Merge pull request #2581 from hroff-1902/hyperopt-list

Add hyperopt-list and hyperopt-show commands

docs/hyperopt.md

@@ -450,7 +450,11 @@ If you are optimizing trailing stop values, Freqtrade creates the 'trailing' opt
 
 Override the `trailing_space()` method and define the desired range in it if you need values of the trailing stop parameters to vary in other ranges during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py).
 
-### Validate backtesting results
+## Show details of Hyperopt results
+
+After you run Hyperopt for the desired amount of epochs, you can later list all results for analysis, select only best or profitable once, and show the details for any of the epochs previously evaluated. This can be done with the `hyperopt-list` and `hyperopt-show` subcommands. The usage of these subcommands is described in the [Utils](utils.md#list-hyperopt-results) chapter.
+
+## Validate backtesting results
 
 Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected.
 

docs/utils.md

@@ -262,3 +262,68 @@ Show whitelist when using a [dynamic pairlist](configuration.md#pairlists).
 ```
 freqtrade test-pairlist --config config.json --quote USDT BTC
 ```
+
+## List Hyperopt results
+
+You can list the hyperoptimization epochs the Hyperopt module evaluated previously with the `hyperopt-list` subcommand.
+
+```
+usage: freqtrade hyperopt-list [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                               [-d PATH] [--userdir PATH] [--best]
+                               [--profitable] [--no-color] [--print-json]
+                               [--no-details]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --best                Select only best epochs.
+  --profitable          Select only profitable epochs.
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --print-json          Print best result detailization in JSON format.
+  --no-details          Do not print best epoch details.
+ ```
+ 
+### Examples
+
+List all results, print details of the best result at the end:
+```
+freqtrade hyperopt-list
+```
+
+List only epochs with positive profit. Do not print the details of the best epoch, so that the list can be iterated in a script:
+```
+freqtrade hyperopt-list --profitable --no-details
+```
+
+## Show details of Hyperopt results
+
+You can show the details of any hyperoptimization epoch previously evaluated by the Hyperopt module with the `hyperopt-show` subcommand.
+
+```
+usage: freqtrade hyperopt-show [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                               [-d PATH] [--userdir PATH] [--best]
+                               [--profitable] [-n INT] [--print-json]
+                               [--no-header]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --best                Select only best epochs.
+  --profitable          Select only profitable epochs.
+  -n INT, --index INT   Specify the index of the epoch to print details for.
+  --print-json          Print best result detailization in JSON format.
+  --no-header           Do not print epoch details header.
+```
+
+### Examples
+
+Print details for the epoch 168 (the number of the epoch is shown by the `hyperopt-list` subcommand or by Hyperopt itself during hyperoptimization run):
+
+```
+freqtrade hyperopt-show -n 168
+```
+
+Prints JSON data with details for the last best epoch (i.e., the best of all epochs):
+
+```
+freqtrade hyperopt-show --best -n -1 --print-json --no-header
+```

freqtrade/configuration/arguments.py

@@ -55,8 +55,14 @@ ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
 ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
                     "trade_source", "ticker_interval"]
 
+ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable", "print_colorized",
+                      "print_json", "hyperopt_list_no_details"]
+
+ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperopt_show_index",
+                      "print_json", "hyperopt_show_no_header"]
+
 NO_CONF_REQURIED = ["download-data", "list-timeframes", "list-markets", "list-pairs",
-                    "plot-dataframe", "plot-profit"]
+                    "hyperopt-list", "hyperopt-show", "plot-dataframe", "plot-profit"]
 
 NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-hyperopt", "new-strategy"]
 
@@ -123,6 +129,7 @@ class Arguments:
 
         from freqtrade.optimize import start_backtesting, start_hyperopt, start_edge
         from freqtrade.utils import (start_create_userdir, start_download_data,
+                                     start_hyperopt_list, start_hyperopt_show,
                                      start_list_exchanges, start_list_markets,
                                      start_new_hyperopt, start_new_strategy,
                                      start_list_timeframes, start_test_pairlist, start_trading)
@@ -248,3 +255,21 @@ class Arguments:
         )
         plot_profit_cmd.set_defaults(func=start_plot_profit)
         self._build_args(optionlist=ARGS_PLOT_PROFIT, parser=plot_profit_cmd)
+
+        # Add hyperopt-list subcommand
+        hyperopt_list_cmd = subparsers.add_parser(
+            'hyperopt-list',
+            help='List Hyperopt results',
+            parents=[_common_parser],
+        )
+        hyperopt_list_cmd.set_defaults(func=start_hyperopt_list)
+        self._build_args(optionlist=ARGS_HYPEROPT_LIST, parser=hyperopt_list_cmd)
+
+        # Add hyperopt-show subcommand
+        hyperopt_show_cmd = subparsers.add_parser(
+            'hyperopt-show',
+            help='Show details of Hyperopt results',
+            parents=[_common_parser],
+        )
+        hyperopt_show_cmd.set_defaults(func=start_hyperopt_show)
+        self._build_args(optionlist=ARGS_HYPEROPT_SHOW, parser=hyperopt_show_cmd)

freqtrade/configuration/cli_options.py

@@ -18,6 +18,18 @@ def check_int_positive(value: str) -> int:
     return uint
 
 
+def check_int_nonzero(value: str) -> int:
+    try:
+        uint = int(value)
+        if uint == 0:
+            raise ValueError
+    except ValueError:
+        raise argparse.ArgumentTypeError(
+            f"{value} is invalid for this parameter, should be a non-zero integer value"
+        )
+    return uint
+
+
 class Arg:
     # Optional CLI arguments
     def __init__(self, *args, **kwargs):
@@ -377,4 +389,31 @@ AVAILABLE_CLI_OPTIONS = {
         choices=["DB", "file"],
         default="file",
     ),
+    # hyperopt-list, hyperopt-show
+    "hyperopt_list_profitable": Arg(
+        '--profitable',
+        help='Select only profitable epochs.',
+        action='store_true',
+    ),
+    "hyperopt_list_best": Arg(
+        '--best',
+        help='Select only best epochs.',
+        action='store_true',
+    ),
+    "hyperopt_list_no_details": Arg(
+        '--no-details',
+        help='Do not print best epoch details.',
+        action='store_true',
+    ),
+    "hyperopt_show_index": Arg(
+        '-n', '--index',
+        help='Specify the index of the epoch to print details for.',
+        type=check_int_nonzero,
+        metavar='INT',
+    ),
+    "hyperopt_show_no_header": Arg(
+        '--no-header',
+        help='Do not print epoch details header.',
+        action='store_true',
+    ),
 }

freqtrade/configuration/configuration.py

@@ -300,6 +300,21 @@ class Configuration:
         self._args_to_config(config, argname='hyperopt_loss',
                              logstring='Using Hyperopt loss class name: {}')
 
+        self._args_to_config(config, argname='hyperopt_show_index',
+                             logstring='Parameter -n/--index detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_best',
+                             logstring='Parameter --best detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_profitable',
+                             logstring='Parameter --profitable detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_no_details',
+                             logstring='Parameter --no-details detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_show_no_header',
+                             logstring='Parameter --no-header detected: {}')
+
     def _process_plot_options(self, config: Dict[str, Any]) -> None:
 
         self._args_to_config(config, argname='pairs',

freqtrade/optimize/hyperopt.py

@@ -22,6 +22,7 @@ from pandas import DataFrame
 from skopt import Optimizer
 from skopt.space import Dimension
 
+from freqtrade import OperationalException
 from freqtrade.data.history import get_timeframe, trim_dataframe
 from freqtrade.misc import plural, round_dict
 from freqtrade.optimize.backtesting import Backtesting
@@ -74,11 +75,11 @@ class Hyperopt:
         else:
             logger.info("Continuing on previous hyperopt results.")
 
+        self.num_trials_saved = 0
+
         # Previous evaluations
         self.trials: List = []
 
-        self.num_trials_saved = 0
-
         # Populate functions here (hasattr is slow so should not be run during "regular" operations)
         if hasattr(self.custom_hyperopt, 'populate_indicators'):
             self.backtesting.strategy.advise_indicators = \
@@ -104,6 +105,10 @@ class Hyperopt:
                 self.config['ask_strategy'] = {}
             self.config['ask_strategy']['use_sell_signal'] = True
 
+        self.print_all = self.config.get('print_all', False)
+        self.print_colorized = self.config.get('print_colorized', False)
+        self.print_json = self.config.get('print_json', False)
+
     @staticmethod
     def get_lock_filename(config) -> str:
 
@@ -119,20 +124,18 @@ class Hyperopt:
                 logger.info(f"Removing `{p}`.")
                 p.unlink()
 
-    def get_args(self, params):
+    def _get_params_dict(self, raw_params: List[Any]) -> Dict:
 
-        dimensions = self.dimensions
+        dimensions: List[Dimension] = self.dimensions
 
         # Ensure the number of dimensions match
-        # the number of parameters in the list x.
+        # the number of parameters in the list.
-        if len(params) != len(dimensions):
+        if len(raw_params) != len(dimensions):
-            raise ValueError('Mismatch in number of search-space dimensions. '
+            raise ValueError('Mismatch in number of search-space dimensions.')
-                             f'len(dimensions)=={len(dimensions)} and len(x)=={len(params)}')
 
-        # Create a dict where the keys are the names of the dimensions
+        # Return a dict where the keys are the names of the dimensions
-        # and the values are taken from the list of parameters x.
+        # and the values are taken from the list of parameters.
-        arg_dict = {dim.name: value for dim, value in zip(dimensions, params)}
+        return {d.name: v for d, v in zip(dimensions, raw_params)}
-        return arg_dict
 
     def save_trials(self, final: bool = False) -> None:
         """
@@ -147,48 +150,71 @@ class Hyperopt:
             logger.info(f"{num_trials} {plural(num_trials, 'epoch')} "
                         f"saved to '{self.trials_file}'.")
 
-    def read_trials(self) -> List:
+    @staticmethod
+    def _read_trials(trials_file) -> List:
         """
         Read hyperopt trials file
         """
-        logger.info("Reading Trials from '%s'", self.trials_file)
+        logger.info("Reading Trials from '%s'", trials_file)
-        trials = load(self.trials_file)
+        trials = load(trials_file)
-        self.trials_file.unlink()
         return trials
 
-    def log_trials_result(self) -> None:
+    def _get_params_details(self, params: Dict) -> Dict:
         """
-        Display Best hyperopt result
+        Return the params for each space
         """
-        # This is printed when Ctrl+C is pressed quickly, before first epochs have
+        result: Dict = {}
-        # a chance to be evaluated.
-        if not self.trials:
-            print("No epochs evaluated yet, no best result.")
-            return
 
-        results = sorted(self.trials, key=itemgetter('loss'))
+        if self.has_space('buy'):
-        best_result = results[0]
+            result['buy'] = {p.name: params.get(p.name)
-        params = best_result['params']
+                             for p in self.hyperopt_space('buy')}
-        log_str = self.format_results_logstring(best_result)
+        if self.has_space('sell'):
+            result['sell'] = {p.name: params.get(p.name)
+                              for p in self.hyperopt_space('sell')}
+        if self.has_space('roi'):
+            result['roi'] = self.custom_hyperopt.generate_roi_table(params)
+        if self.has_space('stoploss'):
+            result['stoploss'] = {p.name: params.get(p.name)
+                                  for p in self.hyperopt_space('stoploss')}
+        if self.has_space('trailing'):
+            result['trailing'] = {p.name: params.get(p.name)
+                                  for p in self.hyperopt_space('trailing')}
 
-        print(f"\nBest result:\n\n{log_str}\n")
+        return result
 
-        if self.config.get('print_json'):
+    @staticmethod
+    def print_epoch_details(results, total_epochs, print_json: bool,
+                            no_header: bool = False, header_str: str = None) -> None:
+        """
+        Display details of the hyperopt result
+        """
+        params = results.get('params_details', {})
+
+        # Default header string
+        if header_str is None:
+            header_str = "Best result"
+
+        if not no_header:
+            explanation_str = Hyperopt._format_explanation_string(results, total_epochs)
+            print(f"\n{header_str}:\n\n{explanation_str}\n")
+
+        if print_json:
             result_dict: Dict = {}
             for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
-                self._params_update_for_json(result_dict, params, s)
+                Hyperopt._params_update_for_json(result_dict, params, s)
             print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
 
         else:
-            self._params_pretty_print(params, 'buy', "Buy hyperspace params:")
+            Hyperopt._params_pretty_print(params, 'buy', "Buy hyperspace params:")
-            self._params_pretty_print(params, 'sell', "Sell hyperspace params:")
+            Hyperopt._params_pretty_print(params, 'sell', "Sell hyperspace params:")
-            self._params_pretty_print(params, 'roi', "ROI table:")
+            Hyperopt._params_pretty_print(params, 'roi', "ROI table:")
-            self._params_pretty_print(params, 'stoploss', "Stoploss:")
+            Hyperopt._params_pretty_print(params, 'stoploss', "Stoploss:")
-            self._params_pretty_print(params, 'trailing', "Trailing stop:")
+            Hyperopt._params_pretty_print(params, 'trailing', "Trailing stop:")
 
-    def _params_update_for_json(self, result_dict, params, space: str):
+    @staticmethod
-        if self.has_space(space):
+    def _params_update_for_json(result_dict, params, space: str):
-            space_params = self.space_params(params, space)
+        if space in params:
+            space_params = Hyperopt._space_params(params, space)
             if space in ['buy', 'sell']:
                 result_dict.setdefault('params', {}).update(space_params)
             elif space == 'roi':
@@ -202,49 +228,65 @@ class Hyperopt:
             else:  # 'stoploss', 'trailing'
                 result_dict.update(space_params)
 
-    def _params_pretty_print(self, params, space: str, header: str):
+    @staticmethod
-        if self.has_space(space):
+    def _params_pretty_print(params, space: str, header: str):
-            space_params = self.space_params(params, space, 5)
+        if space in params:
-            print(header)
+            space_params = Hyperopt._space_params(params, space, 5)
-            pprint(space_params, indent=4)
+            if space == 'stoploss':
+                print(header, space_params.get('stoploss'))
+            else:
+                print(header)
+                pprint(space_params, indent=4)
 
-    def is_best(self, results) -> bool:
+    @staticmethod
-        return results['loss'] < self.current_best_loss
+    def _space_params(params, space: str, r: int = None) -> Dict:
+        d = params[space]
+        # Round floats to `r` digits after the decimal point if requested
+        return round_dict(d, r) if r else d
 
-    def log_results(self, results) -> None:
+    @staticmethod
+    def is_best_loss(results, current_best_loss) -> bool:
+        return results['loss'] < current_best_loss
+
+    def print_results(self, results) -> None:
         """
         Log results if it is better than any previous evaluation
         """
-        print_all = self.config.get('print_all', False)
+        is_best = results['is_best']
-        is_best_loss = self.is_best(results)
+        if not self.print_all:
-
+            # Print '\n' after each 100th epoch to separate dots from the log messages.
-        if not print_all:
+            # Otherwise output is messy on a terminal.
             print('.', end='' if results['current_epoch'] % 100 != 0 else None)  # type: ignore
             sys.stdout.flush()
 
-        if print_all or is_best_loss:
+        if self.print_all or is_best:
-            if is_best_loss:
+            if not self.print_all:
-                self.current_best_loss = results['loss']
+                # Separate the results explanation string from dots
-            log_str = self.format_results_logstring(results)
+                print("\n")
-            # Colorize output
+            self.print_results_explanation(results, self.total_epochs, self.print_all,
-            if self.config.get('print_colorized', False):
+                                           self.print_colorized)
-                if results['total_profit'] > 0:
-                    log_str = Fore.GREEN + log_str
-                if print_all and is_best_loss:
-                    log_str = Style.BRIGHT + log_str
-            if print_all:
-                print(log_str)
-            else:
-                print(f'\n{log_str}')
 
-    def format_results_logstring(self, results) -> str:
+    @staticmethod
-        current = results['current_epoch']
+    def print_results_explanation(results, total_epochs, highlight_best: bool,
-        total = self.total_epochs
+                                  print_colorized: bool) -> None:
-        res = results['results_explanation']
+        """
-        loss = results['loss']
+        Log results explanation string
-        log_str = f'{current:5d}/{total}: {res} Objective: {loss:.5f}'
+        """
-        log_str = f'*{log_str}' if results['is_initial_point'] else f' {log_str}'
+        explanation_str = Hyperopt._format_explanation_string(results, total_epochs)
-        return log_str
+        # Colorize output
+        if print_colorized:
+            if results['total_profit'] > 0:
+                explanation_str = Fore.GREEN + explanation_str
+            if highlight_best and results['is_best']:
+                explanation_str = Style.BRIGHT + explanation_str
+        print(explanation_str)
+
+    @staticmethod
+    def _format_explanation_string(results, total_epochs) -> str:
+        return (("*" if results['is_initial_point'] else " ") +
+                f"{results['current_epoch']:5d}/{total_epochs}: " +
+                f"{results['results_explanation']} " +
+                f"Objective: {results['loss']:.5f}")
 
     def has_space(self, space: str) -> bool:
         """
@@ -287,49 +329,43 @@ class Hyperopt:
 
         return spaces
 
-    def space_params(self, params, space: str, r: int = None) -> Dict:
+    def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
-        if space == 'roi':
-            d = self.custom_hyperopt.generate_roi_table(params)
-        else:
-            d = {p.name: params.get(p.name) for p in self.hyperopt_space(space)}
-        # Round floats to `r` digits after the decimal point if requested
-        return round_dict(d, r) if r else d
-
-    def generate_optimizer(self, _params: Dict, iteration=None) -> Dict:
         """
         Used Optimize function. Called once per epoch to optimize whatever is configured.
         Keep this function as optimized as possible!
         """
-        params = self.get_args(_params)
+        params_dict = self._get_params_dict(raw_params)
+        params_details = self._get_params_details(params_dict)
 
         if self.has_space('roi'):
             self.backtesting.strategy.minimal_roi = \
-                    self.custom_hyperopt.generate_roi_table(params)
+                    self.custom_hyperopt.generate_roi_table(params_dict)
 
         if self.has_space('buy'):
             self.backtesting.strategy.advise_buy = \
-                    self.custom_hyperopt.buy_strategy_generator(params)
+                    self.custom_hyperopt.buy_strategy_generator(params_dict)
 
         if self.has_space('sell'):
             self.backtesting.strategy.advise_sell = \
-                    self.custom_hyperopt.sell_strategy_generator(params)
+                    self.custom_hyperopt.sell_strategy_generator(params_dict)
 
         if self.has_space('stoploss'):
-            self.backtesting.strategy.stoploss = params['stoploss']
+            self.backtesting.strategy.stoploss = params_dict['stoploss']
 
         if self.has_space('trailing'):
-            self.backtesting.strategy.trailing_stop = params['trailing_stop']
+            self.backtesting.strategy.trailing_stop = params_dict['trailing_stop']
-            self.backtesting.strategy.trailing_stop_positive = params['trailing_stop_positive']
+            self.backtesting.strategy.trailing_stop_positive = \
+                params_dict['trailing_stop_positive']
             self.backtesting.strategy.trailing_stop_positive_offset = \
-                params['trailing_stop_positive_offset']
+                params_dict['trailing_stop_positive_offset']
             self.backtesting.strategy.trailing_only_offset_is_reached = \
-                params['trailing_only_offset_is_reached']
+                params_dict['trailing_only_offset_is_reached']
 
         processed = load(self.tickerdata_pickle)
 
         min_date, max_date = get_timeframe(processed)
 
-        results = self.backtesting.backtest(
+        backtesting_results = self.backtesting.backtest(
             {
                 'stake_amount': self.config['stake_amount'],
                 'processed': processed,
@@ -339,58 +375,63 @@ class Hyperopt:
                 'end_date': max_date,
             }
         )
-        results_explanation = self.format_results(results)
+        return self._get_results_dict(backtesting_results, min_date, max_date,
+                                      params_dict, params_details)
 
-        trade_count = len(results.index)
+    def _get_results_dict(self, backtesting_results, min_date, max_date,
-        total_profit = results.profit_abs.sum()
+                          params_dict, params_details):
+        results_metrics = self._calculate_results_metrics(backtesting_results)
+        results_explanation = self._format_results_explanation_string(results_metrics)
+
+        trade_count = results_metrics['trade_count']
+        total_profit = results_metrics['total_profit']
 
         # If this evaluation contains too short amount of trades to be
         # interesting -- consider it as 'bad' (assigned max. loss value)
         # in order to cast this hyperspace point away from optimization
         # path. We do not want to optimize 'hodl' strategies.
-        if trade_count < self.config['hyperopt_min_trades']:
+        loss: float = MAX_LOSS
-            return {
+        if trade_count >= self.config['hyperopt_min_trades']:
-                'loss': MAX_LOSS,
+            loss = self.calculate_loss(results=backtesting_results, trade_count=trade_count,
-                'params': params,
+                                       min_date=min_date.datetime, max_date=max_date.datetime)
-                'results_explanation': results_explanation,
-                'total_profit': total_profit,
-            }
-
-        loss = self.calculate_loss(results=results, trade_count=trade_count,
-                                   min_date=min_date.datetime, max_date=max_date.datetime)
-
         return {
             'loss': loss,
-            'params': params,
+            'params_dict': params_dict,
+            'params_details': params_details,
+            'results_metrics': results_metrics,
             'results_explanation': results_explanation,
             'total_profit': total_profit,
         }
 
-    def format_results(self, results: DataFrame) -> str:
+    def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict:
+        return {
+            'trade_count': len(backtesting_results.index),
+            'avg_profit': backtesting_results.profit_percent.mean() * 100.0,
+            'total_profit': backtesting_results.profit_abs.sum(),
+            'profit': backtesting_results.profit_percent.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
         """
-        trades = len(results.index)
-        avg_profit = results.profit_percent.mean() * 100.0
-        total_profit = results.profit_abs.sum()
         stake_cur = self.config['stake_currency']
-        profit = results.profit_percent.sum() * 100.0
+        return (f"{results_metrics['trade_count']:6d} trades. "
-        duration = results.trade_duration.mean()
+                f"Avg profit {results_metrics['avg_profit']: 6.2f}%. "
-
+                f"Total profit {results_metrics['total_profit']: 11.8f} {stake_cur} "
-        return (f'{trades:6d} trades. Avg profit {avg_profit: 5.2f}%. '
+                f"({results_metrics['profit']: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
-                f'Total profit {total_profit: 11.8f} {stake_cur} '
+                f"Avg duration {results_metrics['duration']:5.1f} mins."
-                f'({profit: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). '
-                f'Avg duration {duration:5.1f} mins.'
                 ).encode(locale.getpreferredencoding(), 'replace').decode('utf-8')
 
-    def get_optimizer(self, dimensions, cpu_count) -> Optimizer:
+    def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
         return Optimizer(
             dimensions,
             base_estimator="ET",
             acq_optimizer="auto",
             n_initial_points=INITIAL_POINTS,
             acq_optimizer_kwargs={'n_jobs': cpu_count},
-            random_state=self.config.get('hyperopt_random_state', None)
+            random_state=self.config.get('hyperopt_random_state', None),
         )
 
     def fix_optimizer_models_list(self):
@@ -414,14 +455,20 @@ class Hyperopt:
         return parallel(delayed(
                         wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
 
-    def load_previous_results(self):
+    @staticmethod
-        """ read trials file if we have one """
+    def load_previous_results(trials_file) -> List:
-        if self.trials_file.is_file() and self.trials_file.stat().st_size > 0:
+        """
-            self.trials = self.read_trials()
+        Load data for epochs from the file if we have one
-            logger.info(
+        """
-                'Loaded %d previous evaluations from disk.',
+        trials: List = []
-                len(self.trials)
+        if trials_file.is_file() and trials_file.stat().st_size > 0:
-            )
+            trials = Hyperopt._read_trials(trials_file)
+            if trials[0].get('is_best') is None:
+                raise OperationalException(
+                        "The file with Hyperopt results is incompatible with this version "
+                        "of Freqtrade and cannot be loaded.")
+            logger.info(f"Loaded {len(trials)} previous evaluations from disk.")
+        return trials
 
     def start(self) -> None:
         data, timerange = self.backtesting.load_bt_data()
@@ -442,17 +489,17 @@ class Hyperopt:
         # We don't need exchange instance anymore while running hyperopt
         self.backtesting.exchange = None  # type: ignore
 
-        self.load_previous_results()
+        self.trials = self.load_previous_results(self.trials_file)
 
         cpus = cpu_count()
         logger.info(f"Found {cpus} CPU cores. Let's make them scream!")
         config_jobs = self.config.get('hyperopt_jobs', -1)
         logger.info(f'Number of parallel jobs set as: {config_jobs}')
 
-        self.dimensions = self.hyperopt_space()
+        self.dimensions: List[Dimension] = self.hyperopt_space()
         self.opt = self.get_optimizer(self.dimensions, config_jobs)
 
-        if self.config.get('print_colorized', False):
+        if self.print_colorized:
             colorama_init(autoreset=True)
 
         try:
@@ -466,19 +513,38 @@ class Hyperopt:
                     self.opt.tell(asked, [v['loss'] for v in f_val])
                     self.fix_optimizer_models_list()
                     for j in range(jobs):
-                        # Use human-friendly index here (starting from 1)
+                        # Use human-friendly indexes here (starting from 1)
                         current = i * jobs + j + 1
                         val = f_val[j]
                         val['current_epoch'] = current
                         val['is_initial_point'] = current <= INITIAL_POINTS
                         logger.debug(f"Optimizer epoch evaluated: {val}")
-                        is_best = self.is_best(val)
+
-                        self.log_results(val)
+                        is_best = self.is_best_loss(val, self.current_best_loss)
+                        # This value is assigned here and not in the optimization method
+                        # to keep proper order in the list of results. That's because
+                        # evaluations can take different time. Here they are aligned in the
+                        # order they will be shown to the user.
+                        val['is_best'] = is_best
+
+                        self.print_results(val)
+
+                        if is_best:
+                            self.current_best_loss = val['loss']
                         self.trials.append(val)
+                        # Save results after each best epoch and every 100 epochs
                         if is_best or current % 100 == 0:
                             self.save_trials()
         except KeyboardInterrupt:
             print('User interrupted..')
 
         self.save_trials(final=True)
-        self.log_trials_result()
+
+        if self.trials:
+            sorted_trials = sorted(self.trials, key=itemgetter('loss'))
+            results = sorted_trials[0]
+            self.print_epoch_details(results, self.total_epochs, self.print_json)
+        else:
+            # This is printed when Ctrl+C is pressed quickly, before first epochs have
+            # a chance to be evaluated.
+            print("No epochs evaluated yet, no best result.")

freqtrade/utils.py

@@ -2,11 +2,13 @@ import csv
 import logging
 import sys
 from collections import OrderedDict
+from operator import itemgetter
 from pathlib import Path
 from typing import Any, Dict, List
 
 import arrow
 import rapidjson
+from colorama import init as colorama_init
 from tabulate import tabulate
 
 from freqtrade import OperationalException
@@ -354,3 +356,106 @@ def start_test_pairlist(args: Dict[str, Any]) -> None:
             print(rapidjson.dumps(list(pairlist), default=str))
         else:
             print(pairlist)
+
+
+def start_hyperopt_list(args: Dict[str, Any]) -> None:
+    """
+    List hyperopt epochs previously evaluated
+    """
+    from freqtrade.optimize.hyperopt import Hyperopt
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    only_best = config.get('hyperopt_list_best', False)
+    only_profitable = config.get('hyperopt_list_profitable', False)
+    print_colorized = config.get('print_colorized', False)
+    print_json = config.get('print_json', False)
+    no_details = config.get('hyperopt_list_no_details', False)
+    no_header = False
+
+    trials_file = (config['user_data_dir'] /
+                   'hyperopt_results' / 'hyperopt_results.pickle')
+
+    # Previous evaluations
+    trials = Hyperopt.load_previous_results(trials_file)
+    total_epochs = len(trials)
+
+    trials = _hyperopt_filter_trials(trials, only_best, only_profitable)
+
+    # TODO: fetch the interval for epochs to print from the cli option
+    epoch_start, epoch_stop = 0, None
+
+    if print_colorized:
+        colorama_init(autoreset=True)
+
+    try:
+        # Human-friendly indexes used here (starting from 1)
+        for val in trials[epoch_start:epoch_stop]:
+            Hyperopt.print_results_explanation(val, total_epochs, not only_best, print_colorized)
+
+    except KeyboardInterrupt:
+        print('User interrupted..')
+
+    if trials and not no_details:
+        sorted_trials = sorted(trials, key=itemgetter('loss'))
+        results = sorted_trials[0]
+        Hyperopt.print_epoch_details(results, total_epochs, print_json, no_header)
+
+
+def start_hyperopt_show(args: Dict[str, Any]) -> None:
+    """
+    Show details of a hyperopt epoch previously evaluated
+    """
+    from freqtrade.optimize.hyperopt import Hyperopt
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    only_best = config.get('hyperopt_list_best', False)
+    only_profitable = config.get('hyperopt_list_profitable', False)
+    no_header = config.get('hyperopt_show_no_header', False)
+
+    trials_file = (config['user_data_dir'] /
+                   'hyperopt_results' / 'hyperopt_results.pickle')
+
+    # Previous evaluations
+    trials = Hyperopt.load_previous_results(trials_file)
+    total_epochs = len(trials)
+
+    trials = _hyperopt_filter_trials(trials, only_best, only_profitable)
+    trials_epochs = len(trials)
+
+    n = config.get('hyperopt_show_index', -1)
+    if n > trials_epochs:
+        raise OperationalException(
+                f"The index of the epoch to show should be less than {trials_epochs + 1}.")
+    if n < -trials_epochs:
+        raise OperationalException(
+                f"The index of the epoch to show should be greater than {-trials_epochs - 1}.")
+
+    # Translate epoch index from human-readable format to pythonic
+    if n > 0:
+        n -= 1
+
+    print_json = config.get('print_json', False)
+
+    if trials:
+        val = trials[n]
+        Hyperopt.print_epoch_details(val, total_epochs, print_json, no_header,
+                                     header_str="Epoch details")
+
+
+def _hyperopt_filter_trials(trials: List, only_best: bool, only_profitable: bool) -> List:
+    """
+    Filter our items from the list of hyperopt results
+    """
+    if only_best:
+        trials = [x for x in trials if x['is_best']]
+    if only_profitable:
+        trials = [x for x in trials if x['results_metrics']['profit'] > 0]
+
+    logger.info(f"{len(trials)} " +
+                ("best " if only_best else "") +
+                ("profitable " if only_profitable else "") +
+                "epochs found.")
+
+    return trials

tests/optimize/test_hyperopt.py

@@ -357,8 +357,9 @@ def test_onlyprofit_loss_prefers_higher_profits(default_conf, hyperopt_results)
 def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
     hyperopt.current_best_loss = 2
     hyperopt.total_epochs = 2
-    hyperopt.log_results(
+    hyperopt.print_results(
         {
+            'is_best': True,
             'loss': 1,
             'current_epoch': 2,  # This starts from 1 (in a human-friendly manner)
             'results_explanation': 'foo.',
@@ -371,8 +372,9 @@ def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
 
 def test_no_log_if_loss_does_not_improve(hyperopt, caplog) -> None:
     hyperopt.current_best_loss = 2
-    hyperopt.log_results(
+    hyperopt.print_results(
         {
+            'is_best': False,
             'loss': 3,
             'current_epoch': 1,
         }
@@ -400,8 +402,8 @@ def test_save_trials_saves_trials(mocker, hyperopt, testdatadir, caplog) -> None
 def test_read_trials_returns_trials_file(mocker, hyperopt, testdatadir, caplog) -> None:
     trials = create_trials(mocker, hyperopt, testdatadir)
     mock_load = mocker.patch('freqtrade.optimize.hyperopt.load', return_value=trials)
-    hyperopt_trial = hyperopt.read_trials()
     trials_file = testdatadir / 'optimize' / 'ut_trials.pickle'
+    hyperopt_trial = hyperopt._read_trials(trials_file)
     assert log_has(f"Reading Trials from '{trials_file}'", caplog)
     assert hyperopt_trial == trials
     mock_load.assert_called_once()
@@ -473,8 +475,22 @@ def test_format_results(hyperopt):
     ]
     labels = ['currency', 'profit_percent', 'profit_abs', 'trade_duration']
     df = pd.DataFrame.from_records(trades, columns=labels)
+    results_metrics = hyperopt._calculate_results_metrics(df)
+    results_explanation = hyperopt._format_results_explanation_string(results_metrics)
+    total_profit = results_metrics['total_profit']
 
-    result = hyperopt.format_results(df)
+    results = {
+        'loss': 0.0,
+        'params_dict': None,
+        'params_details': None,
+        'results_metrics': results_metrics,
+        'results_explanation': results_explanation,
+        'total_profit': total_profit,
+        'current_epoch': 1,
+        'is_initial_point': True,
+    }
+
+    result = hyperopt._format_explanation_string(results, 1)
     assert result.find(' 66.67%')
     assert result.find('Total profit 1.00000000 BTC')
     assert result.find('2.0000Σ %')
@@ -486,7 +502,9 @@ def test_format_results(hyperopt):
         ('XPR/EUR', -1, -2, -246)
     ]
     df = pd.DataFrame.from_records(trades, columns=labels)
-    result = hyperopt.format_results(df)
+    results_metrics = hyperopt._calculate_results_metrics(df)
+    results['total_profit'] = results_metrics['total_profit']
+    result = hyperopt._format_explanation_string(results, 1)
     assert result.find('Total profit 1.00000000 EUR')
 
 
@@ -623,10 +641,42 @@ def test_generate_optimizer(mocker, default_conf) -> None:
     }
     response_expected = {
         'loss': 1.9840569076926293,
-        'results_explanation': ('     1 trades. Avg profit  2.31%. Total profit  0.00023300 BTC '
+        'results_explanation': ('     1 trades. Avg profit   2.31%. Total profit  0.00023300 BTC '
                                 '(   2.31\N{GREEK CAPITAL LETTER SIGMA}%). Avg duration 100.0 mins.'
                                 ).encode(locale.getpreferredencoding(), 'replace').decode('utf-8'),
-        'params': optimizer_param,
+        'params_details': {'buy': {'adx-enabled': False,
+                                   'adx-value': 0,
+                                   'fastd-enabled': True,
+                                   'fastd-value': 35,
+                                   'mfi-enabled': False,
+                                   'mfi-value': 0,
+                                   'rsi-enabled': False,
+                                   'rsi-value': 0,
+                                   'trigger': 'macd_cross_signal'},
+                           'roi': {0: 0.12000000000000001,
+                                   20.0: 0.02,
+                                   50.0: 0.01,
+                                   110.0: 0},
+                           'sell': {'sell-adx-enabled': False,
+                                    'sell-adx-value': 0,
+                                    'sell-fastd-enabled': True,
+                                    'sell-fastd-value': 75,
+                                    'sell-mfi-enabled': False,
+                                    'sell-mfi-value': 0,
+                                    'sell-rsi-enabled': False,
+                                    'sell-rsi-value': 0,
+                                    'sell-trigger': 'macd_cross_signal'},
+                           'stoploss': {'stoploss': -0.4},
+                           'trailing': {'trailing_only_offset_is_reached': False,
+                                        'trailing_stop': True,
+                                        'trailing_stop_positive': 0.02,
+                                        'trailing_stop_positive_offset': 0.1}},
+        'params_dict': optimizer_param,
+        'results_metrics': {'avg_profit': 2.3117,
+                            'duration': 100.0,
+                            'profit': 2.3117,
+                            'total_profit': 0.000233,
+                            'trade_count': 1},
         'total_profit': 0.00023300
     }
 
@@ -682,7 +732,11 @@ def test_print_json_spaces_all(mocker, default_conf, caplog, capsys) -> None:
 
     parallel = mocker.patch(
         'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
-        MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result', 'params': {}}])
+        MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result', 'params': {},
+                                 'params_details': {'buy': {'mfi-value': None},
+                                                    'sell': {'sell-mfi-value': None},
+                                                    'roi': {}, 'stoploss': {'stoploss': None},
+                                                    'trailing': {'trailing_stop': None}}}])
     )
     patch_exchange(mocker)
 
@@ -704,7 +758,7 @@ def test_print_json_spaces_all(mocker, default_conf, caplog, capsys) -> None:
     parallel.assert_called_once()
 
     out, err = capsys.readouterr()
-    assert '{"params":{"mfi-value":null,"fastd-value":null,"adx-value":null,"rsi-value":null,"mfi-enabled":null,"fastd-enabled":null,"adx-enabled":null,"rsi-enabled":null,"trigger":null,"sell-mfi-value":null,"sell-fastd-value":null,"sell-adx-value":null,"sell-rsi-value":null,"sell-mfi-enabled":null,"sell-fastd-enabled":null,"sell-adx-enabled":null,"sell-rsi-enabled":null,"sell-trigger":null},"minimal_roi":{},"stoploss":null,"trailing_stop":null,"trailing_stop_positive":null,"trailing_stop_positive_offset":null,"trailing_only_offset_is_reached":null}' in out  # noqa: E501
+    assert '{"params":{"mfi-value":null,"sell-mfi-value":null},"minimal_roi":{},"stoploss":null,"trailing_stop":null}' in out  # noqa: E501
     assert dumper.called
     # Should be called twice, once for tickerdata, once to save evaluations
     assert dumper.call_count == 2
@@ -721,7 +775,10 @@ def test_print_json_spaces_default(mocker, default_conf, caplog, capsys) -> None
 
     parallel = mocker.patch(
         'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
-        MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result', 'params': {}}])
+        MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result', 'params': {},
+                                 'params_details': {'buy': {'mfi-value': None},
+                                                    'sell': {'sell-mfi-value': None},
+                                                    'roi': {}, 'stoploss': {'stoploss': None}}}])
     )
     patch_exchange(mocker)
 
@@ -743,7 +800,7 @@ def test_print_json_spaces_default(mocker, default_conf, caplog, capsys) -> None
     parallel.assert_called_once()
 
     out, err = capsys.readouterr()
-    assert '{"params":{"mfi-value":null,"fastd-value":null,"adx-value":null,"rsi-value":null,"mfi-enabled":null,"fastd-enabled":null,"adx-enabled":null,"rsi-enabled":null,"trigger":null,"sell-mfi-value":null,"sell-fastd-value":null,"sell-adx-value":null,"sell-rsi-value":null,"sell-mfi-enabled":null,"sell-fastd-enabled":null,"sell-adx-enabled":null,"sell-rsi-enabled":null,"sell-trigger":null},"minimal_roi":{},"stoploss":null}' in out  # noqa: E501
+    assert '{"params":{"mfi-value":null,"sell-mfi-value":null},"minimal_roi":{},"stoploss":null}' in out  # noqa: E501
     assert dumper.called
     # Should be called twice, once for tickerdata, once to save evaluations
     assert dumper.call_count == 2
@@ -760,7 +817,8 @@ def test_print_json_spaces_roi_stoploss(mocker, default_conf, caplog, capsys) ->
 
     parallel = mocker.patch(
         'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
-        MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result', 'params': {}}])
+        MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result', 'params': {},
+                                 'params_details': {'roi': {}, 'stoploss': {'stoploss': None}}}])
     )
     patch_exchange(mocker)
 

tests/test_utils.py

@@ -10,7 +10,8 @@ from freqtrade.utils import (setup_utils_configuration, start_create_userdir,
                              start_download_data, start_list_exchanges,
                              start_list_markets, start_list_timeframes,
                              start_new_hyperopt, start_new_strategy,
-                             start_test_pairlist, start_trading)
+                             start_test_pairlist, start_trading,
+                             start_hyperopt_list, start_hyperopt_show)
 from tests.conftest import (get_args, log_has, log_has_re, patch_exchange,
                             patched_configuration_load_config_file)
 
@@ -657,3 +658,131 @@ def test_start_test_pairlist(mocker, caplog, markets, tickers, default_conf, cap
     captured = capsys.readouterr()
     assert re.match(r"Pairs for .*", captured.out)
     assert re.match("['ETH/BTC', 'TKN/BTC', 'BLK/BTC', 'LTC/BTC', 'XRP/BTC']", captured.out)
+
+
+def test_hyperopt_list(mocker, capsys, hyperopt_results):
+    mocker.patch(
+        'freqtrade.optimize.hyperopt.Hyperopt.load_previous_results',
+        MagicMock(return_value=hyperopt_results)
+    )
+
+    args = [
+        "hyperopt-list",
+        "--no-details"
+    ]
+    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"
+    ]
+    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"
+    ]
+    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"])
+
+
+def test_hyperopt_show(mocker, capsys, hyperopt_results):
+    mocker.patch(
+        'freqtrade.optimize.hyperopt.Hyperopt.load_previous_results',
+        MagicMock(return_value=hyperopt_results)
+    )
+
+    args = [
+        "hyperopt-show",
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_show(pargs)
+    captured = capsys.readouterr()
+    assert " 12/12" in captured.out
+
+    args = [
+        "hyperopt-show",
+        "--best"
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_show(pargs)
+    captured = capsys.readouterr()
+    assert " 10/12" in captured.out
+
+    args = [
+        "hyperopt-show",
+        "-n", "1"
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_show(pargs)
+    captured = capsys.readouterr()
+    assert " 1/12" in captured.out
+
+    args = [
+        "hyperopt-show",
+        "--best",
+        "-n", "2"
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_show(pargs)
+    captured = capsys.readouterr()
+    assert " 5/12" in captured.out
+
+    args = [
+        "hyperopt-show",
+        "--best",
+        "-n", "-1"
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_show(pargs)
+    captured = capsys.readouterr()
+    assert " 10/12" in captured.out
+
+    args = [
+        "hyperopt-show",
+        "--best",
+        "-n", "-4"
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    with pytest.raises(OperationalException,
+                       match="The index of the epoch to show should be greater than -4."):
+        start_hyperopt_show(pargs)
+
+    args = [
+        "hyperopt-show",
+        "--best",
+        "-n", "4"
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    with pytest.raises(OperationalException,
+                       match="The index of the epoch to show should be less than 4."):
+        start_hyperopt_show(pargs)