Add Profit factor to backtesting
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@ -300,6 +300,7 @@ A backtesting result will look like that:
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| Absolute profit | 0.00762792 BTC |
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| Total profit % | 76.2% |
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| CAGR % | 460.87% |
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| Profit factor | 1.11 |
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| Avg. stake amount | 0.001 BTC |
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| Total trade volume | 0.429 BTC |
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@ -399,6 +400,7 @@ It contains some useful key metrics about performance of your strategy on backte
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| Absolute profit | 0.00762792 BTC |
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| Total profit % | 76.2% |
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| CAGR % | 460.87% |
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| Profit factor | 1.11 |
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| Avg. stake amount | 0.001 BTC |
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| Total trade volume | 0.429 BTC |
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@ -444,6 +446,8 @@ It contains some useful key metrics about performance of your strategy on backte
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- `Final balance`: Final balance - starting balance + absolute profit.
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- `Absolute profit`: Profit made in stake currency.
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- `Total profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital − Starting capital) / Starting capital`.
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- `CAGR %`: Compound annual growth rate.
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- `Profit factor`: profit / loss.
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- `Avg. stake amount`: Average stake amount, either `stake_amount` or the average when using dynamic stake amount.
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- `Total trade volume`: Volume generated on the exchange to reach the above profit.
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- `Best Pair` / `Worst Pair`: Best and worst performing pair, and it's corresponding `Cum Profit %`.
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@ -416,6 +416,9 @@ def generate_strategy_stats(pairlist: List[str],
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key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
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worst_pair = min([pair for pair in pair_results if pair['key'] != 'TOTAL'],
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key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
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winning_profit = results.loc[results['profit_abs'] > 0, 'profit_abs'].sum()
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losing_profit = results.loc[results['profit_abs'] < 0, 'profit_abs'].sum()
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profit_factor = winning_profit / abs(losing_profit) if losing_profit else 0.0
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backtest_days = (max_date - min_date).days or 1
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strat_stats = {
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@ -443,6 +446,7 @@ def generate_strategy_stats(pairlist: List[str],
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'profit_total_long_abs': results.loc[~results['is_short'], 'profit_abs'].sum(),
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'profit_total_short_abs': results.loc[results['is_short'], 'profit_abs'].sum(),
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'cagr': calculate_cagr(backtest_days, start_balance, content['final_balance']),
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'profit_factor': profit_factor,
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'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT),
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'backtest_start_ts': int(min_date.timestamp() * 1000),
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'backtest_end': max_date.strftime(DATETIME_PRINT_FORMAT),
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@ -779,6 +783,8 @@ def text_table_add_metrics(strat_results: Dict) -> str:
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strat_results['stake_currency'])),
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('Total profit %', f"{strat_results['profit_total']:.2%}"),
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('CAGR %', f"{strat_results['cagr']:.2%}" if 'cagr' in strat_results else 'N/A'),
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('Profit factor', f'{strat_results["profit_factor"]:.2f}' if 'profit_factor'
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in strat_results else 'N/A'),
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('Trades per day', strat_results['trades_per_day']),
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('Avg. daily profit %',
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f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"),
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