diff --git a/docs/hyperopt.md b/docs/hyperopt.md
index f209b9528..e930f4bf4 100644
--- a/docs/hyperopt.md
+++ b/docs/hyperopt.md
@@ -490,7 +490,7 @@ freqtrade hyperopt --config config.json --hyperopt-loss <hyperoptlossname> --str
 ```
 
 The `-e` option will set how many evaluations hyperopt will do. Since hyperopt uses Bayesian search, running too many epochs at once may not produce greater results. Experience has shown that best results are usually not improving much after 500-1000 epochs.  
-The `-es` option will set ofter how many batches of evaluations with no improvements hyperopt will stop. A good value is 20-30% of the total epochs. Early stop is by default disabled (`-es=0`)
+The `-es` option will set after how many epochs with no improvements hyperopt will stop. A good value is 20-30% of the total epochs. Early stop is by default disabled (`-es=0`)
 
 Doing multiple runs (executions) with a few 1000 epochs and different random state will most likely produce different results.
 
diff --git a/freqtrade/optimize/hyperopt/hyperopt_optimizer.py b/freqtrade/optimize/hyperopt/hyperopt_optimizer.py
index 467475b6d..dd09bf318 100644
--- a/freqtrade/optimize/hyperopt/hyperopt_optimizer.py
+++ b/freqtrade/optimize/hyperopt/hyperopt_optimizer.py
@@ -108,7 +108,7 @@ class HyperOptimizer:
         self.es_epochs = config.get("early_stop", 0)
         self.es_batches = self.es_epochs // config.get("hyperopt_jobs", 1)
         if self.es_epochs > 0 and self.es_epochs < 0.2 * config.get("epochs", 0):
-            logger.warning(f"Easly stop epochs {self.es_epochs} lower than 20% of total epochs")
+            logger.warning(f"Early stop epochs {self.es_epochs} lower than 20% of total epochs")
 
         if HyperoptTools.has_space(self.config, "sell"):
             # Make sure use_exit_signal is enabled