diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py
index 61436e8b1..6ad79c6a0 100644
--- a/freqtrade/optimize/hyperopt.py
+++ b/freqtrade/optimize/hyperopt.py
@@ -15,7 +15,7 @@ from numpy import iinfo, int32
 from operator import itemgetter
 from pathlib import Path
 from pprint import pprint
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Tuple, Set
 
 import rapidjson
 from colorama import Fore, Style
@@ -55,7 +55,7 @@ logger = logging.getLogger(__name__)
 NEXT_POINT_METHODS = ["cl_min", "cl_mean", "cl_max"]
 NEXT_POINT_METHODS_LENGTH = 3
 
-VOID_LOSS = iinfo(int32).max # just a big enough number to be bad result in loss optimization
+VOID_LOSS = iinfo(int32).max  # just a big enough number to be bad result in loss optimization
 
 
 class Hyperopt:
@@ -555,7 +555,7 @@ class Hyperopt:
             # only exclude results at the beginning when void loss is yet to be set
             void_filtered = list(filter(lambda v: v["loss"] != VOID_LOSS, vals))
         else:
-            if opt.void_loss == VOID_LOSS: # set void loss once
+            if opt.void_loss == VOID_LOSS:  # set void loss once
                 opt.void_loss = max(opt.yi)
             void_filtered = []
             # default bad losses to set void_loss
@@ -616,7 +616,7 @@ class Hyperopt:
         """
         vals = []
         to_ask: deque = deque()
-        evald: set(Tuple) = set()
+        evald: Set[Tuple] = set()
         fit = False
         opt = self.opt
         for r in range(tries):
@@ -625,10 +625,10 @@ class Hyperopt:
             if vals:
                 # filter losses
                 void_filtered = self.filter_void_losses(vals, opt)
-                if vals: # again if all are filtered
+                if void_filtered:  # again if all are filtered
                     opt.tell([list(v['params_dict'].values()) for v in void_filtered],
-                                [v['loss'] for v in vals],
-                                fit=fit)
+                             [v['loss'] for v in void_filtered],
+                             fit=fit)
                     if fit:
                         fit = False
                     del vals[:], void_filtered[:]
@@ -841,7 +841,7 @@ class Hyperopt:
                 for _ in range(remaining):  # generate optimizers
                     # random state is preserved
                     opt_copy = opt.copy(random_state=opt.rng.randint(0,
-                                                                iinfo(int32).max))
+                                                                     iinfo(int32).max))
                     opt_copy.void_loss = VOID_LOSS
                     backend.optimizers.put(opt_copy)
                 del opt, opt_copy
@@ -857,6 +857,23 @@ class Hyperopt:
                 self.opt.void_loss = VOID_LOSS
         del opts[:]
 
+    def setup_points(self):
+        self.n_initial_points, self.min_epochs, self.search_space_size = self.calc_epochs(
+            self.dimensions, self.n_jobs, self.effort, self.total_epochs, self.n_points
+        )
+        logger.info(f"Min epochs set to: {self.min_epochs}")
+        # reduce random points by n_points in multi mode because asks are per job
+        if self.multi:
+            self.opt_n_initial_points = self.n_initial_points // self.n_points
+        else:
+            self.opt_n_initial_points = self.n_initial_points
+        logger.info(f'Initial points: {self.n_initial_points}')
+        # if total epochs are not set, max_epoch takes its place
+        if self.total_epochs < 1:
+            self.max_epoch = int(self.min_epochs + len(self.trials))
+        # initialize average best occurrence
+        self.avg_best_occurrence = self.min_epochs // self.n_jobs
+
     def start(self) -> None:
         """ Broom Broom """
         self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
@@ -887,22 +904,8 @@ class Hyperopt:
         logger.info(f'Number of parallel jobs set as: {self.n_jobs}')
 
         self.dimensions: List[Dimension] = self.hyperopt_space()
-        self.n_initial_points, self.min_epochs, self.search_space_size = self.calc_epochs(
-            self.dimensions, self.n_jobs, self.effort, self.total_epochs, self.n_points
-        )
-        # reduce random points by n_points in multi mode because asks are per job
-        if self.multi:
-            self.opt_n_initial_points = self.n_initial_points // self.n_points
-        else:
-            self.opt_n_initial_points = self.n_initial_points
-        logger.info(f"Min epochs set to: {self.min_epochs}")
-        # if total epochs are not set, max_epoch takes its place
-        if self.total_epochs < 1:
-            self.max_epoch = int(self.min_epochs + len(self.trials))
-        # initialize average best occurrence
-        self.avg_best_occurrence = self.min_epochs // self.n_jobs
+        self.setup_points()
 
-        logger.info(f'Initial points: {self.n_initial_points}')
         if self.print_colorized:
             colorama_init(autoreset=True)