improve performance and documentation of spice-rack.

docs/freqai-spice-rack.md

@@ -0,0 +1,68 @@
+# 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.

freqtrade/freqai/data_kitchen.py

@@ -1262,7 +1262,7 @@ class FreqaiDataKitchen:
         return file_exists
 
     def spice_extractor(self, indicator: str, dataframe: DataFrame) -> npt.NDArray:
-        if indicator in dataframe:
+        if indicator in dataframe.columns:
             return np.array(dataframe[indicator])
         else:
             logger.warning(f'User asked spice_rack for {indicator}, '

freqtrade/freqai/spice_rack/lightgbm_config.json

@@ -4,7 +4,7 @@
         "enabled": true,
         "purge_old_models": true,
         "train_period_days": 4,
-        "backtest_period_days": 2,
+        "backtest_period_days": 1,
         "identifier": "spicy-id",
         "feature_parameters": {
             "include_timeframes": [

freqtrade/freqai/utils.py

@@ -160,14 +160,13 @@ def auto_populate_any_indicators(
     if set_generalized_indicators:
         df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
         df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
-        df["&s-minima"] = 0
+        df["&s-extrema"] = 0
-        df["&s-maxima"] = 0
         min_peaks = argrelextrema(df["close"].values, np.less, order=80)
         max_peaks = argrelextrema(df["close"].values, np.greater, order=80)
         for mp in min_peaks[0]:
-            df.at[mp, "&s-minima"] = 1
+            df.at[mp, "&s-extrema"] = -1
         for mp in max_peaks[0]:
-            df.at[mp, "&s-maxima"] = 1
+            df.at[mp, "&s-extrema"] = 1
 
     return df
 
@@ -222,7 +221,7 @@ def setup_freqai_spice_rack(config: dict, exchange: Optional[Exchange]) -> Dict[
 
     config['freqai']['feature_parameters'].update({'include_timeframes': new_tfs})
     config['freqai']['feature_parameters'].update({'include_corr_pairlist': new_corr_pairs})
-    config.update({"freqaimodel": 'LightGBMRegressorMultiTarget'})
+    config.update({"freqaimodel": 'LightGBMRegressor'})
     return config
 
 # Keep below for when we wish to download heterogeneously lengthed data for FreqAI.

mkdocs.yml

@@ -29,6 +29,7 @@ nav:
         - Parameter table: freqai-parameter-table.md
         - Feature engineering: freqai-feature-engineering.md
         - Running FreqAI: freqai-running.md
+        - Spice Rack: freqai-spice-rack.md
         - Developer guide: freqai-developers.md
     - Short / Leverage: leverage.md
     - Utility Sub-commands: utils.md