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add counter to backtesting log so users know how many more pairs and how many more models will need to be trained

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This commit is contained in:
robcaulk 2022-07-21 13:22:12 +02:00
parent 183dec866a
commit 8033e0bf23
1 changed files with 11 additions and 2 deletions
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13
freqtrade/freqai/freqai_interface.py
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@ -73,6 +73,8 @@ class IFreqaiModel(ABC):
self.freqai_info["feature_parameters"]["DI_threshold"] = 0 self.freqai_info["feature_parameters"]["DI_threshold"] = 0
logger.warning("DI threshold is not configured for Keras models yet. Deactivating.") logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
self.CONV_WIDTH = self.freqai_info.get("conv_width", 2) self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
self.pair_it = 0
self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
def assert_config(self, config: Dict[str, Any]) -> None: def assert_config(self, config: Dict[str, Any]) -> None:
@ -164,6 +166,8 @@ class IFreqaiModel(ABC):
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
""" """
self.pair_it += 1
train_it = 0
# Loop enforcing the sliding window training/backtesting paradigm # Loop enforcing the sliding window training/backtesting paradigm
# tr_train is the training time range e.g. 1 historical month # tr_train is the training time range e.g. 1 historical month
# tr_backtest is the backtesting time range e.g. the week directly # tr_backtest is the backtesting time range e.g. the week directly
@ -171,6 +175,8 @@ class IFreqaiModel(ABC):
# entire backtest # entire backtest
for tr_train, tr_backtest in zip(dk.training_timeranges, dk.backtesting_timeranges): for tr_train, tr_backtest in zip(dk.training_timeranges, dk.backtesting_timeranges):
(_, _, _, _) = self.dd.get_pair_dict_info(metadata["pair"]) (_, _, _, _) = self.dd.get_pair_dict_info(metadata["pair"])
train_it += 1
total_trains = len(dk.backtesting_timeranges)
gc.collect() gc.collect()
dk.data = {} # clean the pair specific data between training window sliding dk.data = {} # clean the pair specific data between training window sliding
self.training_timerange = tr_train self.training_timerange = tr_train
@ -184,8 +190,11 @@ class IFreqaiModel(ABC):
tr_train_stopts_str = datetime.datetime.utcfromtimestamp(tr_train.stopts).strftime( tr_train_stopts_str = datetime.datetime.utcfromtimestamp(tr_train.stopts).strftime(
"%Y-%m-%d %H:%M:%S" "%Y-%m-%d %H:%M:%S"
) )
logger.info(f"Training {metadata['pair']}") logger.info(
logger.info(f" from {tr_train_startts_str} to {tr_train_stopts_str}") f"Training {metadata['pair']}, {self.pair_it}/{self.total_pairs} pairs"
f" from {tr_train_startts_str} to {tr_train_stopts_str}, {train_it}/{total_trains} "
"trains"
)
dk.data_path = Path( dk.data_path = Path(
dk.full_path dk.full_path