Rehaul organization of return values
This commit is contained in:
robcaulk
parent
93e1410ed9
commit
106131ff0f
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@ -163,21 +163,30 @@ class FreqaiDataDrawer:
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# send pair to end of queue
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self.pair_dict[pair]['priority'] = len(self.pair_dict)
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def set_initial_return_values(self, pair: str, dh, dataframe: DataFrame) -> None:
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def set_initial_return_values(self, pair: str, dk, pred_df, do_preds) -> None:
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"""
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Set the initial return values to a persistent dataframe. This avoids needing to repredict on
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historical candles, and also stores historical predictions despite retrainings (so stored
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predictions are true predictions, not just inferencing on trained data)
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"""
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self.model_return_values[pair] = pd.DataFrame()
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for label in dk.label_list:
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self.model_return_values[pair][label] = pred_df[label]
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self.model_return_values[pair][f'{label}_mean'] = dk.data['labels_mean'][label]
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self.model_return_values[pair][f'{label}_std'] = dk.data['labels_std'][label]
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self.model_return_values[pair] = dataframe
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self.model_return_values[pair]['target_mean'] = dh.data['target_mean']
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self.model_return_values[pair]['target_std'] = dh.data['target_std']
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if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
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self.model_return_values[pair]['DI_values'] = dh.DI_values
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self.model_return_values[pair]['DI_values'] = dk.DI_values
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self.model_return_values[pair]['do_predict'] = do_preds
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def append_model_predictions(self, pair: str, predictions, do_preds,
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target_mean, target_std, dh, len_df) -> None:
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dk, len_df) -> None:
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# strat seems to feed us variable sized dataframes - and since we are trying to build our
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# own return array in the same shape, we need to figure out how the size has changed
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# and adapt our stored/returned info accordingly.
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length_difference = len(self.model_return_values[pair]['prediction']) - len_df
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length_difference = len(self.model_return_values[pair]) - len_df
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i = 0
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if length_difference == 0:
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@ -185,30 +194,56 @@ class FreqaiDataDrawer:
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elif length_difference > 0:
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i = length_difference + 1
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df = self.model_return_values[pair].shift(-i)
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df = self.model_return_values[pair] = self.model_return_values[pair].shift(-i)
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df['prediction'].iloc[-1] = predictions[-1]
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for label in dk.label_list:
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df[label].iloc[-1] = predictions[label].iloc[-1]
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df[f"{label}_mean"].iloc[-1] = dk.data['labels_mean'][label]
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df[f"{label}_std"].iloc[-1] = dk.data['labels_std'][label]
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# df['prediction'].iloc[-1] = predictions[-1]
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df['do_predict'].iloc[-1] = do_preds[-1]
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df['target_mean'].iloc[-1] = target_mean
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df['target_std'].iloc[-1] = target_std
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if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
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df['DI_values'].iloc[-1] = dh.DI_values[-1]
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df['DI_values'].iloc[-1] = dk.DI_values[-1]
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if length_difference < 0:
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prepend_df = pd.DataFrame(np.zeros((abs(length_difference) - 1, len(df.columns))),
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columns=df.columns)
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df = pd.concat([prepend_df, df], axis=0)
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def return_null_values_to_strategy(self, dataframe: DataFrame, dh) -> None:
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def attach_return_values_to_return_dataframe(self, pair: str, dataframe) -> DataFrame:
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"""
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Attach the return values to the strat dataframe
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:params:
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dataframe: DataFrame = strat dataframe
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:returns:
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dataframe: DataFrame = strat dataframe with return values attached
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"""
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df = self.model_return_values[pair]
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to_keep = [col for col in dataframe.columns if not col.startswith('&')]
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dataframe = pd.concat([dataframe[to_keep], df], axis=1)
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return dataframe
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dataframe['prediction'] = 0
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def return_null_values_to_strategy(self, dataframe: DataFrame, dk) -> None:
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"""
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Build 0 filled dataframe to return to strategy
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"""
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dk.find_features(dataframe)
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for label in dk.label_list:
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dataframe[label] = 0
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dataframe[f"{label}_mean"] = 0
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dataframe[f"{label}_std"] = 0
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# dataframe['prediction'] = 0
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dataframe['do_predict'] = 0
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dataframe['target_mean'] = 0
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dataframe['target_std'] = 0
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if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
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dataframe['DI_value'] = 0
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dk.return_dataframe = dataframe
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def purge_old_models(self) -> None:
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model_folders = [x for x in self.full_path.iterdir() if x.is_dir()]
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@ -257,7 +292,7 @@ class FreqaiDataDrawer:
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# with open(self.full_path / str('model_return_values.json'), "w") as fp:
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# json.dump(self.model_return_values, fp, default=self.np_encoder)
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# def load_model_return_values_from_disk(self, dh: FreqaiDataKitchen) -> FreqaiDataKitchen:
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# def load_model_return_values_from_disk(self, dk: FreqaiDataKitchen) -> FreqaiDataKitchen:
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# exists = Path(self.full_path / str('model_return_values.json')).resolve().exists()
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# if exists:
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# with open(self.full_path / str('model_return_values.json'), "r") as fp:
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@ -268,4 +303,4 @@ class FreqaiDataDrawer:
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# logger.warning(f'Follower could not find pair_dictionary at {self.full_path} '
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# 'sending null values back to strategy')
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# return exists, dh
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# return exists, dk
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@ -53,6 +53,7 @@ class FreqaiDataKitchen:
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self.full_target_mean: npt.ArrayLike = np.array([])
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self.full_target_std: npt.ArrayLike = np.array([])
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self.data_path = Path()
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self.label_list: List = []
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self.model_filename: str = ""
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self.live = live
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self.pair = pair
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@ -68,8 +69,8 @@ class FreqaiDataKitchen:
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config["freqai"]["train_period"],
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config["freqai"]["backtest_period"],
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)
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self.data_drawer = data_drawer
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# self.strat_dataframe: DataFrame = strat_dataframe
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self.dd = data_drawer
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def set_paths(self, pair: str, trained_timestamp: int = None,) -> None:
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"""
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@ -88,7 +89,7 @@ class FreqaiDataKitchen:
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return
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def save_data(self, model: Any, coin: str = '', keras_model=False) -> None:
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def save_data(self, model: Any, coin: str = '', keras_model=False, label=None) -> None:
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"""
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Saves all data associated with a model for a single sub-train time range
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:params:
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@ -103,9 +104,9 @@ class FreqaiDataKitchen:
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# Save the trained model
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if not keras_model:
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dump(model, save_path / str(self.model_filename + "_model.joblib"))
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dump(model, save_path / f"{self.model_filename}_model.joblib")
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else:
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model.save(save_path / str(self.model_filename + "_model.h5"))
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model.save(save_path / f"{self.model_filename}_model.h5")
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if self.svm_model is not None:
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dump(self.svm_model, save_path / str(self.model_filename + "_svm_model.joblib"))
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@ -113,6 +114,7 @@ class FreqaiDataKitchen:
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self.data["data_path"] = str(self.data_path)
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self.data["model_filename"] = str(self.model_filename)
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self.data["training_features_list"] = list(self.data_dictionary["train_features"].columns)
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self.data['label_list'] = self.label_list
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# store the metadata
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with open(save_path / str(self.model_filename + "_metadata.json"), "w") as fp:
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json.dump(self.data, fp, default=self.np_encoder)
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@ -127,10 +129,10 @@ class FreqaiDataKitchen:
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str(self.model_filename + "_pca_object.pkl"), "wb"))
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# if self.live:
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self.data_drawer.model_dictionary[self.model_filename] = model
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self.data_drawer.pair_dict[coin]['model_filename'] = self.model_filename
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self.data_drawer.pair_dict[coin]['data_path'] = str(self.data_path)
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self.data_drawer.save_drawer_to_disk()
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self.dd.model_dictionary[self.model_filename] = model
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self.dd.pair_dict[coin]['model_filename'] = self.model_filename
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self.dd.pair_dict[coin]['data_path'] = str(self.data_path)
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self.dd.save_drawer_to_disk()
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# TODO add a helper function to let user save/load any data they are custom adding. We
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# do not want them having to edit the default save/load methods here. Below is an example
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@ -154,12 +156,12 @@ class FreqaiDataKitchen:
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:model: User trained model which can be inferenced for new predictions
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"""
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if not self.data_drawer.pair_dict[coin]['model_filename']:
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if not self.dd.pair_dict[coin]['model_filename']:
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return None
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if self.live:
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self.model_filename = self.data_drawer.pair_dict[coin]['model_filename']
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self.data_path = Path(self.data_drawer.pair_dict[coin]['data_path'])
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self.model_filename = self.dd.pair_dict[coin]['model_filename']
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self.data_path = Path(self.dd.pair_dict[coin]['data_path'])
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if self.freqai_config.get('follow_mode', False):
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# follower can be on a different system which is rsynced to the leader:
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self.data_path = Path(self.config["user_data_dir"] /
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@ -169,6 +171,7 @@ class FreqaiDataKitchen:
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with open(self.data_path / str(self.model_filename + "_metadata.json"), "r") as fp:
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self.data = json.load(fp)
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self.training_features_list = self.data["training_features_list"]
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self.label_list = self.data['label_list']
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self.data_dictionary["train_features"] = pd.read_pickle(
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self.data_path / str(self.model_filename + "_trained_df.pkl")
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@ -191,8 +194,8 @@ class FreqaiDataKitchen:
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# self.model_filename = self.data["model_filename"]
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# try to access model in memory instead of loading object from disk to save time
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if self.live and self.model_filename in self.data_drawer.model_dictionary:
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model = self.data_drawer.model_dictionary[self.model_filename]
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if self.live and self.model_filename in self.dd.model_dictionary:
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model = self.dd.model_dictionary[self.model_filename]
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elif not keras_model:
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model = load(self.data_path / str(self.model_filename + "_model.joblib"))
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else:
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@ -265,11 +268,12 @@ class FreqaiDataKitchen:
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self,
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unfiltered_dataframe: DataFrame,
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training_feature_list: List,
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labels: DataFrame = pd.DataFrame(),
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label_list: List = list(),
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# labels: DataFrame = pd.DataFrame(),
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training_filter: bool = True,
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) -> Tuple[DataFrame, DataFrame]:
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"""
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Filter the unfiltered dataframe to extract the user requested features and properly
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Filter the unfiltered dataframe to extract the user requested features/labels and properly
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remove all NaNs. Any row with a NaN is removed from training dataset or replaced with
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0s in the prediction dataset. However, prediction dataset do_predict will reflect any
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row that had a NaN and will shield user from that prediction.
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@ -287,6 +291,7 @@ class FreqaiDataKitchen:
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"""
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filtered_dataframe = unfiltered_dataframe.filter(training_feature_list, axis=1)
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filtered_dataframe = filtered_dataframe.replace([np.inf, -np.inf], np.nan)
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drop_index = pd.isnull(filtered_dataframe).any(1) # get the rows that have NaNs,
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drop_index = drop_index.replace(True, 1).replace(False, 0) # pep8 requirement.
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if (
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@ -294,10 +299,8 @@ class FreqaiDataKitchen:
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): # we don't care about total row number (total no. datapoints) in training, we only care
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# about removing any row with NaNs
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# if labels has multiple columns (user wants to train multiple models), we detect here
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if labels.shape[1] == 1:
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drop_index_labels = pd.isnull(labels)
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else:
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drop_index_labels = pd.isnull(labels).any(1)
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labels = unfiltered_dataframe.filter(label_list, axis=1)
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drop_index_labels = pd.isnull(labels).any(1)
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drop_index_labels = drop_index_labels.replace(True, 1).replace(False, 0)
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filtered_dataframe = filtered_dataframe[
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(drop_index == 0) & (drop_index_labels == 0)
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@ -333,6 +336,7 @@ class FreqaiDataKitchen:
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len(self.do_predict) - self.do_predict.sum(),
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len(filtered_dataframe),
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)
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labels = []
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return filtered_dataframe, labels
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@ -388,8 +392,8 @@ class FreqaiDataKitchen:
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self.data[item + "_max"] = train_max[item]
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self.data[item + "_min"] = train_min[item]
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self.data["labels_max"] = train_labels_max
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self.data["labels_min"] = train_labels_min
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self.data["labels_max"] = train_labels_max.to_dict()
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self.data["labels_min"] = train_labels_min.to_dict()
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return data_dictionary
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@ -618,7 +622,7 @@ class FreqaiDataKitchen:
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return
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def find_features(self, dataframe: DataFrame) -> list:
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def find_features(self, dataframe: DataFrame) -> None:
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"""
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Find features in the strategy provided dataframe
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:params:
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@ -628,9 +632,13 @@ class FreqaiDataKitchen:
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"""
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column_names = dataframe.columns
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features = [c for c in column_names if '%' in c]
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labels = [c for c in column_names if '&' in c]
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if not features:
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raise OperationalException("Could not find any features!")
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return features
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self.training_features_list = features
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self.label_list = labels
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# return features, labels
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def check_if_pred_in_training_spaces(self) -> None:
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"""
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@ -808,26 +816,6 @@ class FreqaiDataKitchen:
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data_load_timerange.stopts = int(time)
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retrain = True
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# logger.info(
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# f'Total data download needed '
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# f'{(data_load_timerange.stopts - data_load_timerange.startts)/SECONDS_IN_DAY:.2f}'
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# ' days')
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# logger.info(f'Total training timerange '
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# f'{(trained_timerange.stopts - trained_timerange.startts)/SECONDS_IN_DAY} '
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# ' days')
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# if retrain:
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# coin, _ = metadata['pair'].split("/")
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# # set the new data_path
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# self.data_path = Path(self.full_path / str("sub-train" + "-" +
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# str(int(trained_timerange.stopts))))
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# self.model_filename = "cb_" + coin.lower() + "_" + str(int(trained_timerange.stopts))
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# # this is not persistent at the moment TODO
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# self.freqai_config['live_trained_timerange'] = str(int(trained_timerange.stopts))
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# # enables persistence, but not fully implemented into save/load data yer
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# self.data['live_trained_timerange'] = str(int(trained_timerange.stopts))
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return retrain, trained_timerange, data_load_timerange
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def set_new_model_names(self, pair: str, trained_timerange: TimeRange):
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@ -896,8 +884,8 @@ class FreqaiDataKitchen:
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dataframe: DataFrame = strategy provided dataframe
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"""
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with self.data_drawer.history_lock:
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history_data = self.data_drawer.historic_data
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with self.dd.history_lock:
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history_data = self.dd.historic_data
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for pair in self.all_pairs:
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for tf in self.freqai_config.get('timeframes'):
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@ -939,7 +927,7 @@ class FreqaiDataKitchen:
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timerange: TimeRange = full timerange required to populate all indicators
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for training according to user defined train_period
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"""
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history_data = self.data_drawer.historic_data
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history_data = self.dd.historic_data
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for pair in self.all_pairs:
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if pair not in history_data:
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@ -964,10 +952,10 @@ class FreqaiDataKitchen:
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metadata: dict = strategy furnished pair metadata
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"""
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with self.data_drawer.history_lock:
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with self.dd.history_lock:
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corr_dataframes: Dict[Any, Any] = {}
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base_dataframes: Dict[Any, Any] = {}
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historic_data = self.data_drawer.historic_data
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historic_data = self.dd.historic_data
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pairs = self.freqai_config.get('corr_pairlist', [])
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for tf in self.freqai_config.get('timeframes'):
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@ -1068,18 +1056,18 @@ class FreqaiDataKitchen:
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"""
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import scipy as spy
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f = spy.stats.norm.fit(self.data_dictionary["train_labels"])
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self.data['labels_mean'], self.data['labels_std'] = {}, {}
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for label in self.label_list:
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f = spy.stats.norm.fit(self.data_dictionary["train_labels"][label])
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self.data["labels_mean"][label], self.data["labels_std"][label] = f[0], f[1]
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# KEEPME incase we want to let user start to grab quantiles.
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# upper_q = spy.stats.norm.ppf(self.freqai_config['feature_parameters'][
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# 'target_quantile'], *f)
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# lower_q = spy.stats.norm.ppf(1 - self.freqai_config['feature_parameters'][
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# 'target_quantile'], *f)
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self.data["target_mean"], self.data["target_std"] = f[0], f[1]
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# self.data["upper_quantile"] = upper_q
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# self.data["lower_quantile"] = lower_q
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return
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def np_encoder(self, object):
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|
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@ -59,9 +59,7 @@ class IFreqaiModel(ABC):
|
|||
self.update_historic_data = 0
|
||||
self.set_full_path()
|
||||
self.follow_mode = self.freqai_info.get('follow_mode', False)
|
||||
self.data_drawer = FreqaiDataDrawer(Path(self.full_path),
|
||||
self.config,
|
||||
self.follow_mode)
|
||||
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
|
||||
self.lock = threading.Lock()
|
||||
self.follow_mode = self.freqai_info.get('follow_mode', False)
|
||||
self.identifier = self.freqai_info.get('identifier', 'no_id_provided')
|
||||
|
|
@ -91,12 +89,12 @@ class IFreqaiModel(ABC):
|
|||
"""
|
||||
|
||||
self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
|
||||
self.data_drawer.set_pair_dict_info(metadata)
|
||||
self.dd.set_pair_dict_info(metadata)
|
||||
|
||||
if self.live:
|
||||
self.dh = FreqaiDataKitchen(self.config, self.data_drawer,
|
||||
self.dk = FreqaiDataKitchen(self.config, self.dd,
|
||||
self.live, metadata["pair"])
|
||||
dh = self.start_live(dataframe, metadata, strategy, self.dh)
|
||||
dk = self.start_live(dataframe, metadata, strategy, self.dk)
|
||||
|
||||
# For backtesting, each pair enters and then gets trained for each window along the
|
||||
# sliding window defined by "train_period" (training window) and "backtest_period"
|
||||
|
|
@ -104,19 +102,19 @@ class IFreqaiModel(ABC):
|
|||
# FreqAI slides the window and sequentially builds the backtesting results before returning
|
||||
# the concatenated results for the full backtesting period back to the strategy.
|
||||
elif not self.follow_mode:
|
||||
self.dh = FreqaiDataKitchen(self.config, self.data_drawer, self.live, metadata["pair"])
|
||||
logger.info(f'Training {len(self.dh.training_timeranges)} timeranges')
|
||||
dh = self.start_backtesting(dataframe, metadata, self.dh)
|
||||
self.dk = FreqaiDataKitchen(self.config, self.dd, self.live, metadata["pair"])
|
||||
logger.info(f'Training {len(self.dk.training_timeranges)} timeranges')
|
||||
dk = self.start_backtesting(dataframe, metadata, self.dk)
|
||||
|
||||
dataframe = self.remove_features_from_df(dataframe)
|
||||
return self.return_values(dataframe, dh)
|
||||
dataframe = self.remove_features_from_df(dk.return_dataframe)
|
||||
return self.return_values(dataframe, dk)
|
||||
|
||||
@threaded
|
||||
def start_scanning(self, strategy: IStrategy) -> None:
|
||||
"""
|
||||
Function designed to constantly scan pairs for retraining on a separate thread (intracandle)
|
||||
to improve model youth. This function is agnostic to data preparation/collection/storage,
|
||||
it simply trains on what ever data is available in the self.data_drawer.
|
||||
it simply trains on what ever data is available in the self.dd.
|
||||
:params:
|
||||
strategy: IStrategy = The user defined strategy class
|
||||
"""
|
||||
|
|
@ -124,33 +122,33 @@ class IFreqaiModel(ABC):
|
|||
time.sleep(1)
|
||||
for pair in self.config.get('exchange', {}).get('pair_whitelist'):
|
||||
|
||||
(_, trained_timestamp, _, _) = self.data_drawer.get_pair_dict_info(pair)
|
||||
(_, trained_timestamp, _, _) = self.dd.get_pair_dict_info(pair)
|
||||
|
||||
if self.data_drawer.pair_dict[pair]['priority'] != 1:
|
||||
if self.dd.pair_dict[pair]['priority'] != 1:
|
||||
continue
|
||||
dh = FreqaiDataKitchen(self.config, self.data_drawer,
|
||||
dk = FreqaiDataKitchen(self.config, self.dd,
|
||||
self.live, pair)
|
||||
|
||||
# file_exists = False
|
||||
|
||||
dh.set_paths(pair, trained_timestamp)
|
||||
dk.set_paths(pair, trained_timestamp)
|
||||
# file_exists = self.model_exists(pair,
|
||||
# dh,
|
||||
# dk,
|
||||
# trained_timestamp=trained_timestamp,
|
||||
# model_filename=model_filename,
|
||||
# scanning=True)
|
||||
|
||||
(retrain,
|
||||
new_trained_timerange,
|
||||
data_load_timerange) = dh.check_if_new_training_required(trained_timestamp)
|
||||
dh.set_paths(pair, new_trained_timerange.stopts)
|
||||
data_load_timerange) = dk.check_if_new_training_required(trained_timestamp)
|
||||
dk.set_paths(pair, new_trained_timerange.stopts)
|
||||
|
||||
if retrain: # or not file_exists:
|
||||
self.train_model_in_series(new_trained_timerange, pair,
|
||||
strategy, dh, data_load_timerange)
|
||||
strategy, dk, data_load_timerange)
|
||||
|
||||
def start_backtesting(self, dataframe: DataFrame, metadata: dict,
|
||||
dh: FreqaiDataKitchen) -> FreqaiDataKitchen:
|
||||
dk: FreqaiDataKitchen) -> FreqaiDataKitchen:
|
||||
"""
|
||||
The main broad execution for backtesting. For backtesting, each pair enters and then gets
|
||||
trained for each window along the sliding window defined by "train_period" (training window)
|
||||
|
|
@ -161,9 +159,9 @@ class IFreqaiModel(ABC):
|
|||
:params:
|
||||
dataframe: DataFrame = strategy passed dataframe
|
||||
metadata: Dict = pair metadata
|
||||
dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
|
||||
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
|
||||
:returns:
|
||||
dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
|
||||
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
|
||||
"""
|
||||
|
||||
# Loop enforcing the sliding window training/backtesting paradigm
|
||||
|
|
@ -172,15 +170,15 @@ class IFreqaiModel(ABC):
|
|||
# following tr_train. Both of these windows slide through the
|
||||
# entire backtest
|
||||
for tr_train, tr_backtest in zip(
|
||||
dh.training_timeranges, dh.backtesting_timeranges
|
||||
dk.training_timeranges, dk.backtesting_timeranges
|
||||
):
|
||||
(_, _, _, _) = self.data_drawer.get_pair_dict_info(metadata['pair'])
|
||||
(_, _, _, _) = self.dd.get_pair_dict_info(metadata['pair'])
|
||||
gc.collect()
|
||||
dh.data = {} # clean the pair specific data between training window sliding
|
||||
dk.data = {} # clean the pair specific data between training window sliding
|
||||
self.training_timerange = tr_train
|
||||
# self.training_timerange_timerange = tr_train
|
||||
dataframe_train = dh.slice_dataframe(tr_train, dataframe)
|
||||
dataframe_backtest = dh.slice_dataframe(tr_backtest, dataframe)
|
||||
dataframe_train = dk.slice_dataframe(tr_train, dataframe)
|
||||
dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
|
||||
|
||||
trained_timestamp = tr_train # TimeRange.parse_timerange(tr_train)
|
||||
tr_train_startts_str = datetime.datetime.utcfromtimestamp(
|
||||
|
|
@ -190,33 +188,33 @@ class IFreqaiModel(ABC):
|
|||
logger.info("Training %s", metadata["pair"])
|
||||
logger.info(f'Training {tr_train_startts_str} to {tr_train_stopts_str}')
|
||||
|
||||
dh.data_path = Path(dh.full_path /
|
||||
dk.data_path = Path(dk.full_path /
|
||||
str("sub-train" + "-" + metadata['pair'].split("/")[0] +
|
||||
str(int(trained_timestamp.stopts))))
|
||||
if not self.model_exists(metadata["pair"], dh,
|
||||
if not self.model_exists(metadata["pair"], dk,
|
||||
trained_timestamp=trained_timestamp.stopts):
|
||||
self.model = self.train(dataframe_train, metadata['pair'], dh)
|
||||
self.data_drawer.pair_dict[metadata['pair']][
|
||||
self.model = self.train(dataframe_train, metadata['pair'], dk)
|
||||
self.dd.pair_dict[metadata['pair']][
|
||||
'trained_timestamp'] = trained_timestamp.stopts
|
||||
dh.set_new_model_names(metadata['pair'], trained_timestamp)
|
||||
dh.save_data(self.model, metadata['pair'], keras=self.keras)
|
||||
dk.set_new_model_names(metadata['pair'], trained_timestamp)
|
||||
dk.save_data(self.model, metadata['pair'], keras_model=self.keras)
|
||||
else:
|
||||
self.model = dh.load_data(metadata['pair'], keras=self.keras)
|
||||
self.model = dk.load_data(metadata['pair'], keras_model=self.keras)
|
||||
|
||||
self.check_if_feature_list_matches_strategy(dataframe_train, dh)
|
||||
self.check_if_feature_list_matches_strategy(dataframe_train, dk)
|
||||
|
||||
preds, do_preds = self.predict(dataframe_backtest, dh)
|
||||
preds, do_preds = self.predict(dataframe_backtest, dk)
|
||||
|
||||
dh.append_predictions(preds, do_preds, len(dataframe_backtest))
|
||||
print('predictions', len(dh.full_predictions),
|
||||
'do_predict', len(dh.full_do_predict))
|
||||
dk.append_predictions(preds, do_preds, len(dataframe_backtest))
|
||||
print('predictions', len(dk.full_predictions),
|
||||
'do_predict', len(dk.full_do_predict))
|
||||
|
||||
dh.fill_predictions(len(dataframe))
|
||||
dk.fill_predictions(len(dataframe))
|
||||
|
||||
return dh
|
||||
return dk
|
||||
|
||||
def start_live(self, dataframe: DataFrame, metadata: dict,
|
||||
strategy: IStrategy, dh: FreqaiDataKitchen) -> FreqaiDataKitchen:
|
||||
strategy: IStrategy, dk: FreqaiDataKitchen) -> FreqaiDataKitchen:
|
||||
"""
|
||||
The main broad execution for dry/live. This function will check if a retraining should be
|
||||
performed, and if so, retrain and reset the model.
|
||||
|
|
@ -224,30 +222,30 @@ class IFreqaiModel(ABC):
|
|||
dataframe: DataFrame = strategy passed dataframe
|
||||
metadata: Dict = pair metadata
|
||||
strategy: IStrategy = currently employed strategy
|
||||
dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
|
||||
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
|
||||
:returns:
|
||||
dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
|
||||
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
|
||||
"""
|
||||
|
||||
# update follower
|
||||
if self.follow_mode:
|
||||
self.data_drawer.update_follower_metadata()
|
||||
self.dd.update_follower_metadata()
|
||||
|
||||
# get the model metadata associated with the current pair
|
||||
(_,
|
||||
trained_timestamp,
|
||||
_,
|
||||
return_null_array) = self.data_drawer.get_pair_dict_info(metadata['pair'])
|
||||
return_null_array) = self.dd.get_pair_dict_info(metadata['pair'])
|
||||
|
||||
# if the metadata doesnt exist, the follower returns null arrays to strategy
|
||||
if self.follow_mode and return_null_array:
|
||||
logger.info('Returning null array from follower to strategy')
|
||||
self.data_drawer.return_null_values_to_strategy(dataframe, dh)
|
||||
return dh
|
||||
self.dd.return_null_values_to_strategy(dataframe, dk)
|
||||
return dk
|
||||
|
||||
# append the historic data once per round
|
||||
if self.data_drawer.historic_data:
|
||||
dh.update_historic_data(strategy)
|
||||
if self.dd.historic_data:
|
||||
dk.update_historic_data(strategy)
|
||||
logger.debug(f'Updating historic data on pair {metadata["pair"]}')
|
||||
|
||||
# if trainable, check if model needs training, if so compute new timerange,
|
||||
|
|
@ -257,95 +255,100 @@ class IFreqaiModel(ABC):
|
|||
|
||||
(_,
|
||||
new_trained_timerange,
|
||||
data_load_timerange) = dh.check_if_new_training_required(trained_timestamp)
|
||||
dh.set_paths(metadata['pair'], new_trained_timerange.stopts)
|
||||
data_load_timerange) = dk.check_if_new_training_required(trained_timestamp)
|
||||
dk.set_paths(metadata['pair'], new_trained_timerange.stopts)
|
||||
|
||||
# download candle history if it is not already in memory
|
||||
if not self.data_drawer.historic_data:
|
||||
if not self.dd.historic_data:
|
||||
logger.info('Downloading all training data for all pairs in whitelist and '
|
||||
'corr_pairlist, this may take a while if you do not have the '
|
||||
'data saved')
|
||||
dh.download_all_data_for_training(data_load_timerange)
|
||||
dh.load_all_pair_histories(data_load_timerange)
|
||||
dk.download_all_data_for_training(data_load_timerange)
|
||||
dk.load_all_pair_histories(data_load_timerange)
|
||||
|
||||
if not self.scanning:
|
||||
self.scanning = True
|
||||
self.start_scanning(strategy)
|
||||
|
||||
elif self.follow_mode:
|
||||
dh.set_paths(metadata['pair'], trained_timestamp)
|
||||
dk.set_paths(metadata['pair'], trained_timestamp)
|
||||
logger.info('FreqAI instance set to follow_mode, finding existing pair'
|
||||
f'using { self.identifier }')
|
||||
|
||||
# load the model and associated data into the data kitchen
|
||||
self.model = dh.load_data(coin=metadata['pair'], keras=self.keras)
|
||||
self.model = dk.load_data(coin=metadata['pair'], keras_model=self.keras)
|
||||
|
||||
if not self.model:
|
||||
logger.warning('No model ready, returning null values to strategy.')
|
||||
self.data_drawer.return_null_values_to_strategy(dataframe, dh)
|
||||
return dh
|
||||
self.dd.return_null_values_to_strategy(dataframe, dk)
|
||||
return dk
|
||||
|
||||
# ensure user is feeding the correct indicators to the model
|
||||
self.check_if_feature_list_matches_strategy(dataframe, dh)
|
||||
self.check_if_feature_list_matches_strategy(dataframe, dk)
|
||||
|
||||
self.build_strategy_return_arrays(dataframe, dh, metadata['pair'], trained_timestamp)
|
||||
self.build_strategy_return_arrays(dataframe, dk, metadata['pair'], trained_timestamp)
|
||||
|
||||
return dh
|
||||
return dk
|
||||
|
||||
def build_strategy_return_arrays(self, dataframe: DataFrame,
|
||||
dh: FreqaiDataKitchen, pair: str,
|
||||
dk: FreqaiDataKitchen, pair: str,
|
||||
trained_timestamp: int) -> None:
|
||||
|
||||
# hold the historical predictions in memory so we are sending back
|
||||
# correct array to strategy
|
||||
|
||||
if pair not in self.data_drawer.model_return_values:
|
||||
preds, do_preds = self.predict(dataframe, dh)
|
||||
if pair not in self.dd.model_return_values:
|
||||
pred_df, do_preds = self.predict(dataframe, dk)
|
||||
# mypy doesnt like the typing in else statement, so we need to explicitly add to
|
||||
# dataframe separately
|
||||
dataframe['prediction'], dataframe['do_predict'] = preds, do_preds
|
||||
# dh.append_predictions(preds, do_preds, len(dataframe))
|
||||
# dh.fill_predictions(len(dataframe))
|
||||
self.data_drawer.set_initial_return_values(pair, dh, dataframe)
|
||||
|
||||
# for label in dk.label_list:
|
||||
# dataframe[label] = pred_df[label]
|
||||
|
||||
# dataframe['do_predict'] = do_preds
|
||||
|
||||
# dk.append_predictions(preds, do_preds, len(dataframe))
|
||||
# dk.fill_predictions(len(dataframe))
|
||||
self.dd.set_initial_return_values(pair, dk, pred_df, do_preds)
|
||||
dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
|
||||
return
|
||||
elif self.dh.check_if_model_expired(trained_timestamp):
|
||||
preds, do_preds, dh.DI_values = np.zeros(2), np.ones(2) * 2, np.zeros(2)
|
||||
elif self.dk.check_if_model_expired(trained_timestamp):
|
||||
pred_df = DataFrame(np.zeros((2, len(dk.label_list))), columns=dk.label_list)
|
||||
do_preds, dk.DI_values = np.ones(2) * 2, np.zeros(2)
|
||||
logger.warning('Model expired, returning null values to strategy. Strategy '
|
||||
'construction should take care to consider this event with '
|
||||
'prediction == 0 and do_predict == 2')
|
||||
else:
|
||||
# Only feed in the most recent candle for prediction in live scenario
|
||||
preds, do_preds = self.predict(dataframe.iloc[-self.CONV_WIDTH:], dh, first=False)
|
||||
pred_df, do_preds = self.predict(dataframe.iloc[-self.CONV_WIDTH:], dk, first=False)
|
||||
|
||||
self.dd.append_model_predictions(pair, pred_df, do_preds, dk, len(dataframe))
|
||||
dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
|
||||
|
||||
self.data_drawer.append_model_predictions(pair, preds, do_preds,
|
||||
dh.data["target_mean"],
|
||||
dh.data["target_std"],
|
||||
dh,
|
||||
len(dataframe))
|
||||
return
|
||||
|
||||
def check_if_feature_list_matches_strategy(self, dataframe: DataFrame,
|
||||
dh: FreqaiDataKitchen) -> None:
|
||||
dk: FreqaiDataKitchen) -> None:
|
||||
"""
|
||||
Ensure user is passing the proper feature set if they are reusing an `identifier` pointing
|
||||
to a folder holding existing models.
|
||||
:params:
|
||||
dataframe: DataFrame = strategy provided dataframe
|
||||
dh: FreqaiDataKitchen = non-persistent data container/analyzer for current coin/bot loop
|
||||
dk: FreqaiDataKitchen = non-persistent data container/analyzer for current coin/bot loop
|
||||
"""
|
||||
strategy_provided_features = dh.find_features(dataframe)
|
||||
if 'training_features_list_raw' in dh.data:
|
||||
feature_list = dh.data['training_features_list_raw']
|
||||
dk.find_features(dataframe)
|
||||
if 'training_features_list_raw' in dk.data:
|
||||
feature_list = dk.data['training_features_list_raw']
|
||||
else:
|
||||
feature_list = dh.training_features_list
|
||||
if strategy_provided_features != feature_list:
|
||||
feature_list = dk.training_features_list
|
||||
if dk.training_features_list != feature_list:
|
||||
raise OperationalException("Trying to access pretrained model with `identifier` "
|
||||
"but found different features furnished by current strategy."
|
||||
"Change `identifer` to train from scratch, or ensure the"
|
||||
"strategy is furnishing the same features as the pretrained"
|
||||
"model")
|
||||
|
||||
def data_cleaning_train(self, dh: FreqaiDataKitchen) -> None:
|
||||
def data_cleaning_train(self, dk: FreqaiDataKitchen) -> None:
|
||||
"""
|
||||
Base data cleaning method for train
|
||||
Any function inside this method should drop training data points from the filtered_dataframe
|
||||
|
|
@ -354,23 +357,23 @@ class IFreqaiModel(ABC):
|
|||
"""
|
||||
|
||||
if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
|
||||
dh.principal_component_analysis()
|
||||
dk.principal_component_analysis()
|
||||
|
||||
if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
|
||||
dh.use_SVM_to_remove_outliers(predict=False)
|
||||
dk.use_SVM_to_remove_outliers(predict=False)
|
||||
|
||||
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
|
||||
dh.data["avg_mean_dist"] = dh.compute_distances()
|
||||
dk.data["avg_mean_dist"] = dk.compute_distances()
|
||||
|
||||
# if self.feature_parameters["determine_statistical_distributions"]:
|
||||
# dh.determine_statistical_distributions()
|
||||
# dk.determine_statistical_distributions()
|
||||
# if self.feature_parameters["remove_outliers"]:
|
||||
# dh.remove_outliers(predict=False)
|
||||
# dk.remove_outliers(predict=False)
|
||||
|
||||
def data_cleaning_predict(self, dh: FreqaiDataKitchen, dataframe: DataFrame) -> None:
|
||||
def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None:
|
||||
"""
|
||||
Base data cleaning method for predict.
|
||||
These functions each modify dh.do_predict, which is a dataframe with equal length
|
||||
These functions each modify dk.do_predict, which is a dataframe with equal length
|
||||
to the number of candles coming from and returning to the strategy. Inside do_predict,
|
||||
1 allows prediction and < 0 signals to the strategy that the model is not confident in
|
||||
the prediction.
|
||||
|
|
@ -379,20 +382,20 @@ class IFreqaiModel(ABC):
|
|||
for buy signals.
|
||||
"""
|
||||
if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
|
||||
dh.pca_transform(dataframe)
|
||||
dk.pca_transform(dataframe)
|
||||
|
||||
if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
|
||||
dh.use_SVM_to_remove_outliers(predict=True)
|
||||
dk.use_SVM_to_remove_outliers(predict=True)
|
||||
|
||||
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
|
||||
dh.check_if_pred_in_training_spaces()
|
||||
dk.check_if_pred_in_training_spaces()
|
||||
|
||||
# if self.feature_parameters["determine_statistical_distributions"]:
|
||||
# dh.determine_statistical_distributions()
|
||||
# dk.determine_statistical_distributions()
|
||||
# if self.feature_parameters["remove_outliers"]:
|
||||
# dh.remove_outliers(predict=True) # creates dropped index
|
||||
# dk.remove_outliers(predict=True) # creates dropped index
|
||||
|
||||
def model_exists(self, pair: str, dh: FreqaiDataKitchen, trained_timestamp: int = None,
|
||||
def model_exists(self, pair: str, dk: FreqaiDataKitchen, trained_timestamp: int = None,
|
||||
model_filename: str = '', scanning: bool = False) -> bool:
|
||||
"""
|
||||
Given a pair and path, check if a model already exists
|
||||
|
|
@ -402,14 +405,14 @@ class IFreqaiModel(ABC):
|
|||
coin, _ = pair.split("/")
|
||||
|
||||
if not self.live:
|
||||
dh.model_filename = model_filename = "cb_" + coin.lower() + "_" + str(trained_timestamp)
|
||||
dk.model_filename = model_filename = "cb_" + coin.lower() + "_" + str(trained_timestamp)
|
||||
|
||||
path_to_modelfile = Path(dh.data_path / str(model_filename + "_model.joblib"))
|
||||
path_to_modelfile = Path(dk.data_path / str(model_filename + "_model.joblib"))
|
||||
file_exists = path_to_modelfile.is_file()
|
||||
if file_exists and not scanning:
|
||||
logger.info("Found model at %s", dh.data_path / dh.model_filename)
|
||||
logger.info("Found model at %s", dk.data_path / dk.model_filename)
|
||||
elif not scanning:
|
||||
logger.info("Could not find model at %s", dh.data_path / dh.model_filename)
|
||||
logger.info("Could not find model at %s", dk.data_path / dk.model_filename)
|
||||
return file_exists
|
||||
|
||||
def set_full_path(self) -> None:
|
||||
|
|
@ -430,7 +433,7 @@ class IFreqaiModel(ABC):
|
|||
return dataframe[to_keep]
|
||||
|
||||
def train_model_in_series(self, new_trained_timerange: TimeRange, pair: str,
|
||||
strategy: IStrategy, dh: FreqaiDataKitchen,
|
||||
strategy: IStrategy, dk: FreqaiDataKitchen,
|
||||
data_load_timerange: TimeRange):
|
||||
"""
|
||||
Retreive data and train model in single threaded mode (only used if model directory is empty
|
||||
|
|
@ -439,41 +442,43 @@ class IFreqaiModel(ABC):
|
|||
new_trained_timerange: TimeRange = the timerange to train the model on
|
||||
metadata: dict = strategy provided metadata
|
||||
strategy: IStrategy = user defined strategy object
|
||||
dh: FreqaiDataKitchen = non-persistent data container for current coin/loop
|
||||
dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
|
||||
data_load_timerange: TimeRange = the amount of data to be loaded for populate_any_indicators
|
||||
(larger than new_trained_timerange so that new_trained_timerange does not contain any NaNs)
|
||||
"""
|
||||
|
||||
corr_dataframes, base_dataframes = dh.get_base_and_corr_dataframes(data_load_timerange,
|
||||
corr_dataframes, base_dataframes = dk.get_base_and_corr_dataframes(data_load_timerange,
|
||||
pair)
|
||||
|
||||
unfiltered_dataframe = dh.use_strategy_to_populate_indicators(strategy,
|
||||
unfiltered_dataframe = dk.use_strategy_to_populate_indicators(strategy,
|
||||
corr_dataframes,
|
||||
base_dataframes,
|
||||
pair)
|
||||
|
||||
unfiltered_dataframe = dh.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
|
||||
unfiltered_dataframe = dk.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
|
||||
|
||||
model = self.train(unfiltered_dataframe, pair, dh)
|
||||
# find the features indicated by strategy and store in datakitchen
|
||||
dk.find_features(unfiltered_dataframe)
|
||||
|
||||
self.data_drawer.pair_dict[pair][
|
||||
'trained_timestamp'] = new_trained_timerange.stopts
|
||||
dh.set_new_model_names(pair, new_trained_timerange)
|
||||
self.data_drawer.pair_dict[pair]['first'] = False
|
||||
if self.data_drawer.pair_dict[pair]['priority'] == 1 and self.scanning:
|
||||
model = self.train(unfiltered_dataframe, pair, dk)
|
||||
|
||||
self.dd.pair_dict[pair]['trained_timestamp'] = new_trained_timerange.stopts
|
||||
dk.set_new_model_names(pair, new_trained_timerange)
|
||||
self.dd.pair_dict[pair]['first'] = False
|
||||
if self.dd.pair_dict[pair]['priority'] == 1 and self.scanning:
|
||||
with self.lock:
|
||||
self.data_drawer.pair_to_end_of_training_queue(pair)
|
||||
dh.save_data(model, coin=pair, keras=self.keras)
|
||||
self.dd.pair_to_end_of_training_queue(pair)
|
||||
dk.save_data(model, coin=pair, keras_model=self.keras)
|
||||
|
||||
if self.freqai_info.get('purge_old_models', False):
|
||||
self.data_drawer.purge_old_models()
|
||||
self.dd.purge_old_models()
|
||||
# self.retrain = False
|
||||
|
||||
# Following methods which are overridden by user made prediction models.
|
||||
# See freqai/prediction_models/CatboostPredictionModlel.py for an example.
|
||||
|
||||
@abstractmethod
|
||||
def train(self, unfiltered_dataframe: DataFrame, pair: str, dh: FreqaiDataKitchen) -> Any:
|
||||
def train(self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen) -> Any:
|
||||
"""
|
||||
Filter the training data and train a model to it. Train makes heavy use of the datahandler
|
||||
for storing, saving, loading, and analyzing the data.
|
||||
|
|
@ -499,37 +504,36 @@ class IFreqaiModel(ABC):
|
|||
|
||||
@abstractmethod
|
||||
def predict(self, dataframe: DataFrame,
|
||||
dh: FreqaiDataKitchen, first: bool = True) -> Tuple[npt.ArrayLike, npt.ArrayLike]:
|
||||
dk: FreqaiDataKitchen, first: bool = True) -> Tuple[DataFrame, npt.ArrayLike]:
|
||||
"""
|
||||
Filter the prediction features data and predict with it.
|
||||
:param:
|
||||
unfiltered_dataframe: Full dataframe for the current backtest period.
|
||||
dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
|
||||
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
|
||||
:return:
|
||||
:predictions: np.array of predictions
|
||||
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
|
||||
data (NaNs) or felt uncertain about data (i.e. SVM and/or DI index)
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def make_labels(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame:
|
||||
def make_labels(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
|
||||
"""
|
||||
User defines the labels here (target values).
|
||||
:params:
|
||||
dataframe: DataFrame = the full dataframe for the present training period
|
||||
dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
|
||||
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
|
||||
"""
|
||||
|
||||
return
|
||||
|
||||
@abstractmethod
|
||||
def return_values(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame:
|
||||
def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
|
||||
"""
|
||||
User defines the dataframe to be returned to strategy here.
|
||||
:params:
|
||||
dataframe: DataFrame = the full dataframe for the current prediction (live)
|
||||
or --timerange (backtesting)
|
||||
dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
|
||||
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
|
||||
:returns:
|
||||
dataframe: DataFrame = dataframe filled with user defined data
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -18,18 +18,16 @@ class CatboostPredictionModel(IFreqaiModel):
|
|||
has its own DataHandler where data is held, saved, loaded, and managed.
|
||||
"""
|
||||
|
||||
def return_values(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame:
|
||||
|
||||
dataframe["prediction"] = dh.full_predictions
|
||||
dataframe["do_predict"] = dh.full_do_predict
|
||||
dataframe["target_mean"] = dh.full_target_mean
|
||||
dataframe["target_std"] = dh.full_target_std
|
||||
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
|
||||
dataframe["DI"] = dh.full_DI_values
|
||||
def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
|
||||
"""
|
||||
User uses this function to add any additional return values to the dataframe.
|
||||
e.g.
|
||||
dataframe['volatility'] = dk.volatility_values
|
||||
"""
|
||||
|
||||
return dataframe
|
||||
|
||||
def make_labels(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame:
|
||||
def make_labels(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
|
||||
"""
|
||||
User defines the labels here (target values).
|
||||
:params:
|
||||
|
|
@ -48,7 +46,7 @@ class CatboostPredictionModel(IFreqaiModel):
|
|||
return dataframe["s"]
|
||||
|
||||
def train(self, unfiltered_dataframe: DataFrame,
|
||||
pair: str, dh: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
|
||||
pair: str, dk: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
|
||||
"""
|
||||
Filter the training data and train a model to it. Train makes heavy use of the datahkitchen
|
||||
for storing, saving, loading, and analyzing the data.
|
||||
|
|
@ -62,27 +60,25 @@ class CatboostPredictionModel(IFreqaiModel):
|
|||
logger.info('--------------------Starting training '
|
||||
f'{pair} --------------------')
|
||||
|
||||
# create the full feature list based on user config info
|
||||
dh.training_features_list = dh.find_features(unfiltered_dataframe)
|
||||
unfiltered_labels = self.make_labels(unfiltered_dataframe, dh)
|
||||
# unfiltered_labels = self.make_labels(unfiltered_dataframe, dk)
|
||||
# filter the features requested by user in the configuration file and elegantly handle NaNs
|
||||
features_filtered, labels_filtered = dh.filter_features(
|
||||
features_filtered, labels_filtered = dk.filter_features(
|
||||
unfiltered_dataframe,
|
||||
dh.training_features_list,
|
||||
unfiltered_labels,
|
||||
dk.training_features_list,
|
||||
dk.label_list,
|
||||
training_filter=True,
|
||||
)
|
||||
|
||||
# split data into train/test data.
|
||||
data_dictionary = dh.make_train_test_datasets(features_filtered, labels_filtered)
|
||||
dh.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy
|
||||
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
|
||||
dk.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy
|
||||
# normalize all data based on train_dataset only
|
||||
data_dictionary = dh.normalize_data(data_dictionary)
|
||||
data_dictionary = dk.normalize_data(data_dictionary)
|
||||
|
||||
# optional additional data cleaning/analysis
|
||||
self.data_cleaning_train(dh)
|
||||
self.data_cleaning_train(dk)
|
||||
|
||||
logger.info(f'Training model on {len(dh.data_dictionary["train_features"].columns)}'
|
||||
logger.info(f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
|
||||
' features')
|
||||
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
|
||||
|
||||
|
|
@ -121,34 +117,32 @@ class CatboostPredictionModel(IFreqaiModel):
|
|||
return model
|
||||
|
||||
def predict(self, unfiltered_dataframe: DataFrame,
|
||||
dh: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
|
||||
dk: FreqaiDataKitchen, first: bool = False) -> Tuple[DataFrame, DataFrame]:
|
||||
"""
|
||||
Filter the prediction features data and predict with it.
|
||||
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
|
||||
:return:
|
||||
:predictions: np.array of predictions
|
||||
:pred_df: dataframe containing the predictions
|
||||
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
|
||||
data (NaNs) or felt uncertain about data (PCA and DI index)
|
||||
"""
|
||||
|
||||
# logger.info("--------------------Starting prediction--------------------")
|
||||
|
||||
original_feature_list = dh.find_features(unfiltered_dataframe)
|
||||
filtered_dataframe, _ = dh.filter_features(
|
||||
unfiltered_dataframe, original_feature_list, training_filter=False
|
||||
dk.find_features(unfiltered_dataframe)
|
||||
filtered_dataframe, _ = dk.filter_features(
|
||||
unfiltered_dataframe, dk.training_features_list, training_filter=False
|
||||
)
|
||||
filtered_dataframe = dh.normalize_data_from_metadata(filtered_dataframe)
|
||||
dh.data_dictionary["prediction_features"] = filtered_dataframe
|
||||
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
|
||||
dk.data_dictionary["prediction_features"] = filtered_dataframe
|
||||
|
||||
# optional additional data cleaning/analysis
|
||||
self.data_cleaning_predict(dh, filtered_dataframe)
|
||||
self.data_cleaning_predict(dk, filtered_dataframe)
|
||||
|
||||
predictions = self.model.predict(dh.data_dictionary["prediction_features"])
|
||||
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
|
||||
pred_df = DataFrame(predictions, columns=dk.label_list)
|
||||
|
||||
# compute the non-normalized predictions
|
||||
dh.predictions = (predictions + 1) * (dh.data["labels_max"] -
|
||||
dh.data["labels_min"]) / 2 + dh.data["labels_min"]
|
||||
for label in dk.label_list:
|
||||
pred_df[label] = ((pred_df[label] + 1) *
|
||||
(dk.data["labels_max"][label] -
|
||||
dk.data["labels_min"][label]) / 2) + dk.data["labels_min"][label]
|
||||
|
||||
# logger.info("--------------------Finished prediction--------------------")
|
||||
|
||||
return (dh.predictions, dh.do_predict)
|
||||
return (pred_df, dk.do_predict)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,126 @@
|
|||
import logging
|
||||
from typing import Any, Dict, Tuple
|
||||
|
||||
from catboost import CatBoostRegressor # , Pool
|
||||
from pandas import DataFrame
|
||||
from sklearn.multioutput import MultiOutputRegressor
|
||||
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from freqtrade.freqai.freqai_interface import IFreqaiModel
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class CatboostPredictionMultiModel(IFreqaiModel):
|
||||
"""
|
||||
User created prediction model. The class needs to override three necessary
|
||||
functions, predict(), train(), fit(). The class inherits ModelHandler which
|
||||
has its own DataHandler where data is held, saved, loaded, and managed.
|
||||
"""
|
||||
|
||||
def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
|
||||
"""
|
||||
User uses this function to add any additional return values to the dataframe.
|
||||
e.g.
|
||||
dataframe['volatility'] = dk.volatility_values
|
||||
"""
|
||||
|
||||
return dataframe
|
||||
|
||||
def train(self, unfiltered_dataframe: DataFrame,
|
||||
pair: str, dk: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
|
||||
"""
|
||||
Filter the training data and train a model to it. Train makes heavy use of the datahkitchen
|
||||
for storing, saving, loading, and analyzing the data.
|
||||
:params:
|
||||
:unfiltered_dataframe: Full dataframe for the current training period
|
||||
:metadata: pair metadata from strategy.
|
||||
:returns:
|
||||
:model: Trained model which can be used to inference (self.predict)
|
||||
"""
|
||||
|
||||
logger.info('--------------------Starting training '
|
||||
f'{pair} --------------------')
|
||||
|
||||
# unfiltered_labels = self.make_labels(unfiltered_dataframe, dk)
|
||||
# filter the features requested by user in the configuration file and elegantly handle NaNs
|
||||
features_filtered, labels_filtered = dk.filter_features(
|
||||
unfiltered_dataframe,
|
||||
dk.training_features_list,
|
||||
dk.label_list,
|
||||
training_filter=True,
|
||||
)
|
||||
|
||||
# split data into train/test data.
|
||||
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
|
||||
dk.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy
|
||||
# normalize all data based on train_dataset only
|
||||
data_dictionary = dk.normalize_data(data_dictionary)
|
||||
|
||||
# optional additional data cleaning/analysis
|
||||
self.data_cleaning_train(dk)
|
||||
|
||||
logger.info(f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
|
||||
' features')
|
||||
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
|
||||
|
||||
model = self.fit(data_dictionary)
|
||||
|
||||
logger.info(f'--------------------done training {pair}--------------------')
|
||||
|
||||
return model
|
||||
|
||||
def fit(self, data_dictionary: Dict) -> Any:
|
||||
"""
|
||||
User sets up the training and test data to fit their desired model here
|
||||
:params:
|
||||
:data_dictionary: the dictionary constructed by DataHandler to hold
|
||||
all the training and test data/labels.
|
||||
"""
|
||||
|
||||
cbr = CatBoostRegressor(
|
||||
allow_writing_files=False, gpu_ram_part=0.5,
|
||||
verbose=100, early_stopping_rounds=400, **self.model_training_parameters
|
||||
)
|
||||
|
||||
X = data_dictionary["train_features"]
|
||||
y = data_dictionary["train_labels"]
|
||||
# eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
|
||||
sample_weight = data_dictionary['train_weights']
|
||||
|
||||
model = MultiOutputRegressor(estimator=cbr)
|
||||
model.fit(X=X, y=y, sample_weight=sample_weight) # , eval_set=eval_set)
|
||||
|
||||
return model
|
||||
|
||||
def predict(self, unfiltered_dataframe: DataFrame,
|
||||
dk: FreqaiDataKitchen, first: bool = False) -> Tuple[DataFrame, DataFrame]:
|
||||
"""
|
||||
Filter the prediction features data and predict with it.
|
||||
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
|
||||
:return:
|
||||
:pred_df: dataframe containing the predictions
|
||||
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
|
||||
data (NaNs) or felt uncertain about data (PCA and DI index)
|
||||
"""
|
||||
|
||||
dk.find_features(unfiltered_dataframe)
|
||||
filtered_dataframe, _ = dk.filter_features(
|
||||
unfiltered_dataframe, dk.training_features_list, training_filter=False
|
||||
)
|
||||
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
|
||||
dk.data_dictionary["prediction_features"] = filtered_dataframe
|
||||
|
||||
# optional additional data cleaning/analysis
|
||||
self.data_cleaning_predict(dk, filtered_dataframe)
|
||||
|
||||
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
|
||||
pred_df = DataFrame(predictions, columns=dk.label_list)
|
||||
|
||||
for label in dk.label_list:
|
||||
pred_df[label] = ((pred_df[label] + 1) *
|
||||
(dk.data["labels_max"][label] -
|
||||
dk.data["labels_min"][label]) / 2) + dk.data["labels_min"][label]
|
||||
|
||||
return (pred_df, dk.do_predict)
|
||||
|
|
@ -18,37 +18,17 @@ class LightGBMPredictionModel(IFreqaiModel):
|
|||
has its own DataHandler where data is held, saved, loaded, and managed.
|
||||
"""
|
||||
|
||||
def return_values(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame:
|
||||
|
||||
dataframe["prediction"] = dh.full_predictions
|
||||
dataframe["do_predict"] = dh.full_do_predict
|
||||
dataframe["target_mean"] = dh.full_target_mean
|
||||
dataframe["target_std"] = dh.full_target_std
|
||||
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
|
||||
dataframe["DI"] = dh.full_DI_values
|
||||
def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
|
||||
"""
|
||||
User uses this function to add any additional return values to the dataframe.
|
||||
e.g.
|
||||
dataframe['volatility'] = dk.volatility_values
|
||||
"""
|
||||
|
||||
return dataframe
|
||||
|
||||
def make_labels(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame:
|
||||
"""
|
||||
User defines the labels here (target values).
|
||||
:params:
|
||||
:dataframe: the full dataframe for the present training period
|
||||
"""
|
||||
|
||||
dataframe["s"] = (
|
||||
dataframe["close"]
|
||||
.shift(-self.feature_parameters["period"])
|
||||
.rolling(self.feature_parameters["period"])
|
||||
.mean()
|
||||
/ dataframe["close"]
|
||||
- 1
|
||||
)
|
||||
|
||||
return dataframe["s"]
|
||||
|
||||
def train(self, unfiltered_dataframe: DataFrame,
|
||||
pair: str, dh: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
|
||||
pair: str, dk: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
|
||||
"""
|
||||
Filter the training data and train a model to it. Train makes heavy use of the datahkitchen
|
||||
for storing, saving, loading, and analyzing the data.
|
||||
|
|
@ -62,27 +42,25 @@ class LightGBMPredictionModel(IFreqaiModel):
|
|||
logger.info('--------------------Starting training '
|
||||
f'{pair} --------------------')
|
||||
|
||||
# create the full feature list based on user config info
|
||||
dh.training_features_list = dh.find_features(unfiltered_dataframe)
|
||||
unfiltered_labels = self.make_labels(unfiltered_dataframe, dh)
|
||||
# unfiltered_labels = self.make_labels(unfiltered_dataframe, dk)
|
||||
# filter the features requested by user in the configuration file and elegantly handle NaNs
|
||||
features_filtered, labels_filtered = dh.filter_features(
|
||||
features_filtered, labels_filtered = dk.filter_features(
|
||||
unfiltered_dataframe,
|
||||
dh.training_features_list,
|
||||
unfiltered_labels,
|
||||
dk.training_features_list,
|
||||
dk.label_list,
|
||||
training_filter=True,
|
||||
)
|
||||
|
||||
# split data into train/test data.
|
||||
data_dictionary = dh.make_train_test_datasets(features_filtered, labels_filtered)
|
||||
dh.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy
|
||||
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
|
||||
dk.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy
|
||||
# normalize all data based on train_dataset only
|
||||
data_dictionary = dh.normalize_data(data_dictionary)
|
||||
data_dictionary = dk.normalize_data(data_dictionary)
|
||||
|
||||
# optional additional data cleaning/analysis
|
||||
self.data_cleaning_train(dh)
|
||||
self.data_cleaning_train(dk)
|
||||
|
||||
logger.info(f'Training model on {len(dh.data_dictionary["train_features"].columns)}'
|
||||
logger.info(f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
|
||||
' features')
|
||||
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
|
||||
|
||||
|
|
@ -112,7 +90,7 @@ class LightGBMPredictionModel(IFreqaiModel):
|
|||
return model
|
||||
|
||||
def predict(self, unfiltered_dataframe: DataFrame,
|
||||
dh: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
|
||||
dk: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
|
||||
"""
|
||||
Filter the prediction features data and predict with it.
|
||||
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
|
||||
|
|
@ -124,22 +102,22 @@ class LightGBMPredictionModel(IFreqaiModel):
|
|||
|
||||
# logger.info("--------------------Starting prediction--------------------")
|
||||
|
||||
original_feature_list = dh.find_features(unfiltered_dataframe)
|
||||
filtered_dataframe, _ = dh.filter_features(
|
||||
original_feature_list = dk.find_features(unfiltered_dataframe)
|
||||
filtered_dataframe, _ = dk.filter_features(
|
||||
unfiltered_dataframe, original_feature_list, training_filter=False
|
||||
)
|
||||
filtered_dataframe = dh.normalize_data_from_metadata(filtered_dataframe)
|
||||
dh.data_dictionary["prediction_features"] = filtered_dataframe
|
||||
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
|
||||
dk.data_dictionary["prediction_features"] = filtered_dataframe
|
||||
|
||||
# optional additional data cleaning/analysis
|
||||
self.data_cleaning_predict(dh, filtered_dataframe)
|
||||
self.data_cleaning_predict(dk, filtered_dataframe)
|
||||
|
||||
predictions = self.model.predict(dh.data_dictionary["prediction_features"])
|
||||
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
|
||||
pred_df = DataFrame(predictions, columns=dk.label_list)
|
||||
|
||||
# compute the non-normalized predictions
|
||||
dh.predictions = (predictions + 1) * (dh.data["labels_max"] -
|
||||
dh.data["labels_min"]) / 2 + dh.data["labels_min"]
|
||||
for label in dk.label_list:
|
||||
pred_df[label] = ((pred_df[label] + 1) *
|
||||
(dk.data["labels_max"][label] -
|
||||
dk.data["labels_min"][label]) / 2) + dk.data["labels_min"][label]
|
||||
|
||||
# logger.info("--------------------Finished prediction--------------------")
|
||||
|
||||
return (dh.predictions, dh.do_predict)
|
||||
return (pred_df, dk.do_predict)
|
||||
|
|
|
|||
|
|
@ -156,6 +156,18 @@ class FreqaiExampleStrategy(IStrategy):
|
|||
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
|
||||
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
|
||||
|
||||
# user adds targets here by prepending them with &- (see convention below)
|
||||
# If user wishes to use multiple targets, a multioutput prediction model
|
||||
# needs to be used such as templates/CatboostPredictionMultiModel.py
|
||||
df['&-s_close'] = (
|
||||
df["close"]
|
||||
.shift(-self.freqai_info['feature_parameters']["period"])
|
||||
.rolling(self.freqai_info['feature_parameters']["period"])
|
||||
.mean()
|
||||
/ df["close"]
|
||||
- 1
|
||||
)
|
||||
|
||||
return df
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
|
@ -183,20 +195,20 @@ class FreqaiExampleStrategy(IStrategy):
|
|||
# each training period.
|
||||
dataframe = self.model.bridge.start(dataframe, metadata, self)
|
||||
|
||||
dataframe["target_roi"] = dataframe["target_mean"] + dataframe["target_std"] * 1.25
|
||||
dataframe["sell_roi"] = dataframe["target_mean"] - dataframe["target_std"] * 1.25
|
||||
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
|
||||
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
enter_long_conditions = [df["do_predict"] == 1, df["prediction"] > df["target_roi"]]
|
||||
enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]]
|
||||
|
||||
if enter_long_conditions:
|
||||
df.loc[
|
||||
reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
|
||||
] = (1, "long")
|
||||
|
||||
enter_short_conditions = [df["do_predict"] == 1, df["prediction"] < df["sell_roi"]]
|
||||
enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"]]
|
||||
|
||||
if enter_short_conditions:
|
||||
df.loc[
|
||||
|
|
@ -206,11 +218,11 @@ class FreqaiExampleStrategy(IStrategy):
|
|||
return df
|
||||
|
||||
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
exit_long_conditions = [df["do_predict"] == 1, df["prediction"] < df["sell_roi"] * 0.25]
|
||||
exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"] * 0.25]
|
||||
if exit_long_conditions:
|
||||
df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
|
||||
|
||||
exit_short_conditions = [df["do_predict"] == 1, df["prediction"] > df["target_roi"] * 0.25]
|
||||
exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"] * 0.25]
|
||||
if exit_short_conditions:
|
||||
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
|
||||
|
||||
|
|
@ -243,7 +255,7 @@ class FreqaiExampleStrategy(IStrategy):
|
|||
if ('prediction' + entry_tag not in pair_dict[pair] or
|
||||
pair_dict[pair]['prediction' + entry_tag] > 0):
|
||||
with self.model.bridge.lock:
|
||||
pair_dict[pair]['prediction' + entry_tag] = abs(trade_candle['prediction'])
|
||||
pair_dict[pair]['prediction' + entry_tag] = abs(trade_candle['&-s_close'])
|
||||
if not follow_mode:
|
||||
self.model.bridge.data_drawer.save_drawer_to_disk()
|
||||
else:
|
||||
|
|
|
|||
Loading…
Reference in New Issue