Merge pull request #2089 from hroff-1902/hyperopt-print-colorized

Hyperopt print colorized results

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 +191,13 @@ AVAILABLE_CLI_OPTIONS = {
         action='store_true',
         default=False,
     ),
+    "print_colorized": Arg(
+        '--no-color',
+        help='Disable colorization of hyperopt results. May be useful if you are '
+        'redirecting output to a file.',
+        action='store_false',
+        default=True,
+    ),
     "hyperopt_jobs": Arg(
         '-j', '--job-workers',
         help='The number of concurrently running jobs for hyperoptimization '

freqtrade/configuration/configuration.py

@@ -236,6 +236,12 @@ class Configuration(object):
         self._args_to_config(config, argname='print_all',
                              logstring='Parameter --print-all detected ...')
 
+        if 'print_colorized' in self.args and not self.args.print_colorized:
+            logger.info('Parameter --no-color detected ...')
+            config.update({'print_colorized': False})
+        else:
+            config.update({'print_colorized': True})
+
         self._args_to_config(config, argname='hyperopt_jobs',
                              logstring='Parameter -j/--job-workers detected: {}')
 

freqtrade/optimize/hyperopt.py

@@ -13,6 +13,8 @@ from pathlib import Path
 from pprint import pprint
 from typing import Any, Dict, List, Optional
 
+from colorama import init as colorama_init
+from colorama import Fore, Style
 from joblib import Parallel, delayed, dump, load, wrap_non_picklable_objects, cpu_count
 from pandas import DataFrame
 from skopt import Optimizer
@@ -153,8 +155,17 @@ 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 = Fore.GREEN + log_str
+                if print_all and is_best_loss:
+                    log_str = Style.BRIGHT + log_str
             if print_all:
                 print(log_str)
             else:
@@ -169,7 +180,6 @@ 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 +247,7 @@ 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 +258,7 @@ 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 +268,7 @@ class Hyperopt(Backtesting):
             'loss': loss,
             'params': params,
             'results_explanation': results_explanation,
+            'total_profit': total_profit,
         }
 
     def format_results(self, results: DataFrame) -> str:
@@ -339,6 +352,10 @@ class Hyperopt(Backtesting):
         logger.info(f'Number of parallel jobs set as: {config_jobs}')
 
         opt = self.get_optimizer(config_jobs)
+
+        if self.config.get('print_colorized', False):
+            colorama_init(autoreset=True)
+
         try:
             with Parallel(n_jobs=config_jobs) as parallel:
                 jobs = parallel._effective_n_jobs()

freqtrade/tests/optimize/test_hyperopt.py

@@ -578,7 +578,8 @@ def test_generate_optimizer(mocker, default_conf) -> None:
         'loss': 1.9840569076926293,
         'results_explanation': '     1 trades. Avg profit  2.31%. Total profit  0.00023300 BTC '
                                '(   2.31Σ%). Avg duration 100.0 mins.',
-        'params': optimizer_param
+        'params': optimizer_param,
+        'total_profit': 0.00023300
     }
 
     hyperopt = Hyperopt(default_conf)