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robcaulk
2023-05-29 23:35:24 +02:00
parent 785f0d396f
commit f6a32f4ffd
3 changed files with 5 additions and 118 deletions
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115
freqtrade/freqai/data_kitchen.py
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@@ -77,8 +77,6 @@ class FreqaiDataKitchen:
self.backtest_predictions_folder: str = "backtesting_predictions"
self.live = live
self.pair = pair
# self.svm_model: linear_model.SGDOneClassSVM = None
self.keras: bool = self.freqai_config.get("keras", False)
self.set_all_pairs()
self.backtest_live_models = config.get("freqai_backtest_live_models", False)
@@ -225,13 +223,6 @@ class FreqaiDataKitchen:
drop_index = pd.isnull(filtered_df).any(axis=1) # get the rows that have NaNs,
drop_index = drop_index.replace(True, 1).replace(False, 0) # pep8 requirement.
if (training_filter):
# const_cols = list((filtered_df.nunique() == 1).loc[lambda x: x].index)
# if const_cols:
# filtered_df = filtered_df.filter(filtered_df.columns.difference(const_cols))
# self.data['constant_features_list'] = const_cols
# logger.warning(f"Removed features {const_cols} with constant values.")
# else:
# self.data['constant_features_list'] = []
# we don't care about total row number (total no. datapoints) in training, we only care
# about removing any row with NaNs
@@ -264,9 +255,6 @@ class FreqaiDataKitchen:
else:
# if 'constant_features_list' in self.data and len(self.data['constant_features_list']):
# filtered_df = self.check_pred_labels(filtered_df)
# we are backtesting so we need to preserve row number to send back to strategy,
# so now we use do_predict to avoid any prediction based on a NaN
drop_index = pd.isnull(filtered_df).any(axis=1)
@@ -308,107 +296,6 @@ class FreqaiDataKitchen:
return self.data_dictionary
# def normalize_data(self, data_dictionary: Dict) -> Dict[Any, Any]:
# """
# Normalize all data in the data_dictionary according to the training dataset
# :param data_dictionary: dictionary containing the cleaned and
# split training/test data/labels
# :returns:
# :data_dictionary: updated dictionary with standardized values.
# """
# # standardize the data by training stats
# train_max = data_dictionary["train_features"].max()
# train_min = data_dictionary["train_features"].min()
# data_dictionary["train_features"] = (
# 2 * (data_dictionary["train_features"] - train_min) / (train_max - train_min) - 1
# )
# data_dictionary["test_features"] = (
# 2 * (data_dictionary["test_features"] - train_min) / (train_max - train_min) - 1
# )
# for item in train_max.keys():
# self.data[item + "_max"] = train_max[item]
# self.data[item + "_min"] = train_min[item]
# for item in data_dictionary["train_labels"].keys():
# if data_dictionary["train_labels"][item].dtype == object:
# continue
# train_labels_max = data_dictionary["train_labels"][item].max()
# train_labels_min = data_dictionary["train_labels"][item].min()
# data_dictionary["train_labels"][item] = (
# 2
# * (data_dictionary["train_labels"][item] - train_labels_min)
# / (train_labels_max - train_labels_min)
# - 1
# )
# if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
# data_dictionary["test_labels"][item] = (
# 2
# * (data_dictionary["test_labels"][item] - train_labels_min)
# / (train_labels_max - train_labels_min)
# - 1
# )
# self.data[f"{item}_max"] = train_labels_max
# self.data[f"{item}_min"] = train_labels_min
# return data_dictionary
# def normalize_single_dataframe(self, df: DataFrame) -> DataFrame:
# train_max = df.max()
# train_min = df.min()
# df = (
# 2 * (df - train_min) / (train_max - train_min) - 1
# )
# for item in train_max.keys():
# self.data[item + "_max"] = train_max[item]
# self.data[item + "_min"] = train_min[item]
# return df
# def normalize_data_from_metadata(self, df: DataFrame) -> DataFrame:
# """
# Normalize a set of data using the mean and standard deviation from
# the associated training data.
# :param df: Dataframe to be standardized
# """
# train_max = [None] * len(df.keys())
# train_min = [None] * len(df.keys())
# for i, item in enumerate(df.keys()):
# train_max[i] = self.data[f"{item}_max"]
# train_min[i] = self.data[f"{item}_min"]
# train_max_series = pd.Series(train_max, index=df.keys())
# train_min_series = pd.Series(train_min, index=df.keys())
# df = (
# 2 * (df - train_min_series) / (train_max_series - train_min_series) - 1
# )
# return df
# def denormalize_labels_from_metadata(self, df: DataFrame) -> DataFrame:
# """
# Denormalize a set of data using the mean and standard deviation from
# the associated training data.
# :param df: Dataframe of predictions to be denormalized
# """
# for label in df.columns:
# if df[label].dtype == object or label in self.unique_class_list:
# continue
# df[label] = (
# (df[label] + 1)
# * (self.data[f"{label}_max"] - self.data[f"{label}_min"])
# / 2
# ) + self.data[f"{label}_min"]
# return df
def split_timerange(
self, tr: str, train_split: int = 28, bt_split: float = 7
) -> Tuple[list, list]:
@@ -453,9 +340,7 @@ class FreqaiDataKitchen:
tr_training_list_timerange.append(copy.deepcopy(timerange_train))
# associated backtest period
timerange_backtest.startts = timerange_train.stopts
timerange_backtest.stopts = timerange_backtest.startts + int(bt_period)
if timerange_backtest.stopts > config_timerange.stopts:

6
freqtrade/freqai/freqai_interface.py
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@@ -507,8 +507,10 @@ class IFreqaiModel(ABC):
def define_data_pipeline(self, dk: FreqaiDataKitchen) -> None:
ft_params = self.freqai_info["feature_parameters"]
dk.feature_pipeline = Pipeline(
[('scaler', ds.DataSieveMinMaxScaler(feature_range=(-1, 1)))])
dk.feature_pipeline = Pipeline([
('const', ds.DataSieveVarianceThreshold(threshold=0)),
('scaler', ds.DataSieveMinMaxScaler(feature_range=(-1, 1)))
])
if ft_params.get("principal_component_analysis", False):
dk.feature_pipeline.steps += [('pca', ds.DataSievePCA())]

2
requirements-freqai.txt
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@@ -10,4 +10,4 @@ catboost==1.2; 'arm' not in platform_machine and (sys_platform != 'darwin' or py
lightgbm==3.3.5
xgboost==1.7.5
tensorboard==2.13.0
datasieve==0.0.9
datasieve==0.1.0