diff --git a/docs/freqai-reinforcement-learning.md b/docs/freqai-reinforcement-learning.md
index 8b775e046..7179da626 100644
--- a/docs/freqai-reinforcement-learning.md
+++ b/docs/freqai-reinforcement-learning.md
@@ -38,8 +38,6 @@ where `ReinforcementLearner` will use the templated `ReinforcementLearner` from
         self, pair, df, tf, informative=None, set_generalized_indicators=False
     ):
 
-        coin = pair.split('/')[0]
-
         if informative is None:
             informative = self.dp.get_pair_dataframe(pair, tf)
 
@@ -47,15 +45,15 @@ where `ReinforcementLearner` will use the templated `ReinforcementLearner` from
         for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
 
             t = int(t)
-            informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
-            informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
-            informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
+            informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
+            informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
+            informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
 
         # The following features are necessary for RL models
-        informative[f"%-{coin}raw_close"] = informative["close"]
-        informative[f"%-{coin}raw_open"] = informative["open"]
-        informative[f"%-{coin}raw_high"] = informative["high"]
-        informative[f"%-{coin}raw_low"] = informative["low"]
+        informative[f"%-{pair}raw_close"] = informative["close"]
+        informative[f"%-{pair}raw_open"] = informative["open"]
+        informative[f"%-{pair}raw_high"] = informative["high"]
+        informative[f"%-{pair}raw_low"] = informative["low"]
 
         indicators = [col for col in informative if col.startswith("%")]
         # This loop duplicates and shifts all indicators to add a sense of recency to data
@@ -88,10 +86,10 @@ Most of the function remains the same as for typical Regressors, however, the fu
 
 ```python
         # The following features are necessary for RL models
-        informative[f"%-{coin}raw_close"] = informative["close"]
-        informative[f"%-{coin}raw_open"] = informative["open"]
-        informative[f"%-{coin}raw_high"] = informative["high"]
-        informative[f"%-{coin}raw_low"] = informative["low"]
+        informative[f"%-{pair}raw_close"] = informative["close"]
+        informative[f"%-{pair}raw_open"] = informative["open"]
+        informative[f"%-{pair}raw_high"] = informative["high"]
+        informative[f"%-{pair}raw_low"] = informative["low"]
 ```
 
 Finally, there is no explicit "label" to make - instead the you need to assign the `&-action` column which will contain the agent's actions when accessed in `populate_entry/exit_trends()`. In the present example, the user set the neutral action to 0. This value should align with the environment used. FreqAI provides two environments, both use 0 as the neutral action.
diff --git a/freqtrade/freqai/RL/BaseReinforcementLearningModel.py b/freqtrade/freqai/RL/BaseReinforcementLearningModel.py
index f3da91b51..323cfd782 100644
--- a/freqtrade/freqai/RL/BaseReinforcementLearningModel.py
+++ b/freqtrade/freqai/RL/BaseReinforcementLearningModel.py
@@ -253,18 +253,26 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         Builds the train prices and test prices for the environment.
         """
 
-        coin = pair.split('/')[0]
+        pair = pair.replace(':', '')
         train_df = data_dictionary["train_features"]
         test_df = data_dictionary["test_features"]
 
         # price data for model training and evaluation
         tf = self.config['timeframe']
-        ohlc_list = [f'%-{coin}raw_open_{tf}', f'%-{coin}raw_low_{tf}',
-                     f'%-{coin}raw_high_{tf}', f'%-{coin}raw_close_{tf}']
-        rename_dict = {f'%-{coin}raw_open_{tf}': 'open', f'%-{coin}raw_low_{tf}': 'low',
-                       f'%-{coin}raw_high_{tf}': ' high', f'%-{coin}raw_close_{tf}': 'close'}
+        ohlc_list = [f'%-{pair}raw_open_{tf}', f'%-{pair}raw_low_{tf}',
+                     f'%-{pair}raw_high_{tf}', f'%-{pair}raw_close_{tf}']
+        rename_dict = {f'%-{pair}raw_open_{tf}': 'open', f'%-{pair}raw_low_{tf}': 'low',
+                       f'%-{pair}raw_high_{tf}': ' high', f'%-{pair}raw_close_{tf}': 'close'}
 
         prices_train = train_df.filter(ohlc_list, axis=1)
+        if prices_train.empty:
+            raise OperationalException('Reinforcement learning module didnt find the raw prices '
+                                       'assigned in populate_any_indicators. Please assign them '
+                                       'with:\n'
+                                       'informative[f"%-{pair}raw_close"] = informative["close"]\n'
+                                       'informative[f"%-{pair}raw_open"] = informative["open"]\n'
+                                       'informative[f"%-{pair}raw_high"] = informative["high"]\n'
+                                       'informative[f"%-{pair}raw_low"] = informative["low"]\n')
         prices_train.rename(columns=rename_dict, inplace=True)
         prices_train.reset_index(drop=True)