Merge pull request #5496 from LoveIsGrief/docs/performance-warning

Docs: Mention Performance Warning for strategies

docs/strategy-advanced.md

@@ -701,3 +701,33 @@ The variable 'content', will contain the strategy file in a BASE64 encoded form.
 ```
 
 Please ensure that 'NameOfStrategy' is identical to the strategy name!
+
+## Performance warning
+
+When executing a strategy, one can sometimes be greeted by the following in the logs
+
+> PerformanceWarning: DataFrame is highly fragmented.
+
+This is a warning from [`pandas`](https://github.com/pandas-dev/pandas) and as the warning continues to say:
+use `pd.concat(axis=1)`.
+This can have slight performance implications, which are usually only visible during hyperopt (when optimizing an indicator).
+
+For example:
+
+```python
+for val in self.buy_ema_short.range:
+    dataframe[f'ema_short_{val}'] = ta.EMA(dataframe, timeperiod=val)
+```
+
+should be rewritten to
+
+```python
+frames = [dataframe]
+for val in self.buy_ema_short.range:
+    frames.append({
+        f'ema_short_{val}': ta.EMA(dataframe, timeperiod=val)
+    })
+
+# Append columns to existing dataframe
+merged_frame = pd.concat(frames, axis=1)
+```

docs/strategy-customization.md

@@ -942,6 +942,8 @@ Printing more than a few rows is also possible (simply use  `print(dataframe)` i
 
 ## Common mistakes when developing strategies
 
+### Peeking into the future while backtesting
+
 Backtesting analyzes the whole time-range at once for performance reasons. Because of this, strategy authors need to make sure that strategies do not look-ahead into the future.
 This is a common pain-point, which can cause huge differences between backtesting and dry/live run methods, since they all use data which is not available during dry/live runs, so these strategies will perform well during backtesting, but will fail / perform badly in real conditions.