Merge pull request #4848 from freqtrade/hyperopt_btresults

Hyperopt store backtest-outcome

freqtrade/commands/hyperopt_commands.py

@@ -7,6 +7,7 @@ from colorama import init as colorama_init
 from freqtrade.configuration import setup_utils_configuration
 from freqtrade.data.btanalysis import get_latest_hyperopt_file
 from freqtrade.exceptions import OperationalException
+from freqtrade.optimize.optimize_reports import show_backtest_result
 from freqtrade.state import RunMode
 
 
@@ -125,6 +126,12 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
 
     if epochs:
         val = epochs[n]
+
+        metrics = val['results_metrics']
+        if 'strategy_name' in metrics:
+            show_backtest_result(metrics['strategy_name'], metrics,
+                                 metrics['stake_currency'])
+
         HyperoptTools.print_epoch_details(val, total_epochs, print_json, no_header,
                                           header_str="Epoch details")
 
@@ -132,11 +139,13 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
 def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
     """
     Filter our items from the list of hyperopt results
+    TODO: after 2021.5 remove all "legacy" mode queries.
     """
     if filteroptions['only_best']:
         epochs = [x for x in epochs if x['is_best']]
     if filteroptions['only_profitable']:
-        epochs = [x for x in epochs if x['results_metrics']['profit'] > 0]
+        epochs = [x for x in epochs if x['results_metrics'].get(
+            'profit', x['results_metrics'].get('profit_total', 0)) > 0]
 
     epochs = _hyperopt_filter_epochs_trade_count(epochs, filteroptions)
 
@@ -153,34 +162,55 @@ def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
     return epochs
 
 
+def _hyperopt_filter_epochs_trade(epochs: List, trade_count: int):
+    """
+    Filter epochs with trade-counts > trades
+    """
+    return [
+        x for x in epochs
+        if x['results_metrics'].get(
+            'trade_count', x['results_metrics'].get('total_trades', 0)
+        ) > trade_count
+    ]
+
+
 def _hyperopt_filter_epochs_trade_count(epochs: List, filteroptions: dict) -> List:
 
     if filteroptions['filter_min_trades'] > 0:
-        epochs = [
+        epochs = _hyperopt_filter_epochs_trade(epochs, filteroptions['filter_min_trades'])
-            x for x in epochs
+
-            if x['results_metrics']['trade_count'] > filteroptions['filter_min_trades']
-        ]
     if filteroptions['filter_max_trades'] > 0:
         epochs = [
             x for x in epochs
-            if x['results_metrics']['trade_count'] < filteroptions['filter_max_trades']
+            if x['results_metrics'].get(
+                'trade_count', x['results_metrics'].get('total_trades')
+                ) < filteroptions['filter_max_trades']
         ]
     return epochs
 
 
 def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List:
 
+    def get_duration_value(x):
+        # Duration in minutes ...
+        if 'duration' in x['results_metrics']:
+            return x['results_metrics']['duration']
+        else:
+            # New mode
+            avg = x['results_metrics']['holding_avg']
+            return avg.total_seconds() // 60
+
     if filteroptions['filter_min_avg_time'] is not None:
-        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
         epochs = [
             x for x in epochs
-            if x['results_metrics']['duration'] > filteroptions['filter_min_avg_time']
+            if get_duration_value(x) > filteroptions['filter_min_avg_time']
         ]
     if filteroptions['filter_max_avg_time'] is not None:
-        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
         epochs = [
             x for x in epochs
-            if x['results_metrics']['duration'] < filteroptions['filter_max_avg_time']
+            if get_duration_value(x) < filteroptions['filter_max_avg_time']
         ]
 
     return epochs
@@ -189,28 +219,36 @@ def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List:
 def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List:
 
     if filteroptions['filter_min_avg_profit'] is not None:
-        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
         epochs = [
             x for x in epochs
-            if x['results_metrics']['avg_profit'] > filteroptions['filter_min_avg_profit']
+            if x['results_metrics'].get(
+                'avg_profit', x['results_metrics'].get('profit_mean', 0) * 100
+            ) > filteroptions['filter_min_avg_profit']
         ]
     if filteroptions['filter_max_avg_profit'] is not None:
-        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
         epochs = [
             x for x in epochs
-            if x['results_metrics']['avg_profit'] < filteroptions['filter_max_avg_profit']
+            if x['results_metrics'].get(
+                'avg_profit', x['results_metrics'].get('profit_mean', 0) * 100
+                ) < filteroptions['filter_max_avg_profit']
         ]
     if filteroptions['filter_min_total_profit'] is not None:
-        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
         epochs = [
             x for x in epochs
-            if x['results_metrics']['profit'] > filteroptions['filter_min_total_profit']
+            if x['results_metrics'].get(
+                'profit', x['results_metrics'].get('profit_total_abs', 0)
+                ) > filteroptions['filter_min_total_profit']
         ]
     if filteroptions['filter_max_total_profit'] is not None:
-        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
         epochs = [
             x for x in epochs
-            if x['results_metrics']['profit'] < filteroptions['filter_max_total_profit']
+            if x['results_metrics'].get(
+                'profit', x['results_metrics'].get('profit_total_abs', 0)
+                ) < filteroptions['filter_max_total_profit']
         ]
     return epochs
 
@@ -218,11 +256,11 @@ def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List:
 def _hyperopt_filter_epochs_objective(epochs: List, filteroptions: dict) -> List:
 
     if filteroptions['filter_min_objective'] is not None:
-        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
 
         epochs = [x for x in epochs if x['loss'] < filteroptions['filter_min_objective']]
     if filteroptions['filter_max_objective'] is not None:
-        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
 
         epochs = [x for x in epochs if x['loss'] > filteroptions['filter_max_objective']]
 

freqtrade/optimize/backtesting.py

@@ -330,7 +330,7 @@ class Backtesting:
     def backtest(self, processed: Dict,
                  start_date: datetime, end_date: datetime,
                  max_open_trades: int = 0, position_stacking: bool = False,
-                 enable_protections: bool = False) -> DataFrame:
+                 enable_protections: bool = False) -> Dict[str, Any]:
         """
         Implement backtesting functionality
 
@@ -417,7 +417,13 @@ class Backtesting:
         trades += self.handle_left_open(open_trades, data=data)
         self.wallets.update()
 
-        return trade_list_to_dataframe(trades)
+        results = trade_list_to_dataframe(trades)
+        return {
+            'results': results,
+            'config': self.strategy.config,
+            'locks': PairLocks.get_all_locks(),
+            'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
+        }
 
     def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any], timerange: TimeRange):
         logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
@@ -457,14 +463,12 @@ class Backtesting:
             enable_protections=self.config.get('enable_protections', False),
         )
         backtest_end_time = datetime.now(timezone.utc)
-        self.all_results[self.strategy.get_strategy_name()] = {
+        results.update({
-            'results': results,
-            'config': self.strategy.config,
-            'locks': PairLocks.get_all_locks(),
-            'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
             'backtest_start_time': int(backtest_start_time.timestamp()),
             'backtest_end_time': int(backtest_end_time.timestamp()),
-        }
+        })
+        self.all_results[self.strategy.get_strategy_name()] = results
+
         return min_date, max_date
 
     def start(self) -> None:

freqtrade/optimize/hyperopt.py

@@ -4,11 +4,10 @@
 This module contains the hyperopt logic
 """
 
-import locale
 import logging
 import random
 import warnings
-from datetime import datetime
+from datetime import datetime, timezone
 from math import ceil
 from operator import itemgetter
 from pathlib import Path
@@ -30,6 +29,7 @@ from freqtrade.optimize.hyperopt_auto import HyperOptAuto
 from freqtrade.optimize.hyperopt_interface import IHyperOpt  # noqa: F401
 from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss  # noqa: F401
 from freqtrade.optimize.hyperopt_tools import HyperoptTools
+from freqtrade.optimize.optimize_reports import generate_strategy_stats
 from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver
 from freqtrade.strategy import IStrategy
 
@@ -65,6 +65,13 @@ class Hyperopt:
     custom_hyperopt: IHyperOpt
 
     def __init__(self, config: Dict[str, Any]) -> None:
+        self.buy_space: List[Dimension] = []
+        self.sell_space: List[Dimension] = []
+        self.roi_space: List[Dimension] = []
+        self.stoploss_space: List[Dimension] = []
+        self.trailing_space: List[Dimension] = []
+        self.dimensions: List[Dimension] = []
+
         self.config = config
 
         self.backtesting = Backtesting(self.config)
@@ -79,9 +86,8 @@ class Hyperopt:
         self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
         time_now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
         strategy = str(self.config['strategy'])
-        self.results_file = (self.config['user_data_dir'] /
+        self.results_file: Path = (self.config['user_data_dir'] / 'hyperopt_results' /
-                             'hyperopt_results' /
+                                   f'strategy_{strategy}_hyperopt_results_{time_now}.pickle')
-                             f'strategy_{strategy}_hyperopt_results_{time_now}.pickle')
         self.data_pickle_file = (self.config['user_data_dir'] /
                                  'hyperopt_results' / 'hyperopt_tickerdata.pkl')
         self.total_epochs = config.get('epochs', 0)
@@ -140,9 +146,7 @@ class Hyperopt:
                 logger.info(f"Removing `{p}`.")
                 p.unlink()
 
-    def _get_params_dict(self, raw_params: List[Any]) -> Dict:
+    def _get_params_dict(self, dimensions: List[Dimension], raw_params: List[Any]) -> Dict:
-
-        dimensions: List[Dimension] = self.dimensions
 
         # Ensure the number of dimensions match
         # the number of parameters in the list.
@@ -176,16 +180,13 @@ class Hyperopt:
         result: Dict = {}
 
         if HyperoptTools.has_space(self.config, 'buy'):
-            result['buy'] = {p.name: params.get(p.name)
+            result['buy'] = {p.name: params.get(p.name) for p in self.buy_space}
-                             for p in self.hyperopt_space('buy')}
         if HyperoptTools.has_space(self.config, 'sell'):
-            result['sell'] = {p.name: params.get(p.name)
+            result['sell'] = {p.name: params.get(p.name) for p in self.sell_space}
-                              for p in self.hyperopt_space('sell')}
         if HyperoptTools.has_space(self.config, 'roi'):
             result['roi'] = self.custom_hyperopt.generate_roi_table(params)
         if HyperoptTools.has_space(self.config, 'stoploss'):
-            result['stoploss'] = {p.name: params.get(p.name)
+            result['stoploss'] = {p.name: params.get(p.name) for p in self.stoploss_space}
-                                  for p in self.hyperopt_space('stoploss')}
         if HyperoptTools.has_space(self.config, 'trailing'):
             result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
 
@@ -208,47 +209,42 @@ class Hyperopt:
             )
             self.hyperopt_table_header = 2
 
-    def hyperopt_space(self, space: Optional[str] = None) -> List[Dimension]:
+    def init_spaces(self):
         """
-        Return the dimensions in the hyperoptimization space.
+        Assign 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 space == 'buy' or (space is None and HyperoptTools.has_space(self.config, 'buy')):
+        if HyperoptTools.has_space(self.config, 'buy'):
             logger.debug("Hyperopt has 'buy' space")
-            spaces += self.custom_hyperopt.indicator_space()
+            self.buy_space = self.custom_hyperopt.indicator_space()
 
-        if space == 'sell' or (space is None and HyperoptTools.has_space(self.config, 'sell')):
+        if HyperoptTools.has_space(self.config, 'sell'):
             logger.debug("Hyperopt has 'sell' space")
-            spaces += self.custom_hyperopt.sell_indicator_space()
+            self.sell_space = self.custom_hyperopt.sell_indicator_space()
 
-        if space == 'roi' or (space is None and HyperoptTools.has_space(self.config, 'roi')):
+        if HyperoptTools.has_space(self.config, 'roi'):
             logger.debug("Hyperopt has 'roi' space")
-            spaces += self.custom_hyperopt.roi_space()
+            self.roi_space = self.custom_hyperopt.roi_space()
 
-        if space == 'stoploss' or (space is None
+        if HyperoptTools.has_space(self.config, 'stoploss'):
-                                   and HyperoptTools.has_space(self.config, 'stoploss')):
             logger.debug("Hyperopt has 'stoploss' space")
-            spaces += self.custom_hyperopt.stoploss_space()
+            self.stoploss_space = self.custom_hyperopt.stoploss_space()
 
-        if space == 'trailing' or (space is None
+        if HyperoptTools.has_space(self.config, 'trailing'):
-                                   and HyperoptTools.has_space(self.config, 'trailing')):
             logger.debug("Hyperopt has 'trailing' space")
-            spaces += self.custom_hyperopt.trailing_space()
+            self.trailing_space = self.custom_hyperopt.trailing_space()
-
+        self.dimensions = (self.buy_space + self.sell_space + self.roi_space +
-        return spaces
+                           self.stoploss_space + self.trailing_space)
 
     def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
         """
         Used Optimize function. Called once per epoch to optimize whatever is configured.
         Keep this function as optimized as possible!
         """
-        params_dict = self._get_params_dict(raw_params)
+        backtest_start_time = datetime.now(timezone.utc)
-        params_details = self._get_params_details(params_dict)
+        params_dict = self._get_params_dict(self.dimensions, raw_params)
 
+        # Apply parameters
         if HyperoptTools.has_space(self.config, 'roi'):
             self.backtesting.strategy.minimal_roi = (  # type: ignore
                 self.custom_hyperopt.generate_roi_table(params_dict))
@@ -275,28 +271,40 @@ class Hyperopt:
 
         processed = load(self.data_pickle_file)
 
-        min_date, max_date = get_timerange(processed)
+        bt_results = self.backtesting.backtest(
-
-        backtesting_results = self.backtesting.backtest(
             processed=processed,
-            start_date=min_date.datetime,
+            start_date=self.min_date.datetime,
-            end_date=max_date.datetime,
+            end_date=self.max_date.datetime,
             max_open_trades=self.max_open_trades,
             position_stacking=self.position_stacking,
             enable_protections=self.config.get('enable_protections', False),
-
         )
-        return self._get_results_dict(backtesting_results, min_date, max_date,
+        backtest_end_time = datetime.now(timezone.utc)
-                                      params_dict, params_details,
+        bt_results.update({
+            'backtest_start_time': int(backtest_start_time.timestamp()),
+            'backtest_end_time': int(backtest_end_time.timestamp()),
+        })
+
+        return self._get_results_dict(bt_results, self.min_date, self.max_date,
+                                      params_dict,
                                       processed=processed)
 
     def _get_results_dict(self, backtesting_results, min_date, max_date,
-                          params_dict, params_details, processed: Dict[str, DataFrame]):
+                          params_dict, processed: Dict[str, DataFrame]
-        results_metrics = self._calculate_results_metrics(backtesting_results)
+                          ) -> Dict[str, Any]:
-        results_explanation = self._format_results_explanation_string(results_metrics)
+        params_details = self._get_params_details(params_dict)
 
-        trade_count = results_metrics['trade_count']
+        strat_stats = generate_strategy_stats(
-        total_profit = results_metrics['total_profit']
+            processed, self.backtesting.strategy.get_strategy_name(),
+            backtesting_results, min_date, max_date, market_change=0
+        )
+        results_explanation = HyperoptTools.format_results_explanation_string(
+            strat_stats, self.config['stake_currency'])
+
+        not_optimized = self.backtesting.strategy.get_params_dict()
+
+        trade_count = strat_stats['total_trades']
+        total_profit = strat_stats['profit_total']
 
         # If this evaluation contains too short amount of trades to be
         # interesting -- consider it as 'bad' (assigned max. loss value)
@@ -304,50 +312,20 @@ class Hyperopt:
         # path. We do not want to optimize 'hodl' strategies.
         loss: float = MAX_LOSS
         if trade_count >= self.config['hyperopt_min_trades']:
-            loss = self.calculate_loss(results=backtesting_results, trade_count=trade_count,
+            loss = self.calculate_loss(results=backtesting_results['results'],
+                                       trade_count=trade_count,
                                        min_date=min_date.datetime, max_date=max_date.datetime,
                                        config=self.config, processed=processed)
         return {
             'loss': loss,
             'params_dict': params_dict,
             'params_details': params_details,
-            'results_metrics': results_metrics,
+            'params_not_optimized': not_optimized,
+            'results_metrics': strat_stats,
             'results_explanation': results_explanation,
             'total_profit': total_profit,
         }
 
-    def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict:
-        wins = len(backtesting_results[backtesting_results['profit_ratio'] > 0])
-        draws = len(backtesting_results[backtesting_results['profit_ratio'] == 0])
-        losses = len(backtesting_results[backtesting_results['profit_ratio'] < 0])
-        return {
-            'trade_count': len(backtesting_results.index),
-            'wins': wins,
-            'draws': draws,
-            'losses': losses,
-            'winsdrawslosses': f"{wins:>4} {draws:>4} {losses:>4}",
-            'avg_profit': backtesting_results['profit_ratio'].mean() * 100.0,
-            'median_profit': backtesting_results['profit_ratio'].median() * 100.0,
-            'total_profit': backtesting_results['profit_abs'].sum(),
-            'profit': backtesting_results['profit_ratio'].sum() * 100.0,
-            'duration': backtesting_results['trade_duration'].mean(),
-        }
-
-    def _format_results_explanation_string(self, results_metrics: Dict) -> str:
-        """
-        Return the formatted results explanation in a string
-        """
-        stake_cur = self.config['stake_currency']
-        return (f"{results_metrics['trade_count']:6d} trades. "
-                f"{results_metrics['wins']}/{results_metrics['draws']}"
-                f"/{results_metrics['losses']} Wins/Draws/Losses. "
-                f"Avg profit {results_metrics['avg_profit']: 6.2f}%. "
-                f"Median profit {results_metrics['median_profit']: 6.2f}%. "
-                f"Total profit {results_metrics['total_profit']: 11.8f} {stake_cur} "
-                f"({results_metrics['profit']: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
-                f"Avg duration {results_metrics['duration']:5.1f} min."
-                ).encode(locale.getpreferredencoding(), 'replace').decode('utf-8')
-
     def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
         return Optimizer(
             dimensions,
@@ -370,6 +348,8 @@ class Hyperopt:
         self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
         logger.info(f"Using optimizer random state: {self.random_state}")
         self.hyperopt_table_header = -1
+        # Initialize spaces ...
+        self.init_spaces()
         data, timerange = self.backtesting.load_bt_data()
         logger.info("Dataload complete. Calculating indicators")
         preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
@@ -378,11 +358,11 @@ class Hyperopt:
         for pair, df in preprocessed.items():
             preprocessed[pair] = trim_dataframe(df, timerange,
                                                 startup_candles=self.backtesting.required_startup)
-        min_date, max_date = get_timerange(preprocessed)
+        self.min_date, self.max_date = get_timerange(preprocessed)
 
-        logger.info(f'Hyperopting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
+        logger.info(f'Hyperopting with data from {self.min_date.strftime(DATETIME_PRINT_FORMAT)} '
-                    f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
+                    f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} '
-                    f'({(max_date - min_date).days} days)..')
+                    f'({(self.max_date - self.min_date).days} days)..')
 
         dump(preprocessed, self.data_pickle_file)
 
@@ -400,7 +380,6 @@ class Hyperopt:
         config_jobs = self.config.get('hyperopt_jobs', -1)
         logger.info(f'Number of parallel jobs set as: {config_jobs}')
 
-        self.dimensions: List[Dimension] = self.hyperopt_space()
         self.opt = self.get_optimizer(self.dimensions, config_jobs)
 
         if self.print_colorized:

freqtrade/optimize/hyperopt_tools.py

@@ -1,9 +1,9 @@
 
 import io
+import locale
 import logging
 from collections import OrderedDict
 from pathlib import Path
-from pprint import pformat
 from typing import Any, Dict, List
 
 import rapidjson
@@ -12,7 +12,7 @@ from colorama import Fore, Style
 from pandas import isna, json_normalize
 
 from freqtrade.exceptions import OperationalException
-from freqtrade.misc import round_dict
+from freqtrade.misc import round_coin_value, round_dict
 
 
 logger = logging.getLogger(__name__)
@@ -65,6 +65,7 @@ class HyperoptTools():
         Display details of the hyperopt result
         """
         params = results.get('params_details', {})
+        non_optimized = results.get('params_not_optimized', {})
 
         # Default header string
         if header_str is None:
@@ -81,8 +82,10 @@ class HyperoptTools():
             print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
 
         else:
-            HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:")
+            HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:",
-            HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:")
+                                               non_optimized)
+            HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:",
+                                               non_optimized)
             HyperoptTools._params_pretty_print(params, 'roi', "ROI table:")
             HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:")
             HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:")
@@ -108,12 +111,12 @@ class HyperoptTools():
                 result_dict.update(space_params)
 
     @staticmethod
-    def _params_pretty_print(params, space: str, header: str) -> None:
+    def _params_pretty_print(params, space: str, header: str, non_optimized={}) -> None:
-        if space in params:
+        if space in params or space in non_optimized:
             space_params = HyperoptTools._space_params(params, space, 5)
-            params_result = f"\n# {header}\n"
+            result = f"\n# {header}\n"
             if space == 'stoploss':
-                params_result += f"stoploss = {space_params.get('stoploss')}"
+                result += f"stoploss = {space_params.get('stoploss')}"
             elif space == 'roi':
                 # TODO: get rid of OrderedDict when support for python 3.6 will be
                 # dropped (dicts keep the order as the language feature)
@@ -122,29 +125,64 @@ class HyperoptTools():
                         (str(k), v) for k, v in space_params.items()
                     ),
                     default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
-                params_result += f"minimal_roi = {minimal_roi_result}"
+                result += f"minimal_roi = {minimal_roi_result}"
             elif space == 'trailing':
 
                 for k, v in space_params.items():
-                    params_result += f'{k} = {v}\n'
+                    result += f'{k} = {v}\n'
 
             else:
-                params_result += f"{space}_params = {pformat(space_params, indent=4)}"
+                no_params = HyperoptTools._space_params(non_optimized, space, 5)
-                params_result = params_result.replace("}", "\n}").replace("{", "{\n ")
 
-            params_result = params_result.replace("\n", "\n    ")
+                result += f"{space}_params = {HyperoptTools._pprint(space_params, no_params)}"
-            print(params_result)
+
+            result = result.replace("\n", "\n    ")
+            print(result)
 
     @staticmethod
     def _space_params(params, space: str, r: int = None) -> Dict:
-        d = params[space]
+        d = params.get(space)
-        # Round floats to `r` digits after the decimal point if requested
+        if d:
-        return round_dict(d, r) if r else d
+            # Round floats to `r` digits after the decimal point if requested
+            return round_dict(d, r) if r else d
+        return {}
+
+    @staticmethod
+    def _pprint(params, non_optimized, indent: int = 4):
+        """
+        Pretty-print hyperopt results (based on 2 dicts - with add. comment)
+        """
+        p = params.copy()
+        p.update(non_optimized)
+        result = '{\n'
+
+        for k, param in p.items():
+            result += " " * indent + f'"{k}": {param},'
+            if k in non_optimized:
+                result += "  # value loaded from strategy"
+            result += "\n"
+        result += '}'
+        return result
 
     @staticmethod
     def is_best_loss(results, current_best_loss: float) -> bool:
         return results['loss'] < current_best_loss
 
+    @staticmethod
+    def format_results_explanation_string(results_metrics: Dict, stake_currency: str) -> str:
+        """
+        Return the formatted results explanation in a string
+        """
+        return (f"{results_metrics['total_trades']:6d} trades. "
+                f"{results_metrics['wins']}/{results_metrics['draws']}"
+                f"/{results_metrics['losses']} Wins/Draws/Losses. "
+                f"Avg profit {results_metrics['profit_mean'] * 100: 6.2f}%. "
+                f"Median profit {results_metrics['profit_median'] * 100: 6.2f}%. "
+                f"Total profit {results_metrics['profit_total_abs']: 11.8f} {stake_currency} "
+                f"({results_metrics['profit_total'] * 100: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
+                f"Avg duration {results_metrics['holding_avg']} min."
+                ).encode(locale.getpreferredencoding(), 'replace').decode('utf-8')
+
     @staticmethod
     def _format_explanation_string(results, total_epochs) -> str:
         return (("*" if results['is_initial_point'] else " ") +
@@ -168,12 +206,27 @@ class HyperoptTools():
         if 'results_metrics.winsdrawslosses' not in trials.columns:
             # Ensure compatibility with older versions of hyperopt results
             trials['results_metrics.winsdrawslosses'] = 'N/A'
+        legacy_mode = True
+
+        if 'results_metrics.total_trades' in trials:
+            legacy_mode = False
+            # New mode, using backtest result for metrics
+            trials['results_metrics.winsdrawslosses'] = trials.apply(
+                lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
+                          f"{x['results_metrics.losses']:>4}", axis=1)
+            trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
+                             'results_metrics.winsdrawslosses',
+                             'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
+                             'results_metrics.profit_total', 'results_metrics.holding_avg',
+                             'loss', 'is_initial_point', 'is_best']]
+        else:
+            # Legacy mode
+            trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
+                             'results_metrics.winsdrawslosses',
+                             'results_metrics.avg_profit', 'results_metrics.total_profit',
+                             'results_metrics.profit', 'results_metrics.duration',
+                             'loss', 'is_initial_point', 'is_best']]
 
-        trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
-                         'results_metrics.winsdrawslosses',
-                         'results_metrics.avg_profit', 'results_metrics.total_profit',
-                         'results_metrics.profit', 'results_metrics.duration',
-                         'loss', 'is_initial_point', 'is_best']]
         trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
                           'Total profit', 'Profit', 'Avg duration', 'Objective',
                           'is_initial_point', 'is_best']
@@ -183,26 +236,28 @@ class HyperoptTools():
         trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
         trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
         trials['Trades'] = trials['Trades'].astype(str)
-
+        perc_multi = 1 if legacy_mode else 100
         trials['Epoch'] = trials['Epoch'].apply(
             lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
         )
         trials['Avg profit'] = trials['Avg profit'].apply(
-            lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
+            lambda x: f'{x * perc_multi:,.2f}%'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
         )
         trials['Avg duration'] = trials['Avg duration'].apply(
-            lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
+            lambda x: f'{x:,.1f} m'.rjust(7, ' ') if isinstance(x, float) else f"{x}"
+                      if not isna(x) else "--".rjust(7, ' ')
         )
         trials['Objective'] = trials['Objective'].apply(
-            lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
+            lambda x: f'{x:,.5f}'.rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
         )
 
+        stake_currency = config['stake_currency']
         trials['Profit'] = trials.apply(
-            lambda x: '{:,.8f} {} {}'.format(
+            lambda x: '{} {}'.format(
-                x['Total profit'], config['stake_currency'],
+                round_coin_value(x['Total profit'], stake_currency),
-                '({:,.2f}%)'.format(x['Profit']).rjust(10, ' ')
+                '({:,.2f}%)'.format(x['Profit'] * perc_multi).rjust(10, ' ')
-            ).rjust(25+len(config['stake_currency']))
+            ).rjust(25+len(stake_currency))
-            if x['Total profit'] != 0.0 else '--'.rjust(25+len(config['stake_currency'])),
+            if x['Total profit'] != 0.0 else '--'.rjust(25+len(stake_currency)),
             axis=1
         )
         trials = trials.drop(columns=['Total profit'])
@@ -263,11 +318,21 @@ class HyperoptTools():
         trials['Best'] = ''
         trials['Stake currency'] = config['stake_currency']
 
-        base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
+        if 'results_metrics.total_trades' in trials:
-                        'results_metrics.avg_profit', 'results_metrics.median_profit',
+            base_metrics = ['Best', 'current_epoch', 'results_metrics.total_trades',
-                        'results_metrics.total_profit',
+                            'results_metrics.profit_mean', 'results_metrics.profit_median',
-                        'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
+                            'results_metrics.profit_total',
-                        'loss', 'is_initial_point', 'is_best']
+                            'Stake currency',
+                            'results_metrics.profit_total_abs', 'results_metrics.holding_avg',
+                            'loss', 'is_initial_point', 'is_best']
+            perc_multi = 100
+        else:
+            perc_multi = 1
+            base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
+                            'results_metrics.avg_profit', 'results_metrics.median_profit',
+                            'results_metrics.total_profit',
+                            'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
+                            'loss', 'is_initial_point', 'is_best']
         param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()]
         trials = trials[base_metrics + param_metrics]
 
@@ -284,21 +349,23 @@ class HyperoptTools():
         trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
         trials['Epoch'] = trials['Epoch'].astype(str)
         trials['Trades'] = trials['Trades'].astype(str)
+        trials['Median profit'] = trials['Median profit'] * perc_multi
 
         trials['Total profit'] = trials['Total profit'].apply(
-            lambda x: '{:,.8f}'.format(x) if x != 0.0 else ""
+            lambda x: f'{x:,.8f}' if x != 0.0 else ""
         )
         trials['Profit'] = trials['Profit'].apply(
-            lambda x: '{:,.2f}'.format(x) if not isna(x) else ""
+            lambda x: f'{x:,.2f}' if not isna(x) else ""
         )
         trials['Avg profit'] = trials['Avg profit'].apply(
-            lambda x: '{:,.2f}%'.format(x) if not isna(x) else ""
+            lambda x: f'{x * perc_multi:,.2f}%' if not isna(x) else ""
         )
         trials['Avg duration'] = trials['Avg duration'].apply(
-            lambda x: '{:,.1f} m'.format(x) if not isna(x) else ""
+            lambda x: f'{x:,.1f} m' if isinstance(
+                x, float) else f"{x.total_seconds() // 60:,.1f} m" if not isna(x) else ""
         )
         trials['Objective'] = trials['Objective'].apply(
-            lambda x: '{:,.5f}'.format(x) if x != 100000 else ""
+            lambda x: f'{x:,.5f}' if x != 100000 else ""
         )
 
         trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])

freqtrade/optimize/optimize_reports.py

@@ -153,7 +153,7 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
     return tabular_data
 
 
-def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
+def generate_strategy_comparison(all_results: Dict) -> List[Dict]:
     """
     Generate summary per strategy
     :param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies
@@ -194,7 +194,37 @@ def generate_edge_table(results: dict) -> str:
                     floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")  # type: ignore
 
 
+def generate_trading_stats(results: DataFrame) -> Dict[str, Any]:
+    """ Generate overall trade statistics """
+    if len(results) == 0:
+        return {
+            'wins': 0,
+            'losses': 0,
+            'draws': 0,
+            'holding_avg': timedelta(),
+            'winner_holding_avg': timedelta(),
+            'loser_holding_avg': timedelta(),
+        }
+
+    winning_trades = results.loc[results['profit_ratio'] > 0]
+    draw_trades = results.loc[results['profit_ratio'] == 0]
+    losing_trades = results.loc[results['profit_ratio'] < 0]
+
+    return {
+        'wins': len(winning_trades),
+        'losses': len(losing_trades),
+        'draws': len(draw_trades),
+        'holding_avg': (timedelta(minutes=round(results['trade_duration'].mean()))
+                        if not results.empty else timedelta()),
+        'winner_holding_avg': (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
+                               if not winning_trades.empty else timedelta()),
+        'loser_holding_avg': (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
+                              if not losing_trades.empty else timedelta()),
+    }
+
+
 def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
+    """ Generate daily statistics """
     if len(results) == 0:
         return {
             'backtest_best_day': 0,
@@ -204,8 +234,6 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
             'winning_days': 0,
             'draw_days': 0,
             'losing_days': 0,
-            'winner_holding_avg': timedelta(),
-            'loser_holding_avg': timedelta(),
         }
     daily_profit_rel = results.resample('1d', on='close_date')['profit_ratio'].sum()
     daily_profit = results.resample('1d', on='close_date')['profit_abs'].sum().round(10)
@@ -217,9 +245,6 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
     draw_days = sum(daily_profit == 0)
     losing_days = sum(daily_profit < 0)
 
-    winning_trades = results.loc[results['profit_ratio'] > 0]
-    losing_trades = results.loc[results['profit_ratio'] < 0]
-
     return {
         'backtest_best_day': best_rel,
         'backtest_worst_day': worst_rel,
@@ -228,13 +253,148 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
         'winning_days': winning_days,
         'draw_days': draw_days,
         'losing_days': losing_days,
-        'winner_holding_avg': (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
-                               if not winning_trades.empty else timedelta()),
-        'loser_holding_avg': (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
-                              if not losing_trades.empty else timedelta()),
     }
 
 
+def generate_strategy_stats(btdata: Dict[str, DataFrame],
+                            strategy: str,
+                            content: Dict[str, Any],
+                            min_date: Arrow, max_date: Arrow,
+                            market_change: float
+                            ) -> Dict[str, Any]:
+    """
+    :param btdata: Backtest data
+    :param strategy: Strategy name
+    :param content: Backtest result data in the format:
+                    {'results: results, 'config: config}}.
+    :param min_date: Backtest start date
+    :param max_date: Backtest end date
+    :param market_change: float indicating the market change
+    :return: Dictionary containing results per strategy and a stratgy summary.
+    """
+    results: Dict[str, DataFrame] = content['results']
+    if not isinstance(results, DataFrame):
+        return {}
+    config = content['config']
+    max_open_trades = min(config['max_open_trades'], len(btdata.keys()))
+    starting_balance = config['dry_run_wallet']
+    stake_currency = config['stake_currency']
+
+    pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
+                                         starting_balance=starting_balance,
+                                         results=results, skip_nan=False)
+    sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
+                                                   results=results)
+    left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
+                                              starting_balance=starting_balance,
+                                              results=results.loc[results['is_open']],
+                                              skip_nan=True)
+    daily_stats = generate_daily_stats(results)
+    trade_stats = generate_trading_stats(results)
+    best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'],
+                    key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
+    worst_pair = min([pair for pair in pair_results if pair['key'] != 'TOTAL'],
+                     key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
+    results['open_timestamp'] = results['open_date'].astype(int64) // 1e6
+    results['close_timestamp'] = results['close_date'].astype(int64) // 1e6
+
+    backtest_days = (max_date - min_date).days
+    strat_stats = {
+        'trades': results.to_dict(orient='records'),
+        'locks': [lock.to_json() for lock in content['locks']],
+        'best_pair': best_pair,
+        'worst_pair': worst_pair,
+        'results_per_pair': pair_results,
+        'sell_reason_summary': sell_reason_stats,
+        'left_open_trades': left_open_results,
+        'total_trades': len(results),
+        'total_volume': float(results['stake_amount'].sum()),
+        'avg_stake_amount': results['stake_amount'].mean() if len(results) > 0 else 0,
+        'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0,
+        'profit_median': results['profit_ratio'].median() if len(results) > 0 else 0,
+        'profit_total': results['profit_abs'].sum() / starting_balance,
+        'profit_total_abs': results['profit_abs'].sum(),
+        'backtest_start': min_date.datetime,
+        'backtest_start_ts': min_date.int_timestamp * 1000,
+        'backtest_end': max_date.datetime,
+        'backtest_end_ts': max_date.int_timestamp * 1000,
+        'backtest_days': backtest_days,
+
+        'backtest_run_start_ts': content['backtest_start_time'],
+        'backtest_run_end_ts': content['backtest_end_time'],
+
+        'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
+        'market_change': market_change,
+        'pairlist': list(btdata.keys()),
+        'stake_amount': config['stake_amount'],
+        'stake_currency': config['stake_currency'],
+        'stake_currency_decimals': decimals_per_coin(config['stake_currency']),
+        'starting_balance': starting_balance,
+        'dry_run_wallet': starting_balance,
+        'final_balance': content['final_balance'],
+        'max_open_trades': max_open_trades,
+        'max_open_trades_setting': (config['max_open_trades']
+                                    if config['max_open_trades'] != float('inf') else -1),
+        'timeframe': config['timeframe'],
+        'timerange': config.get('timerange', ''),
+        'enable_protections': config.get('enable_protections', False),
+        'strategy_name': strategy,
+        # Parameters relevant for backtesting
+        'stoploss': config['stoploss'],
+        'trailing_stop': config.get('trailing_stop', False),
+        'trailing_stop_positive': config.get('trailing_stop_positive'),
+        'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset', 0.0),
+        'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached', False),
+        'use_custom_stoploss': config.get('use_custom_stoploss', False),
+        'minimal_roi': config['minimal_roi'],
+        'use_sell_signal': config['ask_strategy']['use_sell_signal'],
+        'sell_profit_only': config['ask_strategy']['sell_profit_only'],
+        'sell_profit_offset': config['ask_strategy']['sell_profit_offset'],
+        'ignore_roi_if_buy_signal': config['ask_strategy']['ignore_roi_if_buy_signal'],
+        **daily_stats,
+        **trade_stats
+    }
+
+    try:
+        max_drawdown, _, _, _, _ = calculate_max_drawdown(
+            results, value_col='profit_ratio')
+        drawdown_abs, drawdown_start, drawdown_end, high_val, low_val = calculate_max_drawdown(
+            results, value_col='profit_abs')
+        strat_stats.update({
+            'max_drawdown': max_drawdown,
+            'max_drawdown_abs': drawdown_abs,
+            'drawdown_start': drawdown_start,
+            'drawdown_start_ts': drawdown_start.timestamp() * 1000,
+            'drawdown_end': drawdown_end,
+            'drawdown_end_ts': drawdown_end.timestamp() * 1000,
+
+            'max_drawdown_low': low_val,
+            'max_drawdown_high': high_val,
+        })
+
+        csum_min, csum_max = calculate_csum(results, starting_balance)
+        strat_stats.update({
+            'csum_min': csum_min,
+            'csum_max': csum_max
+        })
+
+    except ValueError:
+        strat_stats.update({
+            'max_drawdown': 0.0,
+            'max_drawdown_abs': 0.0,
+            'max_drawdown_low': 0.0,
+            'max_drawdown_high': 0.0,
+            'drawdown_start': datetime(1970, 1, 1, tzinfo=timezone.utc),
+            'drawdown_start_ts': 0,
+            'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc),
+            'drawdown_end_ts': 0,
+            'csum_min': 0,
+            'csum_max': 0
+        })
+
+    return strat_stats
+
+
 def generate_backtest_stats(btdata: Dict[str, DataFrame],
                             all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]],
                             min_date: Arrow, max_date: Arrow
@@ -245,132 +405,17 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
                      { Strategy: {'results: results, 'config: config}}.
     :param min_date: Backtest start date
     :param max_date: Backtest end date
-    :return:
+    :return: Dictionary containing results per strategy and a stratgy summary.
-    Dictionary containing results per strategy and a stratgy summary.
     """
     result: Dict[str, Any] = {'strategy': {}}
     market_change = calculate_market_change(btdata, 'close')
 
     for strategy, content in all_results.items():
-        results: Dict[str, DataFrame] = content['results']
+        strat_stats = generate_strategy_stats(btdata, strategy, content,
-        if not isinstance(results, DataFrame):
+                                              min_date, max_date, market_change=market_change)
-            continue
-        config = content['config']
-        max_open_trades = min(config['max_open_trades'], len(btdata.keys()))
-        starting_balance = config['dry_run_wallet']
-        stake_currency = config['stake_currency']
-
-        pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
-                                             starting_balance=starting_balance,
-                                             results=results, skip_nan=False)
-        sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
-                                                       results=results)
-        left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
-                                                  starting_balance=starting_balance,
-                                                  results=results.loc[results['is_open']],
-                                                  skip_nan=True)
-        daily_stats = generate_daily_stats(results)
-        best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'],
-                        key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
-        worst_pair = min([pair for pair in pair_results if pair['key'] != 'TOTAL'],
-                         key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
-        results['open_timestamp'] = results['open_date'].astype(int64) // 1e6
-        results['close_timestamp'] = results['close_date'].astype(int64) // 1e6
-
-        backtest_days = (max_date - min_date).days
-        strat_stats = {
-            'trades': results.to_dict(orient='records'),
-            'locks': [lock.to_json() for lock in content['locks']],
-            'best_pair': best_pair,
-            'worst_pair': worst_pair,
-            'results_per_pair': pair_results,
-            'sell_reason_summary': sell_reason_stats,
-            'left_open_trades': left_open_results,
-            'total_trades': len(results),
-            'total_volume': float(results['stake_amount'].sum()),
-            'avg_stake_amount': results['stake_amount'].mean() if len(results) > 0 else 0,
-            'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0,
-            'profit_total': results['profit_abs'].sum() / starting_balance,
-            'profit_total_abs': results['profit_abs'].sum(),
-            'backtest_start': min_date.datetime,
-            'backtest_start_ts': min_date.int_timestamp * 1000,
-            'backtest_end': max_date.datetime,
-            'backtest_end_ts': max_date.int_timestamp * 1000,
-            'backtest_days': backtest_days,
-
-            'backtest_run_start_ts': content['backtest_start_time'],
-            'backtest_run_end_ts': content['backtest_end_time'],
-
-            'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
-            'market_change': market_change,
-            'pairlist': list(btdata.keys()),
-            'stake_amount': config['stake_amount'],
-            'stake_currency': config['stake_currency'],
-            'stake_currency_decimals': decimals_per_coin(config['stake_currency']),
-            'starting_balance': starting_balance,
-            'dry_run_wallet': starting_balance,
-            'final_balance': content['final_balance'],
-            'max_open_trades': max_open_trades,
-            'max_open_trades_setting': (config['max_open_trades']
-                                        if config['max_open_trades'] != float('inf') else -1),
-            'timeframe': config['timeframe'],
-            'timerange': config.get('timerange', ''),
-            'enable_protections': config.get('enable_protections', False),
-            'strategy_name': strategy,
-            # Parameters relevant for backtesting
-            'stoploss': config['stoploss'],
-            'trailing_stop': config.get('trailing_stop', False),
-            'trailing_stop_positive': config.get('trailing_stop_positive'),
-            'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset', 0.0),
-            'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached', False),
-            'use_custom_stoploss': config.get('use_custom_stoploss', False),
-            'minimal_roi': config['minimal_roi'],
-            'use_sell_signal': config['ask_strategy']['use_sell_signal'],
-            'sell_profit_only': config['ask_strategy']['sell_profit_only'],
-            'sell_profit_offset': config['ask_strategy']['sell_profit_offset'],
-            'ignore_roi_if_buy_signal': config['ask_strategy']['ignore_roi_if_buy_signal'],
-            **daily_stats,
-        }
         result['strategy'][strategy] = strat_stats
 
-        try:
+    strategy_results = generate_strategy_comparison(all_results=all_results)
-            max_drawdown, _, _, _, _ = calculate_max_drawdown(
-                results, value_col='profit_ratio')
-            drawdown_abs, drawdown_start, drawdown_end, high_val, low_val = calculate_max_drawdown(
-                results, value_col='profit_abs')
-            strat_stats.update({
-                'max_drawdown': max_drawdown,
-                'max_drawdown_abs': drawdown_abs,
-                'drawdown_start': drawdown_start,
-                'drawdown_start_ts': drawdown_start.timestamp() * 1000,
-                'drawdown_end': drawdown_end,
-                'drawdown_end_ts': drawdown_end.timestamp() * 1000,
-
-                'max_drawdown_low': low_val,
-                'max_drawdown_high': high_val,
-            })
-
-            csum_min, csum_max = calculate_csum(results, starting_balance)
-            strat_stats.update({
-                'csum_min': csum_min,
-                'csum_max': csum_max
-            })
-
-        except ValueError:
-            strat_stats.update({
-                'max_drawdown': 0.0,
-                'max_drawdown_abs': 0.0,
-                'max_drawdown_low': 0.0,
-                'max_drawdown_high': 0.0,
-                'drawdown_start': datetime(1970, 1, 1, tzinfo=timezone.utc),
-                'drawdown_start_ts': 0,
-                'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc),
-                'drawdown_end_ts': 0,
-                'csum_min': 0,
-                'csum_max': 0
-            })
-
-    strategy_results = generate_strategy_metrics(all_results=all_results)
 
     result['strategy_comparison'] = strategy_results
 
@@ -522,37 +567,43 @@ def text_table_add_metrics(strat_results: Dict) -> str:
         return message
 
 
+def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency: str):
+    """
+    Print results for one strategy
+    """
+    # Print results
+    print(f"Result for strategy {strategy}")
+    table = text_table_bt_results(results['results_per_pair'], stake_currency=stake_currency)
+    if isinstance(table, str):
+        print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
+    print(table)
+
+    table = text_table_sell_reason(sell_reason_stats=results['sell_reason_summary'],
+                                   stake_currency=stake_currency)
+    if isinstance(table, str) and len(table) > 0:
+        print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
+    print(table)
+
+    table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency)
+    if isinstance(table, str) and len(table) > 0:
+        print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
+    print(table)
+
+    table = text_table_add_metrics(results)
+    if isinstance(table, str) and len(table) > 0:
+        print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '='))
+    print(table)
+
+    if isinstance(table, str) and len(table) > 0:
+        print('=' * len(table.splitlines()[0]))
+    print()
+
+
 def show_backtest_results(config: Dict, backtest_stats: Dict):
     stake_currency = config['stake_currency']
 
     for strategy, results in backtest_stats['strategy'].items():
-
+        show_backtest_result(strategy, results, stake_currency)
-        # Print results
-        print(f"Result for strategy {strategy}")
-        table = text_table_bt_results(results['results_per_pair'], stake_currency=stake_currency)
-        if isinstance(table, str):
-            print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
-        print(table)
-
-        table = text_table_sell_reason(sell_reason_stats=results['sell_reason_summary'],
-                                       stake_currency=stake_currency)
-        if isinstance(table, str) and len(table) > 0:
-            print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
-        print(table)
-
-        table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency)
-        if isinstance(table, str) and len(table) > 0:
-            print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
-        print(table)
-
-        table = text_table_add_metrics(results)
-        if isinstance(table, str) and len(table) > 0:
-            print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '='))
-        print(table)
-
-        if isinstance(table, str) and len(table) > 0:
-            print('=' * len(table.splitlines()[0]))
-        print()
 
     if len(backtest_stats['strategy']) > 1:
         # Print Strategy summary table

freqtrade/strategy/hyper.py

@@ -5,7 +5,7 @@ This module defines a base class for auto-hyperoptable strategies.
 import logging
 from abc import ABC, abstractmethod
 from contextlib import suppress
-from typing import Any, Dict, Iterator, Optional, Sequence, Tuple, Union
+from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union
 
 from freqtrade.optimize.hyperopt_tools import HyperoptTools
 
@@ -29,6 +29,7 @@ class BaseParameter(ABC):
     default: Any
     value: Any
     in_space: bool = False
+    name: str
 
     def __init__(self, *, default: Any, space: Optional[str] = None,
                  optimize: bool = True, load: bool = True, **kwargs):
@@ -250,6 +251,9 @@ class HyperStrategyMixin(object):
         Initialize hyperoptable strategy mixin.
         """
         self.config = config
+        self.ft_buy_params: List[BaseParameter] = []
+        self.ft_sell_params: List[BaseParameter] = []
+
         self._load_hyper_params(config.get('runmode') == RunMode.HYPEROPT)
 
     def enumerate_parameters(self, category: str = None) -> Iterator[Tuple[str, BaseParameter]]:
@@ -260,15 +264,26 @@ class HyperStrategyMixin(object):
         """
         if category not in ('buy', 'sell', None):
             raise OperationalException('Category must be one of: "buy", "sell", None.')
+
+        if category is None:
+            params = self.ft_buy_params + self.ft_sell_params
+        else:
+            params = getattr(self, f"ft_{category}_params")
+
+        for par in params:
+            yield par.name, par
+
+    def _detect_parameters(self, category: str) -> Iterator[Tuple[str, BaseParameter]]:
+        """ Detect all parameters for 'category' """
         for attr_name in dir(self):
             if not attr_name.startswith('__'):  # Ignore internals, not strictly necessary.
                 attr = getattr(self, attr_name)
                 if issubclass(attr.__class__, BaseParameter):
-                    if (category and attr_name.startswith(category + '_')
+                    if (attr_name.startswith(category + '_')
                             and attr.category is not None and attr.category != category):
                         raise OperationalException(
                             f'Inconclusive parameter name {attr_name}, category: {attr.category}.')
-                    if (category is None or category == attr.category or
+                    if (category == attr.category or
                             (attr_name.startswith(category + '_') and attr.category is None)):
                         yield attr_name, attr
 
@@ -286,9 +301,16 @@ class HyperStrategyMixin(object):
         """
         if not params:
             logger.info(f"No params for {space} found, using default values.")
+        param_container: List[BaseParameter] = getattr(self, f"ft_{space}_params")
 
-        for attr_name, attr in self.enumerate_parameters(space):
+        for attr_name, attr in self._detect_parameters(space):
+            attr.name = attr_name
             attr.in_space = hyperopt and HyperoptTools.has_space(self.config, space)
+            if not attr.category:
+                attr.category = space
+
+            param_container.append(attr)
+
             if params and attr_name in params:
                 if attr.load:
                     attr.value = params[attr_name]
@@ -298,3 +320,16 @@ class HyperStrategyMixin(object):
                                    f'Default value "{attr.value}" used.')
             else:
                 logger.info(f'Strategy Parameter(default): {attr_name} = {attr.value}')
+
+    def get_params_dict(self):
+        """
+        Returns list of Parameters that are not part of the current optimize job
+        """
+        params = {
+            'buy': {},
+            'sell': {}
+        }
+        for name, p in self.enumerate_parameters():
+            if not p.optimize or not p.in_space:
+                params[p.category][name] = p.value
+        return params

tests/commands/test_commands.py

@@ -918,242 +918,244 @@ def test_start_test_pairlist(mocker, caplog, tickers, default_conf, capsys):
                     captured.out)
 
 
-def test_hyperopt_list(mocker, capsys, caplog, hyperopt_results):
+def test_hyperopt_list(mocker, capsys, caplog, saved_hyperopt_results,
+                       saved_hyperopt_results_legacy):
+    for _ in (saved_hyperopt_results, saved_hyperopt_results_legacy):
+        mocker.patch(
+            'freqtrade.optimize.hyperopt_tools.HyperoptTools.load_previous_results',
+            MagicMock(return_value=saved_hyperopt_results_legacy)
+        )
+
+        args = [
+            "hyperopt-list",
+            "--no-details",
+            "--no-color",
+        ]
+        pargs = get_args(args)
+        pargs['config'] = None
+        start_hyperopt_list(pargs)
+        captured = capsys.readouterr()
+        assert all(x in captured.out
+                   for x in [" 1/12", " 2/12", " 3/12", " 4/12", " 5/12",
+                             " 6/12", " 7/12", " 8/12", " 9/12", " 10/12",
+                             " 11/12", " 12/12"])
+        args = [
+            "hyperopt-list",
+            "--best",
+            "--no-details",
+            "--no-color",
+        ]
+        pargs = get_args(args)
+        pargs['config'] = None
+        start_hyperopt_list(pargs)
+        captured = capsys.readouterr()
+        assert all(x in captured.out
+                   for x in [" 1/12", " 5/12", " 10/12"])
+        assert all(x not in captured.out
+                   for x in [" 2/12", " 3/12", " 4/12", " 6/12", " 7/12", " 8/12", " 9/12",
+                             " 11/12", " 12/12"])
+        args = [
+            "hyperopt-list",
+            "--profitable",
+            "--no-details",
+            "--no-color",
+        ]
+        pargs = get_args(args)
+        pargs['config'] = None
+        start_hyperopt_list(pargs)
+        captured = capsys.readouterr()
+        assert all(x in captured.out
+                   for x in [" 2/12", " 10/12"])
+        assert all(x not in captured.out
+                   for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
+                             " 11/12", " 12/12"])
+        args = [
+            "hyperopt-list",
+            "--profitable",
+            "--no-color",
+        ]
+        pargs = get_args(args)
+        pargs['config'] = None
+        start_hyperopt_list(pargs)
+        captured = capsys.readouterr()
+        assert all(x in captured.out
+                   for x in [" 2/12", " 10/12", "Best result:", "Buy hyperspace params",
+                             "Sell hyperspace params", "ROI table", "Stoploss"])
+        assert all(x not in captured.out
+                   for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
+                             " 11/12", " 12/12"])
+        args = [
+            "hyperopt-list",
+            "--no-details",
+            "--no-color",
+            "--min-trades", "20",
+        ]
+        pargs = get_args(args)
+        pargs['config'] = None
+        start_hyperopt_list(pargs)
+        captured = capsys.readouterr()
+        assert all(x in captured.out
+                   for x in [" 3/12", " 6/12", " 7/12", " 9/12", " 11/12"])
+        assert all(x not in captured.out
+                   for x in [" 1/12", " 2/12", " 4/12", " 5/12", " 8/12", " 10/12", " 12/12"])
+        args = [
+            "hyperopt-list",
+            "--profitable",
+            "--no-details",
+            "--no-color",
+            "--max-trades", "20",
+        ]
+        pargs = get_args(args)
+        pargs['config'] = None
+        start_hyperopt_list(pargs)
+        captured = capsys.readouterr()
+        assert all(x in captured.out
+                   for x in [" 2/12", " 10/12"])
+        assert all(x not in captured.out
+                   for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
+                             " 11/12", " 12/12"])
+        args = [
+            "hyperopt-list",
+            "--profitable",
+            "--no-details",
+            "--no-color",
+            "--min-avg-profit", "0.11",
+        ]
+        pargs = get_args(args)
+        pargs['config'] = None
+        start_hyperopt_list(pargs)
+        captured = capsys.readouterr()
+        assert all(x in captured.out
+                   for x in [" 2/12"])
+        assert all(x not in captured.out
+                   for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
+                             " 10/12", " 11/12", " 12/12"])
+        args = [
+            "hyperopt-list",
+            "--no-details",
+            "--no-color",
+            "--max-avg-profit", "0.10",
+        ]
+        pargs = get_args(args)
+        pargs['config'] = None
+        start_hyperopt_list(pargs)
+        captured = capsys.readouterr()
+        assert all(x in captured.out
+                   for x in [" 1/12", " 3/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
+                             " 11/12"])
+        assert all(x not in captured.out
+                   for x in [" 2/12", " 4/12", " 10/12", " 12/12"])
+        args = [
+            "hyperopt-list",
+            "--no-details",
+            "--no-color",
+            "--min-total-profit", "0.4",
+        ]
+        pargs = get_args(args)
+        pargs['config'] = None
+        start_hyperopt_list(pargs)
+        captured = capsys.readouterr()
+        assert all(x in captured.out
+                   for x in [" 10/12"])
+        assert all(x not in captured.out
+                   for x in [" 1/12", " 2/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12",
+                             " 9/12", " 11/12", " 12/12"])
+        args = [
+            "hyperopt-list",
+            "--no-details",
+            "--no-color",
+            "--max-total-profit", "0.4",
+        ]
+        pargs = get_args(args)
+        pargs['config'] = None
+        start_hyperopt_list(pargs)
+        captured = capsys.readouterr()
+        assert all(x in captured.out
+                   for x in [" 1/12", " 2/12", " 3/12", " 5/12", " 6/12", " 7/12", " 8/12",
+                             " 9/12", " 11/12"])
+        assert all(x not in captured.out
+                   for x in [" 4/12", " 10/12", " 12/12"])
+        args = [
+            "hyperopt-list",
+            "--no-details",
+            "--no-color",
+            "--min-objective", "0.1",
+        ]
+        pargs = get_args(args)
+        pargs['config'] = None
+        start_hyperopt_list(pargs)
+        captured = capsys.readouterr()
+        assert all(x in captured.out
+                   for x in [" 10/12"])
+        assert all(x not in captured.out
+                   for x in [" 1/12", " 2/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12",
+                             " 9/12", " 11/12", " 12/12"])
+        args = [
+            "hyperopt-list",
+            "--no-details",
+            "--max-objective", "0.1",
+        ]
+        pargs = get_args(args)
+        pargs['config'] = None
+        start_hyperopt_list(pargs)
+        captured = capsys.readouterr()
+        assert all(x in captured.out
+                   for x in [" 1/12", " 2/12", " 3/12", " 5/12", " 6/12", " 7/12", " 8/12",
+                             " 9/12", " 11/12"])
+        assert all(x not in captured.out
+                   for x in [" 4/12", " 10/12", " 12/12"])
+        args = [
+            "hyperopt-list",
+            "--profitable",
+            "--no-details",
+            "--no-color",
+            "--min-avg-time", "2000",
+        ]
+        pargs = get_args(args)
+        pargs['config'] = None
+        start_hyperopt_list(pargs)
+        captured = capsys.readouterr()
+        assert all(x in captured.out
+                   for x in [" 10/12"])
+        assert all(x not in captured.out
+                   for x in [" 1/12", " 2/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12",
+                             " 8/12", " 9/12", " 11/12", " 12/12"])
+        args = [
+            "hyperopt-list",
+            "--no-details",
+            "--no-color",
+            "--max-avg-time", "1500",
+        ]
+        pargs = get_args(args)
+        pargs['config'] = None
+        start_hyperopt_list(pargs)
+        captured = capsys.readouterr()
+        assert all(x in captured.out
+                   for x in [" 2/12", " 6/12"])
+        assert all(x not in captured.out
+                   for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 7/12", " 8/12"
+                             " 9/12", " 10/12", " 11/12", " 12/12"])
+        args = [
+            "hyperopt-list",
+            "--no-details",
+            "--no-color",
+            "--export-csv", "test_file.csv",
+        ]
+        pargs = get_args(args)
+        pargs['config'] = None
+        start_hyperopt_list(pargs)
+        captured = capsys.readouterr()
+        log_has("CSV file created: test_file.csv", caplog)
+        f = Path("test_file.csv")
+        assert 'Best,1,2,-1.25%,-1.2222,-0.00125625,,-2.51,"3,930.0 m",0.43662' in f.read_text()
+        assert f.is_file()
+        f.unlink()
+
+
+def test_hyperopt_show(mocker, capsys, saved_hyperopt_results):
     mocker.patch(
         'freqtrade.optimize.hyperopt_tools.HyperoptTools.load_previous_results',
-        MagicMock(return_value=hyperopt_results)
+        MagicMock(return_value=saved_hyperopt_results)
-    )
-
-    args = [
-        "hyperopt-list",
-        "--no-details",
-        "--no-color",
-    ]
-    pargs = get_args(args)
-    pargs['config'] = None
-    start_hyperopt_list(pargs)
-    captured = capsys.readouterr()
-    assert all(x in captured.out
-               for x in [" 1/12", " 2/12", " 3/12", " 4/12", " 5/12",
-                         " 6/12", " 7/12", " 8/12", " 9/12", " 10/12",
-                         " 11/12", " 12/12"])
-    args = [
-        "hyperopt-list",
-        "--best",
-        "--no-details",
-        "--no-color",
-    ]
-    pargs = get_args(args)
-    pargs['config'] = None
-    start_hyperopt_list(pargs)
-    captured = capsys.readouterr()
-    assert all(x in captured.out
-               for x in [" 1/12", " 5/12", " 10/12"])
-    assert all(x not in captured.out
-               for x in [" 2/12", " 3/12", " 4/12", " 6/12", " 7/12", " 8/12", " 9/12",
-                         " 11/12", " 12/12"])
-    args = [
-        "hyperopt-list",
-        "--profitable",
-        "--no-details",
-        "--no-color",
-    ]
-    pargs = get_args(args)
-    pargs['config'] = None
-    start_hyperopt_list(pargs)
-    captured = capsys.readouterr()
-    assert all(x in captured.out
-               for x in [" 2/12", " 10/12"])
-    assert all(x not in captured.out
-               for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
-                         " 11/12", " 12/12"])
-    args = [
-        "hyperopt-list",
-        "--profitable",
-        "--no-color",
-    ]
-    pargs = get_args(args)
-    pargs['config'] = None
-    start_hyperopt_list(pargs)
-    captured = capsys.readouterr()
-    assert all(x in captured.out
-               for x in [" 2/12", " 10/12", "Best result:", "Buy hyperspace params",
-                         "Sell hyperspace params", "ROI table", "Stoploss"])
-    assert all(x not in captured.out
-               for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
-                         " 11/12", " 12/12"])
-    args = [
-        "hyperopt-list",
-        "--no-details",
-        "--no-color",
-        "--min-trades", "20",
-    ]
-    pargs = get_args(args)
-    pargs['config'] = None
-    start_hyperopt_list(pargs)
-    captured = capsys.readouterr()
-    assert all(x in captured.out
-               for x in [" 3/12", " 6/12", " 7/12", " 9/12", " 11/12"])
-    assert all(x not in captured.out
-               for x in [" 1/12", " 2/12", " 4/12", " 5/12", " 8/12", " 10/12", " 12/12"])
-    args = [
-        "hyperopt-list",
-        "--profitable",
-        "--no-details",
-        "--no-color",
-        "--max-trades", "20",
-    ]
-    pargs = get_args(args)
-    pargs['config'] = None
-    start_hyperopt_list(pargs)
-    captured = capsys.readouterr()
-    assert all(x in captured.out
-               for x in [" 2/12", " 10/12"])
-    assert all(x not in captured.out
-               for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
-                         " 11/12", " 12/12"])
-    args = [
-        "hyperopt-list",
-        "--profitable",
-        "--no-details",
-        "--no-color",
-        "--min-avg-profit", "0.11",
-    ]
-    pargs = get_args(args)
-    pargs['config'] = None
-    start_hyperopt_list(pargs)
-    captured = capsys.readouterr()
-    assert all(x in captured.out
-               for x in [" 2/12"])
-    assert all(x not in captured.out
-               for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
-                         " 10/12", " 11/12", " 12/12"])
-    args = [
-        "hyperopt-list",
-        "--no-details",
-        "--no-color",
-        "--max-avg-profit", "0.10",
-    ]
-    pargs = get_args(args)
-    pargs['config'] = None
-    start_hyperopt_list(pargs)
-    captured = capsys.readouterr()
-    assert all(x in captured.out
-               for x in [" 1/12", " 3/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
-                         " 11/12"])
-    assert all(x not in captured.out
-               for x in [" 2/12", " 4/12", " 10/12", " 12/12"])
-    args = [
-        "hyperopt-list",
-        "--no-details",
-        "--no-color",
-        "--min-total-profit", "0.4",
-    ]
-    pargs = get_args(args)
-    pargs['config'] = None
-    start_hyperopt_list(pargs)
-    captured = capsys.readouterr()
-    assert all(x in captured.out
-               for x in [" 10/12"])
-    assert all(x not in captured.out
-               for x in [" 1/12", " 2/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12",
-                         " 9/12", " 11/12", " 12/12"])
-    args = [
-        "hyperopt-list",
-        "--no-details",
-        "--no-color",
-        "--max-total-profit", "0.4",
-    ]
-    pargs = get_args(args)
-    pargs['config'] = None
-    start_hyperopt_list(pargs)
-    captured = capsys.readouterr()
-    assert all(x in captured.out
-               for x in [" 1/12", " 2/12", " 3/12", " 5/12", " 6/12", " 7/12", " 8/12",
-                         " 9/12", " 11/12"])
-    assert all(x not in captured.out
-               for x in [" 4/12", " 10/12", " 12/12"])
-    args = [
-        "hyperopt-list",
-        "--no-details",
-        "--no-color",
-        "--min-objective", "0.1",
-    ]
-    pargs = get_args(args)
-    pargs['config'] = None
-    start_hyperopt_list(pargs)
-    captured = capsys.readouterr()
-    assert all(x in captured.out
-               for x in [" 10/12"])
-    assert all(x not in captured.out
-               for x in [" 1/12", " 2/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12",
-                         " 9/12", " 11/12", " 12/12"])
-    args = [
-        "hyperopt-list",
-        "--no-details",
-        "--max-objective", "0.1",
-    ]
-    pargs = get_args(args)
-    pargs['config'] = None
-    start_hyperopt_list(pargs)
-    captured = capsys.readouterr()
-    assert all(x in captured.out
-               for x in [" 1/12", " 2/12", " 3/12", " 5/12", " 6/12", " 7/12", " 8/12",
-                         " 9/12", " 11/12"])
-    assert all(x not in captured.out
-               for x in [" 4/12", " 10/12", " 12/12"])
-    args = [
-        "hyperopt-list",
-        "--profitable",
-        "--no-details",
-        "--no-color",
-        "--min-avg-time", "2000",
-    ]
-    pargs = get_args(args)
-    pargs['config'] = None
-    start_hyperopt_list(pargs)
-    captured = capsys.readouterr()
-    assert all(x in captured.out
-               for x in [" 10/12"])
-    assert all(x not in captured.out
-               for x in [" 1/12", " 2/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12",
-                         " 8/12", " 9/12", " 11/12", " 12/12"])
-    args = [
-        "hyperopt-list",
-        "--no-details",
-        "--no-color",
-        "--max-avg-time", "1500",
-    ]
-    pargs = get_args(args)
-    pargs['config'] = None
-    start_hyperopt_list(pargs)
-    captured = capsys.readouterr()
-    assert all(x in captured.out
-               for x in [" 2/12", " 6/12"])
-    assert all(x not in captured.out
-               for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 7/12", " 8/12"
-                         " 9/12", " 10/12", " 11/12", " 12/12"])
-    args = [
-        "hyperopt-list",
-        "--no-details",
-        "--no-color",
-        "--export-csv", "test_file.csv",
-    ]
-    pargs = get_args(args)
-    pargs['config'] = None
-    start_hyperopt_list(pargs)
-    captured = capsys.readouterr()
-    log_has("CSV file created: test_file.csv", caplog)
-    f = Path("test_file.csv")
-    assert 'Best,1,2,-1.25%,-1.2222,-0.00125625,,-2.51,"3,930.0 m",0.43662' in f.read_text()
-    assert f.is_file()
-    f.unlink()
-
-
-def test_hyperopt_show(mocker, capsys, hyperopt_results):
-    mocker.patch(
-        'freqtrade.optimize.hyperopt_tools.HyperoptTools.load_previous_results',
-        MagicMock(return_value=hyperopt_results)
     )
 
     args = [

tests/conftest.py

@@ -3,7 +3,7 @@ import json
 import logging
 import re
 from copy import deepcopy
-from datetime import datetime
+from datetime import datetime, timedelta
 from functools import reduce
 from pathlib import Path
 from unittest.mock import MagicMock, Mock, PropertyMock
@@ -1778,7 +1778,7 @@ def open_trade():
 
 
 @pytest.fixture
-def hyperopt_results():
+def saved_hyperopt_results_legacy():
     return [
         {
             'loss': 0.4366182531161519,
@@ -1907,3 +1907,136 @@ def hyperopt_results():
             'is_best': False
             }
     ]
+
+
+@pytest.fixture
+def saved_hyperopt_results():
+    return [
+        {
+            'loss': 0.4366182531161519,
+            'params_dict': {
+                'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1190, 'roi_t2': 541, 'roi_t3': 408, 'roi_p1': 0.026035863879169705, 'roi_p2': 0.12508730043628782, 'roi_p3': 0.27766427921605896, 'stoploss': -0.2562930402099556},  # noqa: E501
+            'params_details': {'buy': {'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4287874435315165, 408: 0.15112316431545753, 949: 0.026035863879169705, 2139: 0}, 'stoploss': {'stoploss': -0.2562930402099556}},  # noqa: E501
+            'results_metrics': {'total_trades': 2, 'wins': 0, 'draws': 0, 'losses': 2, 'profit_mean': -0.01254995, 'profit_median': -0.012222, 'profit_total': -0.00125625, 'profit_total_abs': -2.50999, 'holding_avg': timedelta(minutes=3930.0)},  # noqa: E501
+            'results_explanation': '     2 trades. Avg profit  -1.25%. Total profit -0.00125625 BTC (  -2.51Σ%). Avg duration 3930.0 min.',  # noqa: E501
+            'total_profit': -0.00125625,
+            'current_epoch': 1,
+            'is_initial_point': True,
+            'is_best': True
+        }, {
+            'loss': 20.0,
+            'params_dict': {
+                'mfi-value': 17, 'fastd-value': 38, 'adx-value': 48, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 96, 'sell-fastd-value': 68, 'sell-adx-value': 63, 'sell-rsi-value': 81, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 334, 'roi_t2': 683, 'roi_t3': 140, 'roi_p1': 0.06403981740598495, 'roi_p2': 0.055519840060645045, 'roi_p3': 0.3253712811342459, 'stoploss': -0.338070047333259},  # noqa: E501
+            'params_details': {
+                'buy': {'mfi-value': 17, 'fastd-value': 38, 'adx-value': 48, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'},  # noqa: E501
+                'sell': {'sell-mfi-value': 96, 'sell-fastd-value': 68, 'sell-adx-value': 63, 'sell-rsi-value': 81, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'},  # noqa: E501
+                'roi': {0: 0.4449309386008759, 140: 0.11955965746663, 823: 0.06403981740598495, 1157: 0},  # noqa: E501
+                'stoploss': {'stoploss': -0.338070047333259}},
+            'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 0, 'losses': 1, 'profit_mean': 0.012357, 'profit_median': -0.012222, 'profit_total': 6.185e-05, 'profit_total_abs': 0.12357, 'holding_avg': timedelta(minutes=1200.0)},  # noqa: E501
+            'results_explanation': '     1 trades. Avg profit   0.12%. Total profit  0.00006185 BTC (   0.12Σ%). Avg duration 1200.0 min.',  # noqa: E501
+            'total_profit': 6.185e-05,
+            'current_epoch': 2,
+            'is_initial_point': True,
+            'is_best': False
+        }, {
+            'loss': 14.241196856510731,
+            'params_dict': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 889, 'roi_t2': 533, 'roi_t3': 263, 'roi_p1': 0.04759065393663096, 'roi_p2': 0.1488819964638463, 'roi_p3': 0.4102801822104605, 'stoploss': -0.05394588767607611},  # noqa: E501
+            'params_details': {'buy': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.6067528326109377, 263: 0.19647265040047726, 796: 0.04759065393663096, 1685: 0}, 'stoploss': {'stoploss': -0.05394588767607611}},  # noqa: E501
+            'results_metrics': {'total_trades': 621, 'wins': 320, 'draws': 0, 'losses': 301, 'profit_mean': -0.043883302093397747, 'profit_median': -0.012222, 'profit_total': -0.13639474, 'profit_total_abs': -272.515306, 'holding_avg': timedelta(minutes=1691.207729468599)},  # noqa: E501
+            'results_explanation': '   621 trades. Avg profit  -0.44%. Total profit -0.13639474 BTC (-272.52Σ%). Avg duration 1691.2 min.',  # noqa: E501
+            'total_profit': -0.13639474,
+            'current_epoch': 3,
+            'is_initial_point': True,
+            'is_best': False
+        }, {
+            'loss': 100000,
+            'params_dict': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1402, 'roi_t2': 676, 'roi_t3': 215, 'roi_p1': 0.06264755784937427, 'roi_p2': 0.14258587851894644, 'roi_p3': 0.20671291201040828, 'stoploss': -0.11818343570194478},  # noqa: E501
+            'params_details': {'buy': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.411946348378729, 215: 0.2052334363683207, 891: 0.06264755784937427, 2293: 0}, 'stoploss': {'stoploss': -0.11818343570194478}},  # noqa: E501
+            'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit': 0.0, 'holding_avg': timedelta()},  # noqa: E501
+            'results_explanation': '     0 trades. Avg profit    nan%. Total profit  0.00000000 BTC (   0.00Σ%). Avg duration   nan min.',  # noqa: E501
+            'total_profit': 0, 'current_epoch': 4, 'is_initial_point': True, 'is_best': False
+        }, {
+            'loss': 0.22195522184191518,
+            'params_dict': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 1269, 'roi_t2': 601, 'roi_t3': 444, 'roi_p1': 0.07280999507931168, 'roi_p2': 0.08946698095898986, 'roi_p3': 0.1454876733325284, 'stoploss': -0.18181041180901014},   # noqa: E501
+            'params_details': {'buy': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3077646493708299, 444: 0.16227697603830155, 1045: 0.07280999507931168, 2314: 0}, 'stoploss': {'stoploss': -0.18181041180901014}},  # noqa: E501
+            'results_metrics': {'total_trades': 14, 'wins': 6, 'draws': 0, 'losses': 8, 'profit_mean': -0.003539515, 'profit_median': -0.012222, 'profit_total': -0.002480140000000001, 'profit_total_abs': -4.955321, 'holding_avg': timedelta(minutes=3402.8571428571427)},  # noqa: E501
+            'results_explanation': '    14 trades. Avg profit  -0.35%. Total profit -0.00248014 BTC (  -4.96Σ%). Avg duration 3402.9 min.',  # noqa: E501
+            'total_profit': -0.002480140000000001,
+            'current_epoch': 5,
+            'is_initial_point': True,
+            'is_best': True
+        }, {
+            'loss': 0.545315889154162,
+            'params_dict': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower', 'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 319, 'roi_t2': 556, 'roi_t3': 216, 'roi_p1': 0.06251955472249589, 'roi_p2': 0.11659519602202795, 'roi_p3': 0.0953744132197762, 'stoploss': -0.024551752215582423},  # noqa: E501
+            'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.2744891639643, 216: 0.17911475074452382, 772: 0.06251955472249589, 1091: 0}, 'stoploss': {'stoploss': -0.024551752215582423}},  # noqa: E501
+            'results_metrics': {'total_trades': 39, 'wins': 20, 'draws': 0, 'losses': 19, 'profit_mean': -0.0021400679487179478, 'profit_median': -0.012222, 'profit_total': -0.0041773, 'profit_total_abs': -8.346264999999997, 'holding_avg': timedelta(minutes=636.9230769230769)},  # noqa: E501
+            'results_explanation': '    39 trades. Avg profit  -0.21%. Total profit -0.00417730 BTC (  -8.35Σ%). Avg duration 636.9 min.',  # noqa: E501
+            'total_profit': -0.0041773,
+            'current_epoch': 6,
+            'is_initial_point': True,
+            'is_best': False
+        }, {
+            'loss': 4.713497421432944,
+            'params_dict': {'mfi-value': 13, 'fastd-value': 41, 'adx-value': 21, 'rsi-value': 29, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower', 'sell-mfi-value': 99, 'sell-fastd-value': 60, 'sell-adx-value': 81, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 771, 'roi_t2': 620, 'roi_t3': 145, 'roi_p1': 0.0586919200378493, 'roi_p2': 0.04984118697312542, 'roi_p3': 0.37521058680247044, 'stoploss': -0.14613268022709905},  # noqa: E501
+            'params_details': {
+                'buy': {'mfi-value': 13, 'fastd-value': 41, 'adx-value': 21, 'rsi-value': 29, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 99, 'sell-fastd-value': 60, 'sell-adx-value': 81, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.4837436938134452, 145: 0.10853310701097472, 765: 0.0586919200378493, 1536: 0},  # noqa: E501
+                'stoploss': {'stoploss': -0.14613268022709905}},  # noqa: E501
+            'results_metrics': {'total_trades': 318, 'wins': 100, 'draws': 0, 'losses': 218, 'profit_mean': -0.0039833954716981146, 'profit_median': -0.012222, 'profit_total': -0.06339929, 'profit_total_abs': -126.67197600000004, 'holding_avg': timedelta(minutes=3140.377358490566)},  # noqa: E501
+            'results_explanation': '   318 trades. Avg profit  -0.40%. Total profit -0.06339929 BTC (-126.67Σ%). Avg duration 3140.4 min.',  # noqa: E501
+            'total_profit': -0.06339929,
+            'current_epoch': 7,
+            'is_initial_point': True,
+            'is_best': False
+        }, {
+            'loss': 20.0,  # noqa: E501
+            'params_dict': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal', 'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 1149, 'roi_t2': 375, 'roi_t3': 289, 'roi_p1': 0.05571820757172588, 'roi_p2': 0.0606240398618907, 'roi_p3': 0.1729012220156157, 'stoploss': -0.1588514289110401},  # noqa: E501
+            'params_details': {'buy': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.2892434694492323, 289: 0.11634224743361658, 664: 0.05571820757172588, 1813: 0}, 'stoploss': {'stoploss': -0.1588514289110401}},  # noqa: E501
+            'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 1, 'losses': 0, 'profit_mean': 0.0, 'profit_median': 0.0, 'profit_total': 0.0, 'profit_total_abs': 0.0, 'holding_avg': timedelta(minutes=5340.0)},  # noqa: E501
+            'results_explanation': '     1 trades. Avg profit   0.00%. Total profit  0.00000000 BTC (   0.00Σ%). Avg duration 5340.0 min.',  # noqa: E501
+            'total_profit': 0.0,
+            'current_epoch': 8,
+            'is_initial_point': True,
+            'is_best': False
+        }, {
+            'loss': 2.4731817780991223,
+            'params_dict': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1012, 'roi_t2': 584, 'roi_t3': 422, 'roi_p1': 0.036764323603472565, 'roi_p2': 0.10335480573205287, 'roi_p3': 0.10322347377503042, 'stoploss': -0.2780610808108503},  # noqa: E501
+            'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.2433426031105559, 422: 0.14011912933552545, 1006: 0.036764323603472565, 2018: 0}, 'stoploss': {'stoploss': -0.2780610808108503}},  # noqa: E501
+            'results_metrics': {'total_trades': 229, 'wins': 150, 'draws': 0, 'losses': 79, 'profit_mean': -0.0038433433624454144, 'profit_median': -0.012222, 'profit_total': -0.044050070000000004, 'profit_total_abs': -88.01256299999999, 'holding_avg': timedelta(minutes=6505.676855895196)},  # noqa: E501
+            'results_explanation': '   229 trades. Avg profit  -0.38%. Total profit -0.04405007 BTC ( -88.01Σ%). Avg duration 6505.7 min.',  # noqa: E501
+            'total_profit': -0.044050070000000004,  # noqa: E501
+            'current_epoch': 9,
+            'is_initial_point': True,
+            'is_best': False
+        }, {
+            'loss': -0.2604606005845212,  # noqa: E501
+            'params_dict': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 792, 'roi_t2': 464, 'roi_t3': 215, 'roi_p1': 0.04594053535385903, 'roi_p2': 0.09623192684243963, 'roi_p3': 0.04428219070850663, 'stoploss': -0.16992287161634415},  # noqa: E501
+            'params_details': {'buy': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.18645465290480528, 215: 0.14217246219629864, 679: 0.04594053535385903, 1471: 0}, 'stoploss': {'stoploss': -0.16992287161634415}},  # noqa: E501
+            'results_metrics': {'total_trades': 4, 'wins': 0, 'draws': 0, 'losses': 4, 'profit_mean': 0.001080385, 'profit_median': -0.012222, 'profit_total': 0.00021629, 'profit_total_abs': 0.432154, 'holding_avg': timedelta(minutes=2850.0)},  # noqa: E501
+            'results_explanation': '     4 trades. Avg profit   0.11%. Total profit  0.00021629 BTC (   0.43Σ%). Avg duration 2850.0 min.',  # noqa: E501
+            'total_profit': 0.00021629,
+            'current_epoch': 10,
+            'is_initial_point': True,
+            'is_best': True
+        }, {
+            'loss': 4.876465945994304,  # noqa: E501
+            'params_dict': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower', 'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 579, 'roi_t2': 614, 'roi_t3': 273, 'roi_p1': 0.05307643172744114, 'roi_p2': 0.1352282078262871, 'roi_p3': 0.1913307406325751, 'stoploss': -0.25728526022513887},  # noqa: E501
+            'params_details': {'buy': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3796353801863034, 273: 0.18830463955372825, 887: 0.05307643172744114, 1466: 0}, 'stoploss': {'stoploss': -0.25728526022513887}},  # noqa: E501
+            # New Hyperopt mode!
+            'results_metrics': {'total_trades': 117, 'wins': 67, 'draws': 0, 'losses': 50, 'profit_mean': -0.012698609145299145, 'profit_median': -0.012222, 'profit_total': -0.07436117, 'profit_total_abs': -148.573727, 'holding_avg': timedelta(minutes=4282.5641025641025)},  # noqa: E501
+            'results_explanation': '   117 trades. Avg profit  -1.27%. Total profit -0.07436117 BTC (-148.57Σ%). Avg duration 4282.6 min.',  # noqa: E501
+            'total_profit': -0.07436117,
+            'current_epoch': 11,
+            'is_initial_point': True,
+            'is_best': False
+        }, {
+            'loss': 100000,
+            'params_dict': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1156, 'roi_t2': 581, 'roi_t3': 408, 'roi_p1': 0.06860454019988212, 'roi_p2': 0.12473718444931989, 'roi_p3': 0.2896360635226823, 'stoploss': -0.30889015124682806},  # noqa: E501
+            'params_details': {'buy': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4829777881718843, 408: 0.19334172464920202, 989: 0.06860454019988212, 2145: 0}, 'stoploss': {'stoploss': -0.30889015124682806}},  # noqa: E501
+            'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit_total_abs': 0.0, 'holding_avg': timedelta()},  # noqa: E501
+            'results_explanation': '     0 trades. Avg profit    nan%. Total profit  0.00000000 BTC (   0.00Σ%). Avg duration   nan min.',  # noqa: E501
+            'total_profit': 0,
+            'current_epoch': 12,
+            'is_initial_point': True,
+            'is_best': False
+            }
+    ]

tests/optimize/test_backtest_detail.py

@@ -501,13 +501,14 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
     # Dummy data as we mock the analyze functions
     data_processed = {pair: frame.copy()}
     min_date, max_date = get_timerange({pair: frame})
-    results = backtesting.backtest(
+    result = backtesting.backtest(
         processed=data_processed,
         start_date=min_date,
         end_date=max_date,
         max_open_trades=10,
     )
 
+    results = result['results']
     assert len(results) == len(data.trades)
     assert round(results["profit_ratio"].sum(), 3) == round(data.profit_perc, 3)
 

tests/optimize/test_backtesting.py

@@ -514,13 +514,14 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
                              timerange=timerange)
     processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
     min_date, max_date = get_timerange(processed)
-    results = backtesting.backtest(
+    result = backtesting.backtest(
         processed=processed,
         start_date=min_date,
         end_date=max_date,
         max_open_trades=10,
         position_stacking=False,
     )
+    results = result['results']
     assert not results.empty
     assert len(results) == 2
 
@@ -583,8 +584,8 @@ def test_backtest_1min_timeframe(default_conf, fee, mocker, testdatadir) -> None
         max_open_trades=1,
         position_stacking=False,
     )
-    assert not results.empty
+    assert not results['results'].empty
-    assert len(results) == 1
+    assert len(results['results']) == 1
 
 
 def test_processed(default_conf, mocker, testdatadir) -> None:
@@ -623,7 +624,7 @@ def test_backtest_pricecontours_protections(default_conf, fee, mocker, testdatad
     # While buy-signals are unrealistic, running backtesting
     # over and over again should not cause different results
     for [contour, numres] in tests:
-        assert len(simple_backtest(default_conf, contour, mocker, testdatadir)) == numres
+        assert len(simple_backtest(default_conf, contour, mocker, testdatadir)['results']) == numres
 
 
 @pytest.mark.parametrize('protections,contour,expected', [
@@ -648,7 +649,7 @@ def test_backtest_pricecontours(default_conf, fee, mocker, testdatadir,
     mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
     # While buy-signals are unrealistic, running backtesting
     # over and over again should not cause different results
-    assert len(simple_backtest(default_conf, contour, mocker, testdatadir)) == expected
+    assert len(simple_backtest(default_conf, contour, mocker, testdatadir)['results']) == expected
 
 
 def test_backtest_clash_buy_sell(mocker, default_conf, testdatadir):
@@ -662,8 +663,8 @@ def test_backtest_clash_buy_sell(mocker, default_conf, testdatadir):
     backtesting = Backtesting(default_conf)
     backtesting.strategy.advise_buy = fun  # Override
     backtesting.strategy.advise_sell = fun  # Override
-    results = backtesting.backtest(**backtest_conf)
+    result = backtesting.backtest(**backtest_conf)
-    assert results.empty
+    assert result['results'].empty
 
 
 def test_backtest_only_sell(mocker, default_conf, testdatadir):
@@ -677,8 +678,8 @@ def test_backtest_only_sell(mocker, default_conf, testdatadir):
     backtesting = Backtesting(default_conf)
     backtesting.strategy.advise_buy = fun  # Override
     backtesting.strategy.advise_sell = fun  # Override
-    results = backtesting.backtest(**backtest_conf)
+    result = backtesting.backtest(**backtest_conf)
-    assert results.empty
+    assert result['results'].empty
 
 
 def test_backtest_alternate_buy_sell(default_conf, fee, mocker, testdatadir):
@@ -690,10 +691,11 @@ def test_backtest_alternate_buy_sell(default_conf, fee, mocker, testdatadir):
     backtesting = Backtesting(default_conf)
     backtesting.strategy.advise_buy = _trend_alternate  # Override
     backtesting.strategy.advise_sell = _trend_alternate  # Override
-    results = backtesting.backtest(**backtest_conf)
+    result = backtesting.backtest(**backtest_conf)
     # 200 candles in backtest data
     # won't buy on first (shifted by 1)
     # 100 buys signals
+    results = result['results']
     assert len(results) == 100
     # One trade was force-closed at the end
     assert len(results.loc[results['is_open']]) == 0
@@ -745,9 +747,9 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
     results = backtesting.backtest(**backtest_conf)
 
     # Make sure we have parallel trades
-    assert len(evaluate_result_multi(results, '5m', 2)) > 0
+    assert len(evaluate_result_multi(results['results'], '5m', 2)) > 0
     # make sure we don't have trades with more than configured max_open_trades
-    assert len(evaluate_result_multi(results, '5m', 3)) == 0
+    assert len(evaluate_result_multi(results['results'], '5m', 3)) == 0
 
     backtest_conf = {
         'processed': processed,
@@ -757,7 +759,7 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
         'position_stacking': False,
     }
     results = backtesting.backtest(**backtest_conf)
-    assert len(evaluate_result_multi(results, '5m', 1)) == 0
+    assert len(evaluate_result_multi(results['results'], '5m', 1)) == 0
 
 
 def test_backtest_start_timerange(default_conf, mocker, caplog, testdatadir):
@@ -802,8 +804,19 @@ def test_backtest_start_timerange(default_conf, mocker, caplog, testdatadir):
 @pytest.mark.filterwarnings("ignore:deprecated")
 def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
 
+    default_conf['ask_strategy'].update({
+        "use_sell_signal": True,
+        "sell_profit_only": False,
+        "sell_profit_offset": 0.0,
+        "ignore_roi_if_buy_signal": False,
+    })
     patch_exchange(mocker)
-    backtestmock = MagicMock(return_value=pd.DataFrame(columns=BT_DATA_COLUMNS))
+    backtestmock = MagicMock(return_value={
+        'results': pd.DataFrame(columns=BT_DATA_COLUMNS),
+        'config': default_conf,
+        'locks': [],
+        'final_balance': 1000,
+        })
     mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
                  PropertyMock(return_value=['UNITTEST/BTC']))
     mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)
@@ -817,7 +830,7 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
                           text_table_strategy=strattable_mock,
                           generate_pair_metrics=MagicMock(),
                           generate_sell_reason_stats=sell_reason_mock,
-                          generate_strategy_metrics=strat_summary,
+                          generate_strategy_comparison=strat_summary,
                           generate_daily_stats=MagicMock(),
                           )
     patched_configuration_load_config_file(mocker, default_conf)
@@ -865,41 +878,58 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
 
 @pytest.mark.filterwarnings("ignore:deprecated")
 def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdatadir, capsys):
-
+    default_conf['ask_strategy'].update({
+        "use_sell_signal": True,
+        "sell_profit_only": False,
+        "sell_profit_offset": 0.0,
+        "ignore_roi_if_buy_signal": False,
+    })
     patch_exchange(mocker)
+    result1 = pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC'],
+                            'profit_ratio': [0.0, 0.0],
+                            'profit_abs': [0.0, 0.0],
+                            'open_date': pd.to_datetime(['2018-01-29 18:40:00',
+                                                         '2018-01-30 03:30:00', ], utc=True
+                                                        ),
+                            'close_date': pd.to_datetime(['2018-01-29 20:45:00',
+                                                          '2018-01-30 05:35:00', ], utc=True),
+                            'trade_duration': [235, 40],
+                            'is_open': [False, False],
+                            'stake_amount': [0.01, 0.01],
+                            'open_rate': [0.104445, 0.10302485],
+                            'close_rate': [0.104969, 0.103541],
+                            'sell_reason': [SellType.ROI, SellType.ROI]
+                            })
+    result2 = pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC', 'ETH/BTC'],
+                            'profit_ratio': [0.03, 0.01, 0.1],
+                            'profit_abs': [0.01, 0.02, 0.2],
+                            'open_date': pd.to_datetime(['2018-01-29 18:40:00',
+                                                         '2018-01-30 03:30:00',
+                                                         '2018-01-30 05:30:00'], utc=True
+                                                        ),
+                            'close_date': pd.to_datetime(['2018-01-29 20:45:00',
+                                                          '2018-01-30 05:35:00',
+                                                          '2018-01-30 08:30:00'], utc=True),
+                            'trade_duration': [47, 40, 20],
+                            'is_open': [False, False, False],
+                            'stake_amount': [0.01, 0.01, 0.01],
+                            'open_rate': [0.104445, 0.10302485, 0.122541],
+                            'close_rate': [0.104969, 0.103541, 0.123541],
+                            'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
+                            })
     backtestmock = MagicMock(side_effect=[
-        pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC'],
+        {
-                      'profit_ratio': [0.0, 0.0],
+            'results': result1,
-                      'profit_abs': [0.0, 0.0],
+            'config': default_conf,
-                      'open_date': pd.to_datetime(['2018-01-29 18:40:00',
+            'locks': [],
-                                                   '2018-01-30 03:30:00', ], utc=True
+            'final_balance': 1000,
-                                                  ),
+        },
-                      'close_date': pd.to_datetime(['2018-01-29 20:45:00',
+        {
-                                                    '2018-01-30 05:35:00', ], utc=True),
+            'results': result2,
-                      'trade_duration': [235, 40],
+            'config': default_conf,
-                      'is_open': [False, False],
+            'locks': [],
-                      'stake_amount': [0.01, 0.01],
+            'final_balance': 1000,
-                      'open_rate': [0.104445, 0.10302485],
+        }
-                      'close_rate': [0.104969, 0.103541],
-                      'sell_reason': [SellType.ROI, SellType.ROI]
-                      }),
-        pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC', 'ETH/BTC'],
-                      'profit_ratio': [0.03, 0.01, 0.1],
-                      'profit_abs': [0.01, 0.02, 0.2],
-                      'open_date': pd.to_datetime(['2018-01-29 18:40:00',
-                                                   '2018-01-30 03:30:00',
-                                                   '2018-01-30 05:30:00'], utc=True
-                                                  ),
-                      'close_date': pd.to_datetime(['2018-01-29 20:45:00',
-                                                    '2018-01-30 05:35:00',
-                                                    '2018-01-30 08:30:00'], utc=True),
-                      'trade_duration': [47, 40, 20],
-                      'is_open': [False, False, False],
-                      'stake_amount': [0.01, 0.01, 0.01],
-                      'open_rate': [0.104445, 0.10302485, 0.122541],
-                      'close_rate': [0.104969, 0.103541, 0.123541],
-                      'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
-                      }),
     ])
     mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
                  PropertyMock(return_value=['UNITTEST/BTC']))

tests/optimize/test_hyperopt.py

@@ -5,7 +5,7 @@ import re
 from datetime import datetime
 from pathlib import Path
 from typing import Dict, List
-from unittest.mock import MagicMock
+from unittest.mock import ANY, MagicMock
 
 import pandas as pd
 import pytest
@@ -18,10 +18,12 @@ from freqtrade.exceptions import OperationalException
 from freqtrade.optimize.hyperopt import Hyperopt
 from freqtrade.optimize.hyperopt_auto import HyperOptAuto
 from freqtrade.optimize.hyperopt_tools import HyperoptTools
+from freqtrade.optimize.optimize_reports import generate_strategy_stats
 from freqtrade.optimize.space import SKDecimal
 from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
 from freqtrade.state import RunMode
 from freqtrade.strategy.hyper import IntParameter
+from freqtrade.strategy.interface import SellType
 from tests.conftest import (get_args, log_has, log_has_re, patch_exchange,
                             patched_configuration_load_config_file)
 
@@ -433,18 +435,41 @@ def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
     assert hasattr(hyperopt, "position_stacking")
 
 
-def test_format_results(hyperopt):
+def test_hyperopt_format_results(hyperopt):
-    # Test with BTC as stake_currency
+
-    trades = [
+    bt_result = {
-        ('ETH/BTC', 2, 2, 123),
+        'results': pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
-        ('LTC/BTC', 1, 1, 123),
+                                          "UNITTEST/BTC", "UNITTEST/BTC"],
-        ('XPR/BTC', -1, -2, -246)
+                                 "profit_ratio": [0.003312, 0.010801, 0.013803, 0.002780],
-    ]
+                                 "profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
-    labels = ['currency', 'profit_ratio', 'profit_abs', 'trade_duration']
+                                 "open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
-    df = pd.DataFrame.from_records(trades, columns=labels)
+                                               Arrow(2017, 11, 14, 21, 36, 00).datetime,
-    results_metrics = hyperopt._calculate_results_metrics(df)
+                                               Arrow(2017, 11, 14, 22, 12, 00).datetime,
-    results_explanation = hyperopt._format_results_explanation_string(results_metrics)
+                                               Arrow(2017, 11, 14, 22, 44, 00).datetime],
-    total_profit = results_metrics['total_profit']
+                                 "close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
+                                                Arrow(2017, 11, 14, 22, 10, 00).datetime,
+                                                Arrow(2017, 11, 14, 22, 43, 00).datetime,
+                                                Arrow(2017, 11, 14, 22, 58, 00).datetime],
+                                 "open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
+                                 "close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
+                                 "trade_duration": [123, 34, 31, 14],
+                                 "is_open": [False, False, False, True],
+                                 "stake_amount": [0.01, 0.01, 0.01, 0.01],
+                                 "sell_reason": [SellType.ROI, SellType.STOP_LOSS,
+                                                 SellType.ROI, SellType.FORCE_SELL]
+                                 }),
+        'config': hyperopt.config,
+        'locks': [],
+        'final_balance': 0.02,
+        'backtest_start_time': 1619718665,
+        'backtest_end_time': 1619718665,
+        }
+    results_metrics = generate_strategy_stats({'XRP/BTC': None}, '', bt_result,
+                                              Arrow(2017, 11, 14, 19, 32, 00),
+                                              Arrow(2017, 12, 14, 19, 32, 00), market_change=0)
+
+    results_explanation = HyperoptTools.format_results_explanation_string(results_metrics, 'BTC')
+    total_profit = results_metrics['profit_total_abs']
 
     results = {
         'loss': 0.0,
@@ -458,21 +483,9 @@ def test_format_results(hyperopt):
     }
 
     result = HyperoptTools._format_explanation_string(results, 1)
-    assert result.find(' 66.67%')
+    assert ' 0.71%' in result
-    assert result.find('Total profit 1.00000000 BTC')
+    assert 'Total profit  0.00003100 BTC' in result
-    assert result.find('2.0000Σ %')
+    assert '0:50:00 min' in result
-
-    # Test with EUR as stake_currency
-    trades = [
-        ('ETH/EUR', 2, 2, 123),
-        ('LTC/EUR', 1, 1, 123),
-        ('XPR/EUR', -1, -2, -246)
-    ]
-    df = pd.DataFrame.from_records(trades, columns=labels)
-    results_metrics = hyperopt._calculate_results_metrics(df)
-    results['total_profit'] = results_metrics['total_profit']
-    result = HyperoptTools._format_explanation_string(results, 1)
-    assert result.find('Total profit 1.00000000 EUR')
 
 
 @pytest.mark.parametrize("spaces, expected_results", [
@@ -577,22 +590,37 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
                           'hyperopt_min_trades': 1,
                           })
 
-    trades = [
+    backtest_result = {
-        ('TRX/BTC', 0.023117, 0.000233, 100)
+        'results': pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
-    ]
+                                          "UNITTEST/BTC", "UNITTEST/BTC"],
-    labels = ['currency', 'profit_ratio', 'profit_abs', 'trade_duration']
+                                 "profit_ratio": [0.003312, 0.010801, 0.013803, 0.002780],
-    backtest_result = pd.DataFrame.from_records(trades, columns=labels)
+                                 "profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
+                                 "open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
+                                               Arrow(2017, 11, 14, 21, 36, 00).datetime,
+                                               Arrow(2017, 11, 14, 22, 12, 00).datetime,
+                                               Arrow(2017, 11, 14, 22, 44, 00).datetime],
+                                 "close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
+                                                Arrow(2017, 11, 14, 22, 10, 00).datetime,
+                                                Arrow(2017, 11, 14, 22, 43, 00).datetime,
+                                                Arrow(2017, 11, 14, 22, 58, 00).datetime],
+                                 "open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
+                                 "close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
+                                 "trade_duration": [123, 34, 31, 14],
+                                 "is_open": [False, False, False, True],
+                                 "stake_amount": [0.01, 0.01, 0.01, 0.01],
+                                 "sell_reason": [SellType.ROI, SellType.STOP_LOSS,
+                                                 SellType.ROI, SellType.FORCE_SELL]
+                                 }),
+        'config': hyperopt_conf,
+        'locks': [],
+        'final_balance': 1000,
+    }
 
-    mocker.patch(
+    mocker.patch('freqtrade.optimize.hyperopt.Backtesting.backtest', return_value=backtest_result)
-        'freqtrade.optimize.hyperopt.Backtesting.backtest',
+    mocker.patch('freqtrade.optimize.hyperopt.get_timerange',
-        MagicMock(return_value=backtest_result)
+                 return_value=(Arrow(2017, 12, 10), Arrow(2017, 12, 13)))
-    )
-    mocker.patch(
-        'freqtrade.optimize.hyperopt.get_timerange',
-        MagicMock(return_value=(Arrow(2017, 12, 10), Arrow(2017, 12, 13)))
-    )
     patch_exchange(mocker)
-    mocker.patch('freqtrade.optimize.hyperopt.load', MagicMock())
+    mocker.patch('freqtrade.optimize.hyperopt.load', return_value={'XRP/BTC': None})
 
     optimizer_param = {
         'adx-value': 0,
@@ -626,11 +654,11 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
         'trailing_only_offset_is_reached': False,
     }
     response_expected = {
-        'loss': 1.9840569076926293,
+        'loss': 1.9147239021396234,
-        'results_explanation': ('     1 trades. 1/0/0 Wins/Draws/Losses. '
+        'results_explanation': ('     4 trades. 4/0/0 Wins/Draws/Losses. '
-                                'Avg profit   2.31%. Median profit   2.31%. Total profit  '
+                                'Avg profit   0.77%. Median profit   0.71%. Total profit  '
-                                '0.00023300 BTC (   2.31\N{GREEK CAPITAL LETTER SIGMA}%). '
+                                '0.00003100 BTC (   0.00\N{GREEK CAPITAL LETTER SIGMA}%). '
-                                'Avg duration 100.0 min.'
+                                'Avg duration 0:50:00 min.'
                                 ).encode(locale.getpreferredencoding(), 'replace').decode('utf-8'),
         'params_details': {'buy': {'adx-enabled': False,
                                    'adx-value': 0,
@@ -660,21 +688,16 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
                                         'trailing_stop_positive': 0.02,
                                         'trailing_stop_positive_offset': 0.07}},
         'params_dict': optimizer_param,
-        'results_metrics': {'avg_profit': 2.3117,
+        'params_not_optimized': {'buy': {}, 'sell': {}},
-                            'draws': 0,
+        'results_metrics': ANY,
-                            'duration': 100.0,
+        'total_profit': 3.1e-08
-                            'losses': 0,
-                            'winsdrawslosses': '   1    0    0',
-                            'median_profit': 2.3117,
-                            'profit': 2.3117,
-                            'total_profit': 0.000233,
-                            'trade_count': 1,
-                            'wins': 1},
-        'total_profit': 0.00023300
     }
 
     hyperopt = Hyperopt(hyperopt_conf)
-    hyperopt.dimensions = hyperopt.hyperopt_space()
+    hyperopt.min_date = Arrow(2017, 12, 10)
+    hyperopt.max_date = Arrow(2017, 12, 13)
+    hyperopt.init_spaces()
+    hyperopt.dimensions = hyperopt.dimensions
     generate_optimizer_value = hyperopt.generate_optimizer(list(optimizer_param.values()))
     assert generate_optimizer_value == response_expected
 

tests/optimize/test_optimize_reports.py

@@ -14,7 +14,8 @@ from freqtrade.edge import PairInfo
 from freqtrade.optimize.optimize_reports import (generate_backtest_stats, generate_daily_stats,
                                                  generate_edge_table, generate_pair_metrics,
                                                  generate_sell_reason_stats,
-                                                 generate_strategy_metrics, store_backtest_stats,
+                                                 generate_strategy_comparison,
+                                                 generate_trading_stats, store_backtest_stats,
                                                  text_table_bt_results, text_table_sell_reason,
                                                  text_table_strategy)
 from freqtrade.resolvers.strategy_resolver import StrategyResolver
@@ -226,8 +227,6 @@ def test_generate_daily_stats(testdatadir):
     assert res['winning_days'] == 14
     assert res['draw_days'] == 4
     assert res['losing_days'] == 3
-    assert res['winner_holding_avg'] == timedelta(seconds=1440)
-    assert res['loser_holding_avg'] == timedelta(days=1, seconds=21420)
 
     # Select empty dataframe!
     res = generate_daily_stats(bt_data.loc[bt_data['open_date'] == '2000-01-01', :])
@@ -238,6 +237,23 @@ def test_generate_daily_stats(testdatadir):
     assert res['losing_days'] == 0
 
 
+def test_generate_trading_stats(testdatadir):
+    filename = testdatadir / "backtest-result_new.json"
+    bt_data = load_backtest_data(filename)
+    res = generate_trading_stats(bt_data)
+    assert isinstance(res, dict)
+    assert res['winner_holding_avg'] == timedelta(seconds=1440)
+    assert res['loser_holding_avg'] == timedelta(days=1, seconds=21420)
+    assert 'wins' in res
+    assert 'losses' in res
+    assert 'draws' in res
+
+    # Select empty dataframe!
+    res = generate_trading_stats(bt_data.loc[bt_data['open_date'] == '2000-01-01', :])
+    assert res['wins'] == 0
+    assert res['losses'] == 0
+
+
 def test_text_table_sell_reason():
 
     results = pd.DataFrame(
@@ -345,7 +361,7 @@ def test_text_table_strategy(default_conf):
         '          43.33 |        0:20:00 |      3 |       0 |        0 |'
     )
 
-    strategy_results = generate_strategy_metrics(all_results=results)
+    strategy_results = generate_strategy_comparison(all_results=results)
 
     assert text_table_strategy(strategy_results, 'BTC') == result_str
 

tests/strategy/test_interface.py

@@ -671,4 +671,4 @@ def test_auto_hyperopt_interface(default_conf):
     strategy.sell_rsi = IntParameter([0, 10], default=5, space='buy')
 
     with pytest.raises(OperationalException, match=r"Inconclusive parameter.*"):
-        [x for x in strategy.enumerate_parameters('sell')]
+        [x for x in strategy._detect_parameters('sell')]