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start collecting indefinite history of predictions. Allow user to generate statistics on these predictions. Direct FreqAI to save these to disk and reload them if available.

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This commit is contained in:
Robert Caulk
2022-07-11 22:01:48 +02:00
parent 3fc92b1b21
commit 8ce6b18318
5 changed files with 109 additions and 39 deletions
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45
freqtrade/freqai/data_kitchen.py
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@@ -138,19 +138,6 @@ class FreqaiDataKitchen:
self.dd.pair_dict[coin]["data_path"] = str(self.data_path)
self.dd.save_drawer_to_disk()
# TODO add a helper function to let user save/load any data they are custom adding. We
# do not want them having to edit the default save/load methods here. Below is an example
# of what we do NOT want.
# if self.freqai_config.get('feature_parameters','determine_statistical_distributions'):
# self.data_dictionary["upper_quantiles"].to_pickle(
# save_path / str(self.model_filename + "_upper_quantiles.pkl")
# )
# self.data_dictionary["lower_quantiles"].to_pickle(
# save_path / str(self.model_filename + "_lower_quantiles.pkl")
# )
return
def load_data(self, coin: str = "", keras_model=False) -> Any:
@@ -184,22 +171,6 @@ class FreqaiDataKitchen:
self.data_path / str(self.model_filename + "_trained_df.pkl")
)
# TODO add a helper function to let user save/load any data they are custom adding. We
# do not want them having to edit the default save/load methods here. Below is an example
# of what we do NOT want.
# if self.freqai_config.get('feature_parameters','determine_statistical_distributions'):
# self.data_dictionary["upper_quantiles"] = pd.read_pickle(
# self.data_path / str(self.model_filename + "_upper_quantiles.pkl")
# )
# self.data_dictionary["lower_quantiles"] = pd.read_pickle(
# self.data_path / str(self.model_filename + "_lower_quantiles.pkl")
# )
# self.data_path = Path(self.data["data_path"])
# self.model_filename = self.data["model_filename"]
# try to access model in memory instead of loading object from disk to save time
if self.live and self.model_filename in self.dd.model_dictionary:
model = self.dd.model_dictionary[self.model_filename]
@@ -207,7 +178,6 @@ class FreqaiDataKitchen:
model = load(self.data_path / str(self.model_filename + "_model.joblib"))
else:
from tensorflow import keras
model = keras.models.load_model(self.data_path / str(self.model_filename + "_model.h5"))
if Path(self.data_path / str(self.model_filename + "_svm_model.joblib")).resolve().exists():
@@ -263,7 +233,6 @@ class FreqaiDataKitchen:
labels,
weights,
stratify=stratification,
# shuffle=False,
**self.config["freqai"]["data_split_parameters"],
)
@@ -276,7 +245,6 @@ class FreqaiDataKitchen:
unfiltered_dataframe: DataFrame,
training_feature_list: List,
label_list: List = list(),
# labels: DataFrame = pd.DataFrame(),
training_filter: bool = True,
) -> Tuple[DataFrame, DataFrame]:
"""
@@ -1135,6 +1103,19 @@ class FreqaiDataKitchen:
return dataframe
def fit_live_predictions(self) -> None:
"""
Fit the labels with a gaussian distribution
"""
import scipy as spy
num_candles = self.freqai_config.get('fit_live_predictions_candles', 100)
self.data["labels_mean"], self.data["labels_std"] = {}, {}
for label in self.label_list:
f = spy.stats.norm.fit(self.dd.historic_predictions[self.pair][label].tail(num_candles))
self.data["labels_mean"][label], self.data["labels_std"][label] = f[0], f[1]
return
def fit_labels(self) -> None:
"""
Fit the labels with a gaussian distribution