improve wording, move warning
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@ -1,5 +1,8 @@
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# Using the `spice_rack`
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# Using the `spice_rack`
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!!! Note:
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`spice_rack` indicators should not be used exclusively for entries and exits, the following example is just a demonstration of syntax. `spice_rack` indicators should **always** be used to support existing strategies).
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The `spice_rack` is aimed at users who do not wish to deal with setting up `FreqAI` confgs, but instead prefer to interact with `FreqAI` similar to a `talib` indicator. In this case, the user can instead simply add two keys to their config:
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The `spice_rack` is aimed at users who do not wish to deal with setting up `FreqAI` confgs, but instead prefer to interact with `FreqAI` similar to a `talib` indicator. In this case, the user can instead simply add two keys to their config:
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```json
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```json
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@ -17,7 +20,7 @@ Which tells `FreqAI` to set up a pre-set `FreqAI` instance automatically under t
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self.freqai.close_spice_rack() # user must close the spicerack
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self.freqai.close_spice_rack() # user must close the spicerack
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```
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```
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Users can then use these columns, concert with all their own additional indicators added to `populate_indicators` in their entry/exit criteria and strategy callback methods the same way as any typical indicator (note: `spice_rack` indicators should not be used exclusively for entries and exits, the following example is just a demonstration of syntax. `spice_rack` indicators should **always** be used to support existing strategies). For example:
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Users can then use these columns, concert with all their own additional indicators added to `populate_indicators` in their entry/exit criteria and strategy callback methods the same way as any typical indicator. For example:
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```python
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```python
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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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@ -25,14 +28,14 @@ Users can then use these columns, concert with all their own additional indicato
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df.loc[
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df.loc[
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(
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(
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(df['dissimilarity_index'] < 1) &
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(df['dissimilarity_index'] < 1) &
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(df['extrema'] > 0.1)
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(df['extrema'] < -0.1)
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),
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),
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'enter_long'] = 1
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'enter_long'] = 1
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df.loc[
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df.loc[
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(
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(
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(df['dissimilarity_index'] < 1) &
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(df['dissimilarity_index'] < 1) &
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(df['extrema'] <> -0.1)
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(df['extrema'] > 0.1)
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),
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),
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'enter_short'] = 1
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'enter_short'] = 1
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