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72 lines
2.8 KiB
Markdown
72 lines
2.8 KiB
Markdown
# 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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```json
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"freqai_spice_rack": true,
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"freqai_identifier": "spicey-id",
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```
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Which tells `FreqAI` to set up a pre-set `FreqAI` instance automatically under the hood with preset parameters. Now the user can access a suite of custom `FreqAI` supercharged indicators inside their strategy by placing the following code into `populate_indicators`:
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```python
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dataframe['dissimilarity_index'] = self.freqai.spice_rack(
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'DI_values', dataframe, metadata, self)
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dataframe['extrema'] = self.freqai.spice_rack(
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'&s-extrema', dataframe, metadata, self)
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self.freqai.close_spice_rack() # user must close the spicerack
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```
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Users can then use these columns in 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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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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df.loc[
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(
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(df['dissimilarity_index'] < 1) &
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(df['extrema'] < -0.1)
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),
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'enter_long'] = 1
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df.loc[
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(
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(df['dissimilarity_index'] < 1) &
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(df['extrema'] > 0.1)
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),
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'enter_short'] = 1
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return df
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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df.loc[
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(
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(df['dissimilarity_index'] < 1) &
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(df['extrema'] > 0.1)
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),
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'exit_long'] = 1
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df.loc[
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(
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(df['dissimilarity_index'] < 1) &
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(df['extrema'] < -0.1)
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),
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'exit_short'] = 1
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return df
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```
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## Available indicators
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| Parameter | Description |
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|------------|-------------|
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| `DI_values` | **Required.** <br> The dissimilarity index of the current candle to the recent candles. More information available [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di) <br> **Datatype:** Floats.
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| `extrema` | **Required.** <br> A continuous prediction from FreqAI which aims to help predict if the current candle is a maxima or a minma. FreqAI aims for 1 to be a maxima and -1 to be a minima - but the values should typically hover between -0.2 and 0.2. <br> **Datatype:** Floats.
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