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add min max strategies and data augmentation training

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This commit is contained in:
longyu
2022-09-15 14:14:02 +02:00
parent 8a236c3c4f
commit ff285fffe1
3 changed files with 687 additions and 5 deletions
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25
freqtrade/freqai/data_kitchen.py
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@@ -870,11 +870,25 @@ class FreqaiDataKitchen:
"""
Add noise to train features to reduce the risk of overfitting.
"""
mu = 0 # no shift
sigma = self.freqai_config["feature_parameters"]["noise_standard_deviation"]
compute_df = self.data_dictionary['train_features']
noise = np.random.normal(mu, sigma, [compute_df.shape[0], compute_df.shape[1]])
self.data_dictionary['train_features'] += noise
da = self.freqai_config["feature_parameters"]["data_augment"]
X = self.data_dictionary['train_features']
y = self.data_dictionary['train_labels']
da_type = da.get("type", "std")
if da_type == "std":
# generate alpha values of 0-mean and 1-std
alpha = np.random.randn(*X.shape)
scale = da.get("vaue", 0.01)
Xaugmented = X + alpha * scale * X.std(0)[None, :]
X = np.vstack((X, Xaugmented))
y = y.append(y)
self.data_dictionary['train_features'] = X
self.data_dictionary['train_labels'] = y
elif da_type == "constant":
mu = 0 # no shift
sigma = self.freqai_config["feature_parameters"]["data_augment"]["value"]
compute_df = self.data_dictionary['train_features']
noise = np.random.normal(mu, sigma, [compute_df.shape[0], compute_df.shape[1]])
self.data_dictionary['train_features'] += noise
return
def find_features(self, dataframe: DataFrame) -> None:
@@ -1217,6 +1231,7 @@ class FreqaiDataKitchen:
for key in self.label_list:
if dataframe[key].dtype == object:
# TODO: make sure the `dataframe[key].dropna().unique()` are objet type too!
self.unique_classes[key] = dataframe[key].dropna().unique()
if self.unique_classes: