another attempt at fixing datalength bug
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@ -39,6 +39,10 @@ class FreqaiDataKitchen:
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self.do_predict = np.array([])
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self.target_mean = np.array([])
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self.target_std = np.array([])
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self.full_predictions = np.array([])
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self.full_do_predict = np.array([])
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self.full_target_mean = np.array([])
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self.full_target_std = np.array([])
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self.model_path = Path()
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self.model_filename = ""
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@ -313,12 +317,11 @@ class FreqaiDataKitchen:
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timerange_backtest.startts = timerange_train.stopts
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timerange_backtest.stopts = timerange_backtest.startts + bt_period
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if timerange_backtest.stopts > config_timerange.stopts:
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timerange_backtest.stopts = config_timerange.stopts
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else:
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timerange_backtest.stopts = timerange_backtest.startts + bt_period
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start = datetime.datetime.utcfromtimestamp(timerange_backtest.startts)
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stop = datetime.datetime.utcfromtimestamp(timerange_backtest.stopts)
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tr_backtesting_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d"))
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@ -328,7 +331,7 @@ class FreqaiDataKitchen:
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if timerange_backtest.stopts == config_timerange.stopts:
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break
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# print(tr_training_list, tr_backtesting_list)
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print(tr_training_list, tr_backtesting_list)
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return tr_training_list, tr_backtesting_list
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def slice_dataframe(self, tr: str, df: DataFrame) -> DataFrame:
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@ -536,10 +539,10 @@ class FreqaiDataKitchen:
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ones = np.ones(len_dataframe)
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s_mean, s_std = ones * self.data["s_mean"], ones * self.data["s_std"]
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self.predictions = np.append(self.predictions, predictions)
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self.do_predict = np.append(self.do_predict, do_predict)
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self.target_mean = np.append(self.target_mean, s_mean)
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self.target_std = np.append(self.target_std, s_std)
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self.full_predictions = np.append(self.full_predictions, predictions)
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self.full_do_predict = np.append(self.full_do_predict, do_predict)
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self.full_target_mean = np.append(self.full_target_mean, s_mean)
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self.full_target_std = np.append(self.full_target_std, s_std)
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return
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@ -549,11 +552,11 @@ class FreqaiDataKitchen:
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when it goes back to the strategy. These rows are not included in the backtest.
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"""
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filler = np.zeros(len_dataframe - len(self.predictions)) # startup_candle_count
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self.predictions = np.append(filler, self.predictions)
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self.do_predict = np.append(filler, self.do_predict)
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self.target_mean = np.append(filler, self.target_mean)
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self.target_std = np.append(filler, self.target_std)
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filler = np.zeros(len_dataframe - len(self.full_predictions)) # startup_candle_count
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self.full_predictions = np.append(filler, self.full_predictions)
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self.full_do_predict = np.append(filler, self.full_do_predict)
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self.full_target_mean = np.append(filler, self.full_target_mean)
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self.full_target_std = np.append(filler, self.full_target_std)
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return
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@ -84,10 +84,13 @@ class IFreqaiModel(ABC):
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preds, do_preds = self.predict(dataframe_backtest)
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self.dh.append_predictions(preds, do_preds, len(dataframe_backtest))
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print('predictions', len(self.dh.full_predictions),
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'do_predict', len(self.dh.full_do_predict))
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self.dh.fill_predictions(len(dataframe))
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return self.dh.predictions, self.dh.do_predict, self.dh.target_mean, self.dh.target_std
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return (self.dh.full_predictions, self.dh.full_do_predict,
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self.dh.full_target_mean, self.dh.full_target_std)
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def make_labels(self, dataframe: DataFrame) -> DataFrame:
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"""
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