Show wins / draws / losses in hyperopt table
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@ -312,11 +312,16 @@ class Hyperopt:
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trials = json_normalize(results, max_level=1)
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trials['Best'] = ''
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if 'results_metrics.winsdrawslosses' not in trials.columns:
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# Ensure compatibility with older versions of hyperopt results
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trials['results_metrics.winsdrawslosses'] = 'N/A'
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trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
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'results_metrics.winsdrawslosses',
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'results_metrics.avg_profit', 'results_metrics.total_profit',
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'results_metrics.profit', 'results_metrics.duration',
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'loss', 'is_initial_point', 'is_best']]
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trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit',
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trials.columns = ['Best', 'Epoch', 'Trades', 'W/D/L', 'Avg profit', 'Total profit',
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'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
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trials['is_profit'] = False
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trials.loc[trials['is_initial_point'], 'Best'] = '* '
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@ -558,11 +563,15 @@ class Hyperopt:
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}
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def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict:
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wins = len(backtesting_results[backtesting_results.profit_percent > 0])
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draws = len(backtesting_results[backtesting_results.profit_percent == 0])
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losses = len(backtesting_results[backtesting_results.profit_percent < 0])
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return {
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'trade_count': len(backtesting_results.index),
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'wins': len(backtesting_results[backtesting_results.profit_percent > 0]),
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'draws': len(backtesting_results[backtesting_results.profit_percent == 0]),
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'losses': len(backtesting_results[backtesting_results.profit_percent < 0]),
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'wins': wins,
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'draws': draws,
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'losses': losses,
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'winsdrawslosses': f"{wins}/{draws}/{losses}",
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'avg_profit': backtesting_results.profit_percent.mean() * 100.0,
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'median_profit': backtesting_results.profit_percent.median() * 100.0,
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'total_profit': backtesting_results.profit_abs.sum(),
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@ -781,6 +781,7 @@ def test_generate_optimizer(mocker, default_conf) -> None:
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'draws': 0,
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'duration': 100.0,
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'losses': 0,
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'winsdrawslosses': '1/0/0',
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'median_profit': 2.3117,
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'profit': 2.3117,
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'total_profit': 0.000233,
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