adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

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

Browse Source
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
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

18
freqtrade/freqai/data_drawer.py
View File

@ -28,7 +28,7 @@ class FreqaiDataDrawer:
self.full_path = full_path
self.load_drawer_from_disk()
self.training_queue: Dict[str, int] = {}
self.create_training_queue(pair_whitelist)
# self.create_training_queue(pair_whitelist)
def load_drawer_from_disk(self):
exists = Path(self.full_path / str('pair_dictionary.json')).resolve().exists()
@ -58,7 +58,6 @@ class FreqaiDataDrawer:
model_filename = self.pair_dict[metadata['pair']]['model_filename'] = ''
coin_first = self.pair_dict[metadata['pair']]['first'] = True
trained_timestamp = self.pair_dict[metadata['pair']]['trained_timestamp'] = 0
self.pair_dict[metadata['pair']]['priority'] = 1
return model_filename, trained_timestamp, coin_first
@ -71,17 +70,16 @@ class FreqaiDataDrawer:
self.pair_dict[metadata['pair']]['model_filename'] = ''
self.pair_dict[metadata['pair']]['first'] = True
self.pair_dict[metadata['pair']]['trained_timestamp'] = 0
self.pair_dict[metadata['pair']]['priority'] = 1
self.pair_dict[metadata['pair']]['priority'] = len(self.pair_dict)
return
def create_training_queue(self, pairs: list) -> None:
for i, pair in enumerate(pairs):
self.training_queue[pair] = i + 1
# def create_training_queue(self, pairs: list) -> None:
# for i, pair in enumerate(pairs):
# self.training_queue[pair] = i + 1
def pair_to_end_of_training_queue(self, pair: str) -> None:
# march all pairs up in the queue
for p in self.training_queue:
self.training_queue[p] -= 1
for p in self.pair_dict:
self.pair_dict[p]['priority'] -= 1
# send pair to end of queue
self.training_queue[pair] = len(self.training_queue)
self.pair_dict[pair]['priority'] = len(self.pair_dict)

6
freqtrade/freqai/data_kitchen.py
View File

@ -717,10 +717,12 @@ class FreqaiDataKitchen:
# enables persistence, but not fully implemented into save/load data yer
# self.data['live_trained_timerange'] = str(int(trained_timerange.stopts))
def download_new_data_for_retraining(self, timerange: TimeRange, metadata: dict) -> None:
def download_new_data_for_retraining(self, timerange: TimeRange, metadata: dict,
strategy: IStrategy) -> None:
exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'],
self.config, validate=False, freqai=True)
# exchange = strategy.dp._exchange # closes ccxt session
pairs = copy.deepcopy(self.freqai_config.get('corr_pairlist', []))
if str(metadata['pair']) not in pairs:
pairs.append(str(metadata['pair']))
@ -766,7 +768,7 @@ class FreqaiDataKitchen:
base_dataframes: dict,
metadata: dict) -> DataFrame:
dataframe = base_dataframes[self.config['timeframe']]
dataframe = base_dataframes[self.config['timeframe']].copy()
pairs = self.freqai_config.get("corr_pairlist", [])
for tf in self.freqai_config.get("timeframes"):

21
freqtrade/freqai/freqai_interface.py
View File

@ -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)
# 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