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catch errors occuring on background thread, and make sure to keep the ball rolling. Improve pair retraining queue.

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This commit is contained in:
robcaulk
2022-05-28 18:26:19 +02:00
parent e54614fa2f
commit 83dd453723
3 changed files with 31 additions and 22 deletions
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29
freqtrade/freqai/freqai_interface.py
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@@ -85,7 +85,7 @@ class IFreqaiModel(ABC):
# determine what the current pair will do
if self.live:
if (not self.training_on_separate_thread and
self.data_drawer.training_queue[metadata['pair']] == 1):
self.data_drawer.pair_dict[metadata['pair']]['priority'] == 1):
self.dh = FreqaiDataKitchen(self.config, self.data_drawer,
self.live, metadata["pair"])
@@ -313,13 +313,22 @@ class IFreqaiModel(ABC):
strategy: IStrategy, dh: FreqaiDataKitchen):
# with nostdout():
dh.download_new_data_for_retraining(new_trained_timerange, metadata)
dh.download_new_data_for_retraining(new_trained_timerange, metadata, strategy)
corr_dataframes, base_dataframes = dh.load_pairs_histories(new_trained_timerange,
metadata)
unfiltered_dataframe = dh.use_strategy_to_populate_indicators(strategy,
corr_dataframes,
base_dataframes,
metadata)
# protecting from common benign errors associated with grabbing new data from exchange:
try:
unfiltered_dataframe = dh.use_strategy_to_populate_indicators(strategy,
corr_dataframes,
base_dataframes,
metadata)
except Exception:
logger.warning('Mismatched sizes encountered in strategy')
self.data_drawer.pair_to_end_of_training_queue(metadata['pair'])
self.training_on_separate_thread = False
self.retrain = False
return
try:
model = self.train(unfiltered_dataframe, metadata, dh)
@@ -333,8 +342,8 @@ class IFreqaiModel(ABC):
self.data_drawer.pair_dict[metadata['pair']][
'trained_timestamp'] = new_trained_timerange.stopts
dh.set_new_model_names(metadata, new_trained_timerange)
logger.info('Training queue'
f'{sorted(self.data_drawer.training_queue.items(), key=lambda item: item[1])}')
# logger.info('Training queue'
# f'{sorted(self.data_drawer.pair_dict.items(), key=lambda item: item[1])}')
dh.save_data(model, coin=metadata['pair'])
self.data_drawer.pair_to_end_of_training_queue(metadata['pair'])
@@ -345,7 +354,7 @@ class IFreqaiModel(ABC):
def train_model_in_series(self, new_trained_timerange: TimeRange, metadata: dict,
strategy: IStrategy, dh: FreqaiDataKitchen):
dh.download_new_data_for_retraining(new_trained_timerange, metadata)
dh.download_new_data_for_retraining(new_trained_timerange, metadata, strategy)
corr_dataframes, base_dataframes = dh.load_pairs_histories(new_trained_timerange,
metadata)
@@ -363,7 +372,7 @@ class IFreqaiModel(ABC):
dh.save_data(model, coin=metadata['pair'])
self.retrain = False
# Methods which are overridden by user made prediction models.
# Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModlel.py for an example.
@abstractmethod