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Make best / worst day absolute

This commit is contained in:
Matthias 2021-03-05 19:21:09 +01:00
parent 078b77d41b
commit bc05d03126
2 changed files with 19 additions and 10 deletions
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10
docs/backtesting.md
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@ -289,8 +289,8 @@ A backtesting result will look like that:
| Worst Pair | ZEC/BTC -10.18% |
| Best Trade | LSK/BTC 4.25% |
| Worst Trade | ZEC/BTC -10.25% |
| Best day | 25.27% |
| Worst day | -30.67% |
| Best day | 0.00076 BTC |
| Worst day | -0.00036 BTC |
| Days win/draw/lose | 12 / 82 / 25 |
| Avg. Duration Winners | 4:23:00 |
| Avg. Duration Loser | 6:55:00 |
@ -376,8 +376,8 @@ It contains some useful key metrics about performance of your strategy on backte
| Worst Pair | ZEC/BTC -10.18% |
| Best Trade | LSK/BTC 4.25% |
| Worst Trade | ZEC/BTC -10.25% |
| Best day | 25.27% |
| Worst day | -30.67% |
| Best day | 0.00076 BTC |
| Worst day | -0.00036 BTC |
| Days win/draw/lose | 12 / 82 / 25 |
| Avg. Duration Winners | 4:23:00 |
| Avg. Duration Loser | 6:55:00 |
@ -406,7 +406,7 @@ It contains some useful key metrics about performance of your strategy on backte
- `Avg. stake amount`: Average stake amount, either `stake_amount` or the average when using dynamic stake amount.
- `Total trade volume`: Volume generated on the exchange to reach the above profit.
- `Best Pair` / `Worst Pair`: Best and worst performing pair, and it's corresponding `Cum Profit %`.
- `Best Trade` / `Worst Trade`: Biggest winning trade and biggest losing trade
- `Best Trade` / `Worst Trade`: Biggest single winning trade and biggest single losing trade.
- `Best day` / `Worst day`: Best and worst day based on daily profit.
- `Days win/draw/lose`: Winning / Losing days (draws are usually days without closed trade).
- `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.

19
freqtrade/optimize/optimize_reports.py
View File

@ -196,13 +196,18 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
return {
'backtest_best_day': 0,
'backtest_worst_day': 0,
'backtest_best_day_abs': 0,
'backtest_worst_day_abs': 0,
'winning_days': 0,
'draw_days': 0,
'losing_days': 0,
'winner_holding_avg': timedelta(),
'loser_holding_avg': timedelta(),
}
daily_profit = results.resample('1d', on='close_date')['profit_ratio'].sum()
daily_profit_rel = results.resample('1d', on='close_date')['profit_ratio'].sum()
daily_profit = results.resample('1d', on='close_date')['profit_abs'].sum().round(10)
worst_rel = min(daily_profit_rel)
best_rel = max(daily_profit_rel)
worst = min(daily_profit)
best = max(daily_profit)
winning_days = sum(daily_profit > 0)
@ -213,8 +218,10 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
losing_trades = results.loc[results['profit_ratio'] < 0]
return {
'backtest_best_day': best,
'backtest_worst_day': worst,
'backtest_best_day': best_rel,
'backtest_worst_day': worst_rel,
'backtest_best_day_abs': best,
'backtest_worst_day_abs': worst,
'winning_days': winning_days,
'draw_days': draw_days,
'losing_days': losing_days,
@ -470,8 +477,10 @@ def text_table_add_metrics(strat_results: Dict) -> str:
('Worst trade', f"{worst_trade['pair']} "
f"{round(worst_trade['profit_ratio'] * 100, 2)}%"),
('Best day', f"{round(strat_results['backtest_best_day'] * 100, 2)}%"),
('Worst day', f"{round(strat_results['backtest_worst_day'] * 100, 2)}%"),
('Best day', round_coin_value(strat_results['backtest_best_day_abs'],
strat_results['stake_currency'])),
('Worst day', round_coin_value(strat_results['backtest_worst_day_abs'],
strat_results['stake_currency'])),
('Days win/draw/lose', f"{strat_results['winning_days']} / "
f"{strat_results['draw_days']} / {strat_results['losing_days']}"),
('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),