From 4595db39aa95b2da1d9d3c8d297072a6dfdf2fa2 Mon Sep 17 00:00:00 2001
From: Bruno Gouvea <brunohenriquebh@gmail.com>
Date: Tue, 8 Jun 2021 02:18:00 -0300
Subject: [PATCH] Displaying max. drawdown only when it is not legacy mode.

---
 freqtrade/optimize/hyperopt_tools.py | 39 ++++++++++++++++------------
 1 file changed, 23 insertions(+), 16 deletions(-)

diff --git a/freqtrade/optimize/hyperopt_tools.py b/freqtrade/optimize/hyperopt_tools.py
index 59332ef69..4243c182d 100644
--- a/freqtrade/optimize/hyperopt_tools.py
+++ b/freqtrade/optimize/hyperopt_tools.py
@@ -221,6 +221,7 @@ 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:
@@ -235,17 +236,22 @@ class HyperoptTools():
                              'results_metrics.profit_total', 'results_metrics.holding_avg',
                              'results_metrics.max_drawdown', 'results_metrics.max_drawdown_abs',
                              'loss', 'is_initial_point', 'is_best']]
+
+            trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
+                              'Total profit', 'Profit', 'Avg duration', 'Max Drawdown',
+                              'max_drawdown_abs', 'Objective', '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', 'results_metrics.max_drawdown',
-                             'results_metrics.max_drawdown_abs', 'loss', 'is_initial_point', 'is_best']]
+                             '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']
 
-        trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
-                          'Total profit', 'Profit', 'Avg duration', 'Max Drawdown', 'max_drawdown_abs',
-                          'Objective', 'is_initial_point', 'is_best']
         trials['is_profit'] = False
         trials.loc[trials['is_initial_point'], 'Best'] = '*     '
         trials.loc[trials['is_best'], 'Best'] = 'Best'
@@ -269,15 +275,16 @@ class HyperoptTools():
 
         stake_currency = config['stake_currency']
 
-        trials['Max Drawdown'] = trials.apply(
-            lambda x: '{} {}'.format(
-                round_coin_value(x['max_drawdown_abs'], stake_currency),
-                '({:,.2f}%)'.format(x['Max Drawdown'] * perc_multi).rjust(10, ' ')
-            ).rjust(25 + len(stake_currency))
-            if x['Max Drawdown'] != 0.0 else '--'.rjust(25 + len(stake_currency)),
-            axis=1
-        )
-        trials = trials.drop(columns=['max_drawdown_abs'])
+        if not legacy_mode:
+            trials['Max Drawdown'] = trials.apply(
+                lambda x: '{} {}'.format(
+                    round_coin_value(x['max_drawdown_abs'], stake_currency),
+                    '({:,.2f}%)'.format(x['Max Drawdown'] * perc_multi).rjust(10, ' ')
+                ).rjust(25 + len(stake_currency))
+                if x['Max Drawdown'] != 0.0 else '--'.rjust(25 + len(stake_currency)),
+                axis=1
+            )
+            trials = trials.drop(columns=['max_drawdown_abs'])
 
         trials['Profit'] = trials.apply(
             lambda x: '{} {}'.format(