ensure normalization acceleration methods are employed in RL
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@@ -253,18 +253,26 @@ class BaseReinforcementLearningModel(IFreqaiModel):
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Builds the train prices and test prices for the environment.
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"""
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coin = pair.split('/')[0]
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pair = pair.replace(':', '')
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train_df = data_dictionary["train_features"]
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test_df = data_dictionary["test_features"]
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# price data for model training and evaluation
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tf = self.config['timeframe']
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ohlc_list = [f'%-{coin}raw_open_{tf}', f'%-{coin}raw_low_{tf}',
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f'%-{coin}raw_high_{tf}', f'%-{coin}raw_close_{tf}']
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rename_dict = {f'%-{coin}raw_open_{tf}': 'open', f'%-{coin}raw_low_{tf}': 'low',
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f'%-{coin}raw_high_{tf}': ' high', f'%-{coin}raw_close_{tf}': 'close'}
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ohlc_list = [f'%-{pair}raw_open_{tf}', f'%-{pair}raw_low_{tf}',
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f'%-{pair}raw_high_{tf}', f'%-{pair}raw_close_{tf}']
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rename_dict = {f'%-{pair}raw_open_{tf}': 'open', f'%-{pair}raw_low_{tf}': 'low',
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f'%-{pair}raw_high_{tf}': ' high', f'%-{pair}raw_close_{tf}': 'close'}
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prices_train = train_df.filter(ohlc_list, axis=1)
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if prices_train.empty:
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raise OperationalException('Reinforcement learning module didnt find the raw prices '
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'assigned in populate_any_indicators. Please assign them '
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'with:\n'
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'informative[f"%-{pair}raw_close"] = informative["close"]\n'
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'informative[f"%-{pair}raw_open"] = informative["open"]\n'
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'informative[f"%-{pair}raw_high"] = informative["high"]\n'
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'informative[f"%-{pair}raw_low"] = informative["low"]\n')
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prices_train.rename(columns=rename_dict, inplace=True)
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prices_train.reset_index(drop=True)
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