Handle backtest results without any trades
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@ -31,6 +31,7 @@ def store_backtest_stats(recordfilename: Path, stats: Dict[str, DataFrame]) -> N
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recordfilename.parent,
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f'{recordfilename.stem}-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}'
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).with_suffix(recordfilename.suffix)
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print(stats)
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file_dump_json(filename, stats)
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latest_filename = Path.joinpath(filename.parent, LAST_BT_RESULT_FN)
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@ -185,6 +186,16 @@ def generate_edge_table(results: dict) -> str:
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def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
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if len(results) == 0:
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return {
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'backtest_best_day': 0,
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'backtest_worst_day': 0,
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'winning_days': 0,
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'draw_days': 0,
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'losing_days': 0,
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'winner_holding_avg': timedelta(),
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'loser_holding_avg': timedelta(),
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}
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daily_profit = results.resample('1d', on='close_date')['profit_percent'].sum()
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worst = min(daily_profit)
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best = max(daily_profit)
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@ -249,7 +260,7 @@ def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
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'sell_reason_summary': sell_reason_stats,
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'left_open_trades': left_open_results,
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'total_trades': len(results),
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'profit_mean': results['profit_percent'].mean(),
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'profit_mean': results['profit_percent'].mean() if len(results) > 0 else 0,
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'profit_total': results['profit_percent'].sum(),
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'profit_total_abs': results['profit_abs'].sum(),
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'backtest_start': min_date.datetime,
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@ -258,7 +269,7 @@ def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
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'backtest_end_ts': max_date.timestamp * 1000,
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'backtest_days': backtest_days,
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'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else None,
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'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
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'market_change': market_change,
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'pairlist': list(btdata.keys()),
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'stake_amount': config['stake_amount'],
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