diff --git a/docs/freqai-spice-rack.md b/docs/freqai-spice-rack.md
index fa32ccbed..ec0baad16 100644
--- a/docs/freqai-spice-rack.md
+++ b/docs/freqai-spice-rack.md
@@ -1,5 +1,8 @@
 # Using the `spice_rack`
 
+!!! 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).
+
 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
@@ -17,7 +20,7 @@ Which tells `FreqAI` to set up a pre-set `FreqAI` instance automatically under t
         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:
+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:
 
 ```python
     def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
@@ -25,14 +28,14 @@ Users can then use these columns, concert with all their own additional indicato
         df.loc[
             (
                 (df['dissimilarity_index'] < 1) &
-                (df['extrema'] > 0.1)
+                (df['extrema'] < -0.1)
             ),
             'enter_long'] = 1
 
         df.loc[
             (
                 (df['dissimilarity_index'] < 1) &
-                (df['extrema'] <> -0.1)
+                (df['extrema'] > 0.1)
             ),
             'enter_short'] = 1