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Add follow_mode feature so that secondary bots can be launched with the same identifier and load models trained by the leader

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This commit is contained in:
robcaulk
2022-05-30 21:35:48 +02:00
parent 5b4c649d43
commit 606f18e5c1
5 changed files with 99 additions and 16 deletions
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25
freqtrade/freqai/freqai_interface.py
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@@ -54,9 +54,13 @@ class IFreqaiModel(ABC):
self.retrain = False
self.first = True
self.set_full_path()
self.follow_mode = self.freqai_info.get('follow_mode', False)
self.data_drawer = FreqaiDataDrawer(Path(self.full_path),
self.config['exchange']['pair_whitelist'])
self.config['exchange']['pair_whitelist'],
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')
def assert_config(self, config: Dict[str, Any]) -> None:
@@ -105,7 +109,7 @@ class IFreqaiModel(ABC):
# (backtest window, i.e. window immediately following the training window).
# FreqAI slides the window and sequentially builds the backtesting results before returning
# the concatenated results for the full backtesting period back to the strategy.
else:
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)
@@ -138,7 +142,7 @@ class IFreqaiModel(ABC):
for tr_train, tr_backtest in zip(
dh.training_timeranges, dh.backtesting_timeranges
):
(_, _, _) = self.data_drawer.get_pair_dict_info(metadata)
(_, _, _, _) = self.data_drawer.get_pair_dict_info(metadata)
gc.collect()
dh.data = {} # clean the pair specific data between training window sliding
self.training_timerange = tr_train
@@ -188,9 +192,15 @@ class IFreqaiModel(ABC):
(model_filename,
trained_timestamp,
coin_first) = self.data_drawer.get_pair_dict_info(metadata)
coin_first,
return_null_array) = self.data_drawer.get_pair_dict_info(metadata)
if (not self.training_on_separate_thread):
# if the files do not yet exist, the follower returns null arrays to strategy
if self.follow_mode and return_null_array:
self.data_drawer.return_null_values_to_strategy(dataframe, dh)
return dh
if (not self.training_on_separate_thread and not self.follow_mode):
file_exists = False
if trained_timestamp != 0: # historical model available
@@ -212,8 +222,11 @@ class IFreqaiModel(ABC):
self.retrain_model_on_separate_thread(new_trained_timerange,
metadata, strategy, dh)
else:
elif self.training_on_separate_thread and not self.follow_mode:
logger.info("FreqAI training a new model on background thread.")
elif self.follow_mode:
logger.info('FreqAI instance set to follow_mode, finding existing pair'
f'using { self.identifier }')
self.model = dh.load_data(coin=metadata['pair'])