remove trained_stake
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@ -62,10 +62,6 @@ class IFreqaiModel(ABC):
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print('going to train',len(self.dh.training_timeranges),
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'timeranges:',self.dh.training_timeranges)
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predictions = np.array([])
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do_predict = np.array([])
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target_mean = np.array([])
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target_std = np.array([])
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# Loop enforcing the sliding window training/backtesting paragigm
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# tr_train is the training time range e.g. 1 historical month
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@ -149,7 +145,7 @@ class IFreqaiModel(ABC):
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:param path: path to model
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"""
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coin,_ = pair.split('/')
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self.dh.model_filename = f"cb_"+coin.lower()+"_"+self.freqai_info['trained_stake']+"_"+training_timerange
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self.dh.model_filename = f"cb_"+coin.lower()+"_"+training_timerange
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file_exists = os.path.isfile(self.dh.model_path+
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self.dh.model_filename+"_model.joblib")
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if file_exists:
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