Add typehint to corr_pairlist
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@ -2,7 +2,7 @@
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## Defining the features
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Low level feature engineering is performed in the user strategy within a function called `populate_any_indicators()`. That function sets the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. One important syntax rule is that all `base features` string names are prepended with `%-{pair}`, while labels/targets are prepended with `&`.
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Low level feature engineering is performed in the user strategy within a function called `populate_any_indicators()`. That function sets the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. One important syntax rule is that all `base features` string names are prepended with `%-{pair}`, while labels/targets are prepended with `&`.
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!!! Note
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Adding the full pair string, e.g. XYZ/USD, in the feature name enables improved performance for dataframe caching on the backend. If users elect *not* to add the full pair string in the feature string, FreqAI will operate in a reduced performance mode.
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@ -72,7 +72,7 @@ class IFreqaiModel(ABC):
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self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
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self.scanning = False
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self.ft_params = self.freqai_info["feature_parameters"]
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self.corr_pairlist = self.ft_params.get("include_corr_pairlist", [])
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self.corr_pairlist: List[str] = self.ft_params.get("include_corr_pairlist", [])
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self.keras: bool = self.freqai_info.get("keras", False)
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if self.keras and self.ft_params.get("DI_threshold", 0):
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self.ft_params["DI_threshold"] = 0
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