improve performance and documentation of spice-rack.

2022-10-08 12:45:49 +02:00
parent d362332527
commit 06f4f2db0a
5 changed files with 75 additions and 7 deletions
--- a/docs/freqai-spice-rack.md
+++ b/docs/freqai-spice-rack.md
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+# Using the `spice_rack`
+
+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:
+
+```json
+    "freqai_spice_rack": true, 
+    "freqai_identifier": "spicey-id",
+```
+
+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:
+
+```python
+        dataframe['dissimilarity_index'] = self.freqai.spice_rack(
+            'DI_values', dataframe, metadata, self)
+        dataframe['extrema'] = self.freqai.spice_rack(
+            '&s-extrema', dataframe, metadata, self)
+        self.freqai.close_spice_rack()  # user must close the spicerack
+```
+
+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:
+
+```python
+    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+
+        df.loc[
+            (
+                (df['dissimilarity_index'] < 1) &
+                (df['extrema'] > 0.1)
+            ),
+            'enter_long'] = 1
+
+        df.loc[
+            (
+                (df['dissimilarity_index'] < 1) &
+                (df['extrema'] <> -0.1)
+            ),
+            'enter_short'] = 1
+
+        return df
+
+    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+
+        df.loc[
+            (
+                (df['dissimilarity_index'] < 1) &
+                (df['extrema'] > 0.1) 
+            ),
+
+            'exit_long'] = 1
+
+        df.loc[
+            (
+
+                (df['dissimilarity_index'] < 1) &
+                (df['extrema'] < -0.1)
+            ),
+            'exit_short'] = 1
+
+        return df
+```
+
+
+## Available indicators
+
+|  Parameter | Description |
+|------------|-------------|
+| `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.
+| `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.