model age checker to determine if the model is trustworthy based on user defined
`expiration_hours` in the configuration file. `expiration_hours` in the configuration file.
:params: :param trained_timestamp: int = The time of training for the most recent model.
trained_timestamp: int = The time of training for the most recent model. :return:
:returns:
bool = If the model is expired or not. bool = If the model is expired or not.
""" """
time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp() time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
@ -873,7 +864,7 @@ class FreqaiDataKitchen:
data_load_timerange.stopts = int(time) data_load_timerange.stopts = int(time)
else: # user passed no live_trained_timerange in config else: # user passed no live_trained_timerange in config
trained_timerange.startts = int( trained_timerange.startts = int(
time - self.freqai_config.get("train_period_days") * SECONDS_IN_DAY time - self.freqai_config.get("train_period_days", 0) * SECONDS_IN_DAY
) )
trained_timerange.stopts = int(time) trained_timerange.stopts = int(time)
@ -1053,7 +1044,7 @@ class FreqaiDataKitchen:
if isinstance(object, np.generic): if isinstance(object, np.generic):
return object.item() return object.item()
# Functions containing useful data manpulation examples. but not actively in use. # Functions containing useful data manipulation examples. but not actively in use.
# Possibly phasing these outlier removal methods below out in favor of # Possibly phasing these outlier removal methods below out in favor of
# use_SVM_to_remove_outliers (computationally more efficient and apparently higher performance). # use_SVM_to_remove_outliers (computationally more efficient and apparently higher performance).

2
freqtrade/freqai/freqai_interface.py
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@ -572,7 +572,7 @@ class IFreqaiModel(ABC):
return return
# Following methods which are overridden by user made prediction models. # Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModlel.py for an example. # See freqai/prediction_models/CatboostPredictionModel.py for an example.
@abstractmethod @abstractmethod
def train(self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen) -> Any: def train(self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen) -> Any: