simplify freqAI start_backtesting
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@ -244,7 +244,8 @@ class IFreqaiModel(ABC):
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# following tr_train. Both of these windows slide through the
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# entire backtest
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for tr_train, tr_backtest in zip(dk.training_timeranges, dk.backtesting_timeranges):
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(_, _, _) = self.dd.get_pair_dict_info(metadata["pair"])
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pair = metadata["pair"]
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(_, _, _) = self.dd.get_pair_dict_info(pair)
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train_it += 1
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total_trains = len(dk.backtesting_timeranges)
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self.training_timerange = tr_train
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@ -266,12 +267,10 @@ class IFreqaiModel(ABC):
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trained_timestamp_int = int(trained_timestamp.stopts)
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dk.data_path = Path(
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dk.full_path
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/
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f"sub-train-{metadata['pair'].split('/')[0]}_{trained_timestamp_int}"
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dk.full_path / f"sub-train-{pair.split('/')[0]}_{trained_timestamp_int}"
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)
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dk.set_new_model_names(metadata["pair"], trained_timestamp)
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dk.set_new_model_names(pair, trained_timestamp)
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if dk.check_if_backtest_prediction_exists():
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append_df = dk.get_backtesting_prediction()
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@ -281,15 +280,15 @@ class IFreqaiModel(ABC):
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metadata["pair"], dk, trained_timestamp=trained_timestamp_int
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):
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dk.find_features(dataframe_train)
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self.model = self.train(dataframe_train, metadata["pair"], dk)
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self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = int(
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self.model = self.train(dataframe_train, pair, dk)
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self.dd.pair_dict[pair]["trained_timestamp"] = int(
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trained_timestamp.stopts)
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if self.save_backtest_models:
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logger.info('Saving backtest model to disk.')
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self.dd.save_data(self.model, metadata["pair"], dk)
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self.dd.save_data(self.model, pair, dk)
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else:
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self.model = self.dd.load_data(metadata["pair"], dk)
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self.model = self.dd.load_data(pair, dk)
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self.check_if_feature_list_matches_strategy(dataframe_train, dk)
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