Rename indicator_space to buy_indicator_space
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@ -677,7 +677,7 @@ If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace f
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These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the timeframe used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the timeframe used.
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If you have the `generate_roi_table()` and `roi_space()` methods in your custom hyperopt file, remove them in order to utilize these adaptive ROI tables and the ROI hyperoptimization space generated by Freqtrade by default.
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If you have the `generate_roi_table()` and `roi_space()` methods in your custom hyperopt, remove them in order to utilize these adaptive ROI tables and the ROI hyperoptimization space generated by Freqtrade by default.
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Override the `roi_space()` method if you need components of the ROI tables to vary in other ranges. Override the `generate_roi_table()` and `roi_space()` methods and implement your own custom approach for generation of the ROI tables during hyperoptimization if you need a different structure of the ROI tables or other amount of rows (steps).
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@ -241,7 +241,7 @@ class Hyperopt:
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if HyperoptTools.has_space(self.config, 'buy'):
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logger.debug("Hyperopt has 'buy' space")
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self.buy_space = self.custom_hyperopt.indicator_space()
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self.buy_space = self.custom_hyperopt.buy_indicator_space()
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if HyperoptTools.has_space(self.config, 'sell'):
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logger.debug("Hyperopt has 'sell' space")
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@ -56,7 +56,7 @@ class HyperOptAuto(IHyperOpt):
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else:
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_format_exception_message(category)
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def indicator_space(self) -> List['Dimension']:
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def buy_indicator_space(self) -> List['Dimension']:
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return self._get_indicator_space('buy')
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def sell_indicator_space(self) -> List['Dimension']:
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