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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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61
freqtrade/freqai/data_kitchen.py
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@ -61,7 +61,7 @@ class FreqaiDataKitchen:
self.data: Dict[Any, Any] = {}
self.data_dictionary: Dict[Any, Any] = {}
self.config = config
self.freqai_config = config["freqai"]
self.freqai_config: Dict[str, Any] = config["freqai"]
self.full_df: DataFrame = DataFrame()
self.append_df: DataFrame = DataFrame()
self.data_path = Path()
@ -71,14 +71,14 @@ class FreqaiDataKitchen:
self.live = live
self.pair = pair
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()
if not self.live:
if not self.config["timerange"]:
raise OperationalException(
'Please pass --timerange if you intend to use FreqAI for backtesting.')
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(
@ -203,9 +203,8 @@ class FreqaiDataKitchen:
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.
if (
training_filter
): # we don't care about total row number (total no. datapoints) in training, we only care
if (training_filter):
# we don't care about total row number (total no. datapoints) in training, we only care
# about removing any row with NaNs
# if labels has multiple columns (user wants to train multiple models), we detect here
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
the associated training data.
:params:
:df: Dataframe to be standardized
:param df: Dataframe to be standardized
"""
for item in df.keys():
@ -338,8 +336,7 @@ class FreqaiDataKitchen:
"""
Normalize a set of data using the mean and standard deviation from
the associated training data.
:params:
:df: Dataframe of predictions to be denormalized
:param df: Dataframe of predictions to be denormalized
"""
for label in df.columns:
@ -367,7 +364,7 @@ class FreqaiDataKitchen:
if not isinstance(train_split, int) or train_split < 1:
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
bt_period = bt_split * SECONDS_IN_DAY
@ -423,10 +420,9 @@ class FreqaiDataKitchen:
def slice_dataframe(self, timerange: TimeRange, df: DataFrame) -> DataFrame:
"""
Given a full dataframe, extract the user desired window
:params:
:tr: timerange string that we wish to extract from df
:df: Dataframe containing all candles to run the entire backtest. Here
it is sliced down to just the present training period.
:param tr: timerange string that we wish to extract from df
:param df: Dataframe containing all candles to run the entire backtest. Here
it is sliced down to just the present training period.
"""
start = datetime.datetime.fromtimestamp(timerange.startts, tz=datetime.timezone.utc)
@ -536,9 +532,7 @@ class FreqaiDataKitchen:
do_predict = np.where(y_pred == -1, 0, y_pred)
if (len(do_predict) - do_predict.sum()) > 0:
logger.info(
f"SVM tossed {len(do_predict) - do_predict.sum()} predictions."
)
logger.info(f"SVM tossed {len(do_predict) - do_predict.sum()} predictions.")
self.do_predict += do_predict
self.do_predict -= 1
@ -568,6 +562,8 @@ class FreqaiDataKitchen:
)
# 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:
y_pred = self.svm_model.predict(self.data_dictionary["test_features"])
dropped_points = np.where(y_pred == -1, 0, y_pred)
@ -604,17 +600,14 @@ class FreqaiDataKitchen:
pred_ft_df = self.data_dictionary['prediction_features']
num_preds = len(pred_ft_df)
df = pd.concat([train_ft_df, pred_ft_df], axis=0, ignore_index=True)
clustering = DBSCAN(
eps=self.data['DBSCAN_eps'],
min_samples=self.data['DBSCAN_min_samples'],
n_jobs=self.thread_count
clustering = DBSCAN(eps=self.data['DBSCAN_eps'],
min_samples=self.data['DBSCAN_min_samples'],
n_jobs=self.thread_count
).fit(df)
do_predict = np.where(clustering.labels_[-num_preds:] == -1, 0, 1)
if (len(do_predict) - do_predict.sum()) > 0:
logger.info(
f"DBSCAN tossed {len(do_predict) - do_predict.sum()} predictions"
)
logger.info(f"DBSCAN tossed {len(do_predict) - do_predict.sum()} predictions")
self.do_predict += do_predict
self.do_predict -= 1
@ -662,9 +655,8 @@ class FreqaiDataKitchen:
def find_features(self, dataframe: DataFrame) -> None:
"""
Find features in the strategy provided dataframe
:params:
dataframe: DataFrame = strategy provided dataframe
:returns:
:param dataframe: DataFrame = strategy provided dataframe
:return:
features: list = the features to be used for training/prediction
"""
column_names = dataframe.columns
@ -772,7 +764,7 @@ class FreqaiDataKitchen:
if backtest_timerange.stopts == 0:
# 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
# to simply ask user to add their end date
raise OperationalException("FreqAI backtesting does not allow open ended timeranges. "
@ -808,10 +800,9 @@ class FreqaiDataKitchen:
"""
A model age checker to determine if the model is trustworthy based on user defined
`expiration_hours` in the configuration file.
:params:
trained_timestamp: int = The time of training for the most recent model.
:returns:
bool = If the model is expired or not.
:param trained_timestamp: int = The time of training for the most recent model.
:return:
bool = If the model is expired or not.
"""
time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
elapsed_time = (time - trained_timestamp) / 3600 # hours
@ -873,7 +864,7 @@ class FreqaiDataKitchen:
data_load_timerange.stopts = int(time)
else: # user passed no live_trained_timerange in config
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)
@ -1053,7 +1044,7 @@ class FreqaiDataKitchen:
if isinstance(object, np.generic):
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
# use_SVM_to_remove_outliers (computationally more efficient and apparently higher performance).

2
freqtrade/freqai/freqai_interface.py
View File

@ -572,7 +572,7 @@ class IFreqaiModel(ABC):
return
# 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
def train(self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen) -> Any: