Вычислить доход с несколькими строками купить и продать в R

Question

Я хочу вычислить возврат в моем dataframe Вот пример

     Type                    Date Open Qty        Price       Amount

7  Bought 2018-06-28 20:47:39 UTC     0.01 667272.13336 6672.7213336
8    Sold 2018-06-28 20:47:48 UTC   0.0078    667514.96  5206.616688
9    Sold 2018-06-28 20:47:48 UTC   0.0022    667514.96  1468.532912
10 Bought 2018-06-28 20:48:17 UTC     0.01 668624.18413 6686.2418413
11   Sold 2018-06-28 20:48:33 UTC    0.005    668424.97   3342.12485
12   Sold 2018-06-28 20:48:35 UTC    0.005    668435.82    3342.1791
13 Bought 2018-06-28 20:48:50 UTC     0.01    667898.67    6678.9867
14   Sold 2018-06-28 20:48:58 UTC     0.01    667881.57    6678.8157

для примерных строк 7, 8 и 9, возвраты строки 7 к средней цене строк 8 и 9, То же самое для строк 11, 12 и 13.

Иногда у меня может быть 3 bought и 1 Sold или 4 sold и 1 bought, но величины всегда совпадают между bought и sold

Вот более длинный кадр данных.

9    Sold 2018-06-28 20:47:48 UTC     0.0022    667514.96     1468.532912
10 Bought 2018-06-28 20:48:17 UTC       0.01 668624.18413    6686.2418413
11   Sold 2018-06-28 20:48:33 UTC      0.005    668424.97      3342.12485
12   Sold 2018-06-28 20:48:35 UTC      0.005    668435.82       3342.1791
13 Bought 2018-06-28 20:48:50 UTC       0.01    667898.67       6678.9867
14   Sold 2018-06-28 20:48:58 UTC       0.01    667881.57       6678.8157
15 Bought 2018-06-28 20:49:54 UTC       0.01    668941.24       6689.4124
16   Sold 2018-06-28 20:50:01 UTC       0.01    668895.62       6688.9562
17 Bought 2018-06-28 20:50:09 UTC       0.01 668363.00995    6683.6300995
18   Sold 2018-06-28 20:50:16 UTC       0.01    668598.95       6685.9895
19 Bought 2018-06-28 20:50:26 UTC       0.01    668302.02       6683.0202
20   Sold 2018-06-28 20:51:13 UTC       0.01    668048.75       6680.4875
21 Bought 2018-06-28 20:51:21 UTC       0.01    667738.97       6677.3897
22   Sold 2018-06-28 20:51:26 UTC       0.01    667407.38       6674.0738
23 Bought 2018-06-28 20:51:45 UTC       0.01    667202.04       6672.0204
24   Sold 2018-06-28 20:51:51 UTC       0.01    667691.88       6676.9188
25 Bought 2018-06-28 20:51:59 UTC       0.01    667555.03       6675.5503
26   Sold 2018-06-28 20:52:02 UTC       0.01 668399.88746    6683.9988746
27 Bought 2018-06-28 20:53:19 UTC       0.01    669015.02       6690.1502
28   Sold 2018-06-28 20:53:28 UTC       0.01    669273.76       6692.7376
29 Bought 2018-06-28 20:53:42 UTC       0.01 668927.79587    6689.2779587
30   Sold 2018-06-28 20:53:47 UTC       0.01     669060.9        6690.609
31 Bought 2018-06-28 20:53:52 UTC       0.01     668489.0         6684.89
32   Sold 2018-06-28 20:56:04 UTC       0.01    668645.62       6686.4562
33 Bought 2018-06-28 20:56:11 UTC       0.01 668350.04001    6683.5004001
34   Sold 2018-06-28 20:56:15 UTC       0.01    669108.98       6691.0898
35 Bought 2018-06-28 20:57:07 UTC       0.01    669105.02       6691.0502
36   Sold 2018-06-28 20:57:10 UTC       0.01    669413.97       6694.1397
37 Bought 2018-06-28 20:57:13 UTC       0.01 669028.23458    6690.2823458
38   Sold 2018-06-28 20:57:32 UTC       0.01    668769.98       6687.6998
39 Bought 2018-06-28 20:57:58 UTC      0.001    668442.05       668.44205
40 Bought 2018-06-28 20:58:31 UTC      0.009    668602.02      6017.41818
41   Sold 2018-06-28 20:58:35 UTC       0.01    668293.98       6682.9398
42 Bought 2018-06-28 20:59:04 UTC       0.01    668626.02       6686.2602
43   Sold 2018-06-28 20:59:43 UTC       0.01    668892.83       6688.9283
44 Bought 2018-06-28 20:59:54 UTC       0.01    669230.64       6692.3064
45   Sold 2018-06-28 21:02:40 UTC       0.01    668746.96       6687.4696
46 Bought 2018-06-28 21:02:49 UTC       0.01    668019.58       6680.1958
47   Sold 2018-06-28 21:03:43 UTC       0.01    667884.01       6678.8401
50 Bought 2018-06-28 21:08:27 UTC      0.004 666489.20149   2665.95680596
51   Sold 2018-06-28 21:08:33 UTC      0.004    667159.97      2668.63988
52 Bought 2018-06-28 21:09:02 UTC      0.004     666584.0        2666.336
53   Sold 2018-06-28 21:09:08 UTC      0.004    666740.27      2666.96108
54 Bought 2018-06-28 21:09:11 UTC      0.004     666243.0        2664.972
55   Sold 2018-06-28 21:09:35 UTC      0.004    664868.94      2659.47576
56 Bought 2018-06-28 21:09:38 UTC      0.004    664281.02      2657.12408
57   Sold 2018-06-28 21:09:40 UTC      0.004 663341.23067   2653.36492268
58 Bought 2018-06-28 21:09:46 UTC      0.004 663304.22439   2653.21689756
59   Sold 2018-06-28 21:09:51 UTC      0.004    663513.95       2654.0558
60 Bought 2018-06-28 21:10:00 UTC      0.004 662645.73213   2650.58292852
61   Sold 2018-06-28 21:10:03 UTC      0.004 663052.46877   2652.20987508
62 Bought 2018-06-28 21:10:17 UTC      0.004 663231.30528   2652.92522112
63   Sold 2018-06-28 21:10:22 UTC      0.004 662929.97241   2651.71988964
64 Bought 2018-06-28 21:10:25 UTC      0.004    662654.21      2650.61684
65   Sold 2018-06-28 21:10:29 UTC      0.004    662415.95       2649.6638
66 Bought 2018-06-28 21:10:32 UTC 0.00273645    662007.02  1811.549109879
67 Bought 2018-06-28 21:10:34 UTC 0.00126355    661876.02   836.313445071
68   Sold 2018-06-28 21:10:41 UTC      0.004    661164.97      2644.65988
69 Bought 2018-06-28 21:10:45 UTC      0.004    660556.03      2642.22412
70   Sold 2018-06-28 21:10:48 UTC      0.004    660872.98      2643.49192
71 Bought 2018-06-28 21:11:30 UTC      0.004    661167.27      2644.66908

Если у кого-то есть идея, спасибо

Answer 1

Можно сгруппировать данные по каждому вхождению Bought, а затем вычислить сумму разницы между Sold и "Куплено" для каждой группы.

Решение с использованием dplyr может быть следующим:

library(dplyr)

df %>% group_by(Grp = cumsum(Type=="Bought")) %>%
  summarise(Date = first(Date), 
            ReturnVal = sum(Amount[Type=="Sold"]) / sum(Amount[Type=="Bought"])) %>%
  as.data.frame()

#   Grp                    Date ReturnVal
# 1   1 2018-06-28 20:47:39 UTC 1.0003639
# 2   2 2018-06-28 20:48:17 UTC 0.9997102
# 3   3 2018-06-28 20:48:50 UTC 0.9999744

Примечание: Я выбрал отображение 1-й даты вместе со сводными данными. Можно включить дополнительные столбцы.

Данные:

df <-read.table(text="
Type                    Date Qty        Price       Amount
Bought '2018-06-28 20:47:39 UTC'     0.01 667272.13336 6672.7213336
Sold '2018-06-28 20:47:48 UTC'   0.0078    667514.96  5206.616688
Sold '2018-06-28 20:47:48 UTC'   0.0022    667514.96  1468.532912
Bought '2018-06-28 20:48:17 UTC'     0.01 668624.18413 6686.2418413
Sold '2018-06-28 20:48:33 UTC'    0.005    668424.97   3342.12485
Sold '2018-06-28 20:48:35 UTC'    0.005    668435.82    3342.1791
Bought '2018-06-28 20:48:50 UTC'     0.01    667898.67    6678.9867
Sold '2018-06-28 20:48:58 UTC'     0.01    667881.57    6678.8157",
header = TRUE, stringsAsFactors = FALSE)