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detect if upper tf candles are new or not, append if so. Correct the epoch for candle update check

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robcaulk
2022-06-07 19:49:20 +02:00
parent cab8f517b4
commit 15d049cffe
3 changed files with 44 additions and 36 deletions
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15
freqtrade/freqai/freqai_interface.py
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@@ -216,12 +216,9 @@ class IFreqaiModel(ABC):
# append the historic data once per round
if (self.data_drawer.historic_data and
self.update_historic_data >= len(self.config.get('exchange', '')
.get('pair_whitelist'))):
self.config.get('exchange', '').get('pair_whitelist').index(metadata['pair']) == 1):
dh.update_historic_data(strategy)
self.update_historic_data = 1
else:
self.update_historic_data += 1
logger.info(f'Updating historic data on pair {metadata["pair"]}')
# if trainable, check if model needs training, if so compute new timerange,
# then save model and metadata.
@@ -405,9 +402,9 @@ class IFreqaiModel(ABC):
# dh.download_new_data_for_retraining(data_load_timerange, metadata, strategy)
# corr_dataframes, base_dataframes = dh.load_pairs_histories(data_load_timerange,
# metadata)
with self.data_drawer.history_lock:
corr_dataframes, base_dataframes = dh.get_base_and_corr_dataframes(data_load_timerange,
metadata)
corr_dataframes, base_dataframes = dh.get_base_and_corr_dataframes(data_load_timerange,
metadata)
# protecting from common benign errors associated with grabbing new data from exchange:
try:
@@ -419,7 +416,6 @@ class IFreqaiModel(ABC):
except Exception as err:
logger.exception(err)
# self.data_drawer.pair_to_end_of_training_queue(metadata['pair'])
self.training_on_separate_thread = False
self.retrain = False
return
@@ -428,7 +424,6 @@ class IFreqaiModel(ABC):
model = self.train(unfiltered_dataframe, metadata, dh)
except ValueError:
logger.warning('Value error encountered during training')
# self.data_drawer.pair_to_end_of_training_queue(metadata['pair'])
self.training_on_separate_thread = False
self.retrain = False
return