added wins/draws/losses
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@ -533,10 +533,14 @@ class Hyperopt:
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'total_profit': total_profit,
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}
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def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict:
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def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict:
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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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'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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'profit': backtesting_results.profit_percent.sum() * 100.0,
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'duration': backtesting_results.trade_duration.mean(),
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@ -548,7 +552,11 @@ class Hyperopt:
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"""
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stake_cur = self.config['stake_currency']
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return (f"{results_metrics['trade_count']:6d} trades. "
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f"{results_metrics['wins']:6d} wins. "
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f"{results_metrics['draws']:6d} draws. "
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f"{results_metrics['losses']:6d} losses. "
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f"Avg profit {results_metrics['avg_profit']: 6.2f}%. "
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f"Median profit {results_metrics['median_profit']: 6.2f}%. "
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f"Total profit {results_metrics['total_profit']: 11.8f} {stake_cur} "
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f"({results_metrics['profit']: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
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f"Avg duration {results_metrics['duration']:5.1f} min."
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