hyperopt print colorized results

docs/hyperopt.md

@@ -343,6 +343,10 @@ def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
     return dataframe
 ```
 
+You can use the `--print-all` command line option if you would like to see all results in the hyperopt output, not only the best ones.
+
+When the `--color/--print-colorized` command line option is used, the results are colorized -- bad results (with zero trades or limited by the `--min-trades` option) are red, currest bests -- in green, results with positive total profit are printed in bold.
+
 ### Understand Hyperopt ROI results
 
 If you are optimizing ROI, you're result will look as follows and include a ROI table.

freqtrade/configuration/arguments.py

@@ -23,7 +23,8 @@ ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_pos
 
 ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
                                         "position_stacking", "epochs", "spaces",
-                                        "use_max_market_positions", "print_all", "hyperopt_jobs",
+                                        "use_max_market_positions", "print_all",
+                                        "print_colorized", "hyperopt_jobs",
                                         "hyperopt_random_state", "hyperopt_min_trades",
                                         "hyperopt_continue", "hyperopt_loss"]
 

freqtrade/configuration/cli_options.py

@@ -191,6 +191,12 @@ AVAILABLE_CLI_OPTIONS = {
         action='store_true',
         default=False,
     ),
+    "print_colorized": Arg(
+        '--color', '--print-colorized',
+        help='Print colorized hyperopt results.',
+        action='store_true',
+        default=False
+    ),
     "hyperopt_jobs": Arg(
         '-j', '--job-workers',
         help='The number of concurrently running jobs for hyperoptimization '

freqtrade/configuration/configuration.py

@@ -254,6 +254,9 @@ class Configuration(object):
         self._args_to_config(config, argname='print_all',
                              logstring='Parameter --print-all detected ...')
 
+        self._args_to_config(config, argname='print_colorized',
+                             logstring='Parameter --color/--print-colorized detected ...')
+
         self._args_to_config(config, argname='hyperopt_jobs',
                              logstring='Parameter -j/--job-workers detected: {}')
 

freqtrade/misc.py

@@ -113,3 +113,15 @@ def deep_merge_dicts(source, destination):
             destination[key] = value
 
     return destination
+
+
+def green(s):
+    return '\033[92m' + s + '\033[0m'
+
+
+def red(s):
+    return '\033[91m' + s + '\033[0m'
+
+
+def bold(s):
+    return '\033[1m' + s + '\033[0m'

freqtrade/optimize/hyperopt.py

@@ -20,6 +20,7 @@ from skopt.space import Dimension
 
 from freqtrade.configuration import Arguments
 from freqtrade.data.history import load_data, get_timeframe
+from freqtrade.misc import green, red, bold
 from freqtrade.optimize.backtesting import Backtesting
 # Import IHyperOptLoss to allow users import from this file
 from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss  # noqa: F4
@@ -153,8 +154,19 @@ class Hyperopt(Backtesting):
         Log results if it is better than any previous evaluation
         """
         print_all = self.config.get('print_all', False)
-        if print_all or results['loss'] < self.current_best_loss:
+        is_best_loss = results['loss'] < self.current_best_loss
+        if print_all or is_best_loss:
+            if is_best_loss:
+                self.current_best_loss = results['loss']
             log_str = self.format_results_logstring(results)
+            # Colorize output
+            if self.config.get('print_colorized', False):
+                if results['total_profit'] > 0:
+                    log_str = bold(log_str)
+                if results['loss'] >= MAX_LOSS:
+                    log_str = red(log_str)
+                elif is_best_loss:
+                    log_str = green(log_str)
             if print_all:
                 print(log_str)
             else:
@@ -169,7 +181,6 @@ class Hyperopt(Backtesting):
         total = self.total_epochs
         res = results['results_explanation']
         loss = results['loss']
-        self.current_best_loss = results['loss']
         log_str = f'{current:5d}/{total}: {res} Objective: {loss:.5f}'
         log_str = f'*{log_str}' if results['is_initial_point'] else f' {log_str}'
         return log_str
@@ -237,6 +248,7 @@ class Hyperopt(Backtesting):
         results_explanation = self.format_results(results)
 
         trade_count = len(results.index)
+        total_profit = results.profit_abs.sum()
 
         # If this evaluation contains too short amount of trades to be
         # interesting -- consider it as 'bad' (assigned max. loss value)
@@ -247,6 +259,7 @@ class Hyperopt(Backtesting):
                 'loss': MAX_LOSS,
                 'params': params,
                 'results_explanation': results_explanation,
+                'total_profit': total_profit,
             }
 
         loss = self.calculate_loss(results=results, trade_count=trade_count,
@@ -256,6 +269,7 @@ class Hyperopt(Backtesting):
             'loss': loss,
             'params': params,
             'results_explanation': results_explanation,
+            'total_profit': total_profit,
         }
 
     def format_results(self, results: DataFrame) -> str: