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add docstrings to new functions, remove superceded code

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This commit is contained in:
robcaulk 2022-06-17 16:16:23 +02:00
parent 5e914d5756
commit 6da7a98857
2 changed files with 15 additions and 18 deletions
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8
freqtrade/freqai/data_kitchen.py
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@ -726,6 +726,14 @@ class FreqaiDataKitchen:
return full_timerange return full_timerange
def check_if_model_expired(self, trained_timestamp: int) -> bool: def check_if_model_expired(self, trained_timestamp: int) -> bool:
"""
A model age checker to determine if the model is trustworthy based on user defined
`expiration_hours` in the configuration file.
:params:
trained_timestamp: int = The time of training for the most recent model.
:returns:
bool = If the model is expired or not.
"""
time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp() time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
elapsed_time = (time - trained_timestamp) / 3600 # hours elapsed_time = (time - trained_timestamp) / 3600 # hours
max_time = self.freqai_config.get('expiration_hours', 0) max_time = self.freqai_config.get('expiration_hours', 0)

25
freqtrade/freqai/freqai_interface.py
View File

@ -113,6 +113,13 @@ class IFreqaiModel(ABC):
@threaded @threaded
def start_scanning(self, strategy: IStrategy) -> None: 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.
:params:
strategy: IStrategy = The user defined strategy class
"""
while 1: while 1:
for pair in self.config.get('exchange', {}).get('pair_whitelist'): for pair in self.config.get('exchange', {}).get('pair_whitelist'):
if self.data_drawer.pair_dict[pair]['priority'] != 1: if self.data_drawer.pair_dict[pair]['priority'] != 1:
@ -247,12 +254,6 @@ class IFreqaiModel(ABC):
# then save model and metadata. # then save model and metadata.
# if not trainable, load existing data # if not trainable, load existing data
if not self.follow_mode: if not self.follow_mode:
# if trained_timestamp != 0: # historical model available
# dh.set_paths(metadata['pair'], trained_timestamp)
# # file_exists = self.model_exists(metadata['pair'],
# # dh,
# # trained_timestamp=trained_timestamp,
# # model_filename=model_filename)
(_, (_,
new_trained_timerange, new_trained_timerange,
@ -271,18 +272,6 @@ class IFreqaiModel(ABC):
self.scanning = True self.scanning = True
self.start_scanning(strategy) self.start_scanning(strategy)
# train the model on the trained timerange
# if coin_first and not self.scanning:
# self.train_model_in_series(new_trained_timerange, metadata['pair'],
# strategy, dh, data_load_timerange)
# elif not coin_first and not self.scanning:
# self.scanning = True
# self.start_scanning(strategy)
# elif not trainable and not self.follow_mode:
# logger.info(f'{metadata["pair"]} holds spot '
# f'{self.data_drawer.pair_dict[metadata["pair"]]["priority"]} '
# 'in training queue')
elif self.follow_mode: elif self.follow_mode:
dh.set_paths(metadata, trained_timestamp) dh.set_paths(metadata, trained_timestamp)
logger.info('FreqAI instance set to follow_mode, finding existing pair' logger.info('FreqAI instance set to follow_mode, finding existing pair'