ensure RL works with new naming scheme
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@ -280,22 +280,26 @@ class BaseReinforcementLearningModel(IFreqaiModel):
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train_df = data_dictionary["train_features"]
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train_df = data_dictionary["train_features"]
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test_df = data_dictionary["test_features"]
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test_df = data_dictionary["test_features"]
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# %-raw_volume_gen_shift-2_ETH/USDT_1h
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# price data for model training and evaluation
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# price data for model training and evaluation
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tf = self.config['timeframe']
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tf = self.config['timeframe']
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ohlc_list = [f'%-{pair}raw_open_{tf}', f'%-{pair}raw_low_{tf}',
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ohlc_list = [f'%-raw_open_gen_{pair}_{tf}', f'%-raw_low_gen_{pair}_{tf}',
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f'%-{pair}raw_high_{tf}', f'%-{pair}raw_close_{tf}']
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f'%-raw_high_gen_{pair}_{tf}', f'%-raw_close_gen_{pair}_{tf}']
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rename_dict = {f'%-{pair}raw_open_{tf}': 'open', f'%-{pair}raw_low_{tf}': 'low',
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rename_dict = {f'%-raw_open_gen_{pair}_{tf}': 'open',
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f'%-{pair}raw_high_{tf}': ' high', f'%-{pair}raw_close_{tf}': 'close'}
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f'%-raw_low_gen_{pair}_{tf}': 'low',
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f'%-raw_high_gen_{pair}_{tf}': ' high',
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f'%-raw_close_gen_{pair}_{tf}': 'close'}
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prices_train = train_df.filter(ohlc_list, axis=1)
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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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if prices_train.empty:
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raise OperationalException('Reinforcement learning module didnt find the raw prices '
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raise OperationalException('Reinforcement learning module didnt find the raw prices '
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'assigned in feature_engineering_standard(). '
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'assigned in feature_engineering_standard(). '
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'Please assign them with:\n'
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'Please assign them with:\n'
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'informative[f"%-{pair}raw_close"] = informative["close"]\n'
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'dataframe["%-raw_close"] = dataframe["close"]\n'
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'informative[f"%-{pair}raw_open"] = informative["open"]\n'
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'dataframe["%-raw_open"] = dataframe["open"]\n'
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'informative[f"%-{pair}raw_high"] = informative["high"]\n'
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'dataframe["%-raw_high"] = dataframe["high"]\n'
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'informative[f"%-{pair}raw_low"] = informative["low"]\n')
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'dataframe["%-raw_low"] = dataframe["low"]\n'
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'inside `feature_engineering_expand_basic()`')
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prices_train.rename(columns=rename_dict, inplace=True)
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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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prices_train.reset_index(drop=True)
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@ -90,7 +90,7 @@ def test_use_SVM_to_remove_outliers_and_outlier_protection(mocker, freqai_conf,
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freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 0.1})
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freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 0.1})
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freqai.dk.use_SVM_to_remove_outliers(predict=False)
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freqai.dk.use_SVM_to_remove_outliers(predict=False)
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assert log_has_re(
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assert log_has_re(
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"SVM detected 7.36%",
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"SVM detected 7.83%",
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caplog,
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caplog,
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)
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)
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