Merge pull request #2084 from hroff-1902/hyperopt-print-params4

Improvements to hyperopt output

docs/hyperopt.md

@@ -303,8 +303,10 @@ Given the following result from hyperopt:
 
 ```
 Best result:
-   135 trades. Avg profit  0.57%. Total profit  0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins.
-with values:
+
+    44/100:    135 trades. Avg profit  0.57%. Total profit  0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. Objective: 1.94367
+
+Buy hyperspace params:
 {    'adx-value': 44,
      'rsi-value': 29,
      'adx-enabled': False,
@@ -347,21 +349,15 @@ If you are optimizing ROI, you're result will look as follows and include a ROI
 
 ```
 Best result:
-   135 trades. Avg profit  0.57%. Total profit  0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins.
-with values:
+
+    44/100:    135 trades. Avg profit  0.57%. Total profit  0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. Objective: 1.94367
+
+Buy hyperspace params:
 {   'adx-value': 44,
     'rsi-value': 29,
-    'adx-enabled': false,
+    'adx-enabled': False,
     'rsi-enabled': True,
-    'trigger': 'bb_lower',
-    'roi_t1': 40,
-    'roi_t2': 57,
-    'roi_t3': 21,
-    'roi_p1': 0.03634636907306948,
-    'roi_p2': 0.055237357937802885,
-    'roi_p3': 0.015163796015548354,
-    'stoploss': -0.37996664668703606
-}
+    'trigger': 'bb_lower'}
 ROI table:
 {   0: 0.10674752302642071,
     21: 0.09158372701087236,
@@ -374,7 +370,7 @@ This would translate to the following ROI table:
 ``` python
 minimal_roi = {
         "118": 0,
-        "78": 0.0363463,
+        "78": 0.0363,
         "21": 0.0915,
         "0": 0.106
     }

freqtrade/optimize/hyperopt.py

@@ -11,7 +11,7 @@ import sys
 from operator import itemgetter
 from pathlib import Path
 from pprint import pprint
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Optional
 
 from joblib import Parallel, delayed, dump, load, wrap_non_picklable_objects, cpu_count
 from pandas import DataFrame
@@ -133,11 +133,20 @@ class Hyperopt(Backtesting):
         params = best_result['params']
 
         log_str = self.format_results_logstring(best_result)
-        print(f"\nBest result:\n{log_str}\nwith values:")
-        pprint(params, indent=4)
+        print(f"\nBest result:\n\n{log_str}\n")
+        if self.has_space('buy'):
+            print('Buy hyperspace params:')
+            pprint({p.name: params.get(p.name) for p in self.hyperopt_space('buy')},
+                   indent=4)
+        if self.has_space('sell'):
+            print('Sell hyperspace params:')
+            pprint({p.name: params.get(p.name) for p in self.hyperopt_space('sell')},
+                   indent=4)
         if self.has_space('roi'):
             print("ROI table:")
             pprint(self.custom_hyperopt.generate_roi_table(params), indent=4)
+        if self.has_space('stoploss'):
+            print(f"Stoploss: {params.get('stoploss')}")
 
     def log_results(self, results) -> None:
         """
@@ -171,21 +180,24 @@ class Hyperopt(Backtesting):
         """
         return any(s in self.config['spaces'] for s in [space, 'all'])
 
-    def hyperopt_space(self) -> List[Dimension]:
+    def hyperopt_space(self, space: Optional[str] = None) -> List[Dimension]:
         """
-        Return the space to use during Hyperopt
+        Return the dimensions in the hyperoptimization space.
+        :param space: Defines hyperspace to return dimensions for.
+        If None, then the self.has_space() will be used to return dimensions
+        for all hyperspaces used.
         """
         spaces: List[Dimension] = []
-        if self.has_space('buy'):
+        if space == 'buy' or (space is None and self.has_space('buy')):
             logger.debug("Hyperopt has 'buy' space")
             spaces += self.custom_hyperopt.indicator_space()
-        if self.has_space('sell'):
+        if space == 'sell' or (space is None and self.has_space('sell')):
             logger.debug("Hyperopt has 'sell' space")
             spaces += self.custom_hyperopt.sell_indicator_space()
-        if self.has_space('roi'):
+        if space == 'roi' or (space is None and self.has_space('roi')):
             logger.debug("Hyperopt has 'roi' space")
             spaces += self.custom_hyperopt.roi_space()
-        if self.has_space('stoploss'):
+        if space == 'stoploss' or (space is None and self.has_space('stoploss')):
             logger.debug("Hyperopt has 'stoploss' space")
             spaces += self.custom_hyperopt.stoploss_space()
         return spaces

freqtrade/tests/optimize/test_hyperopt.py

@@ -463,7 +463,7 @@ def test_start_calls_optimizer(mocker, default_conf, caplog, capsys) -> None:
     parallel.assert_called_once()
 
     out, err = capsys.readouterr()
-    assert 'Best result:\n*    1/1: foo result Objective: 1.00000\nwith values:\n' in out
+    assert 'Best result:\n\n*    1/1: foo result Objective: 1.00000\n' in out
     assert dumper.called
     # Should be called twice, once for tickerdata, once to save evaluations
     assert dumper.call_count == 2