使用 Python pandas 计算调整后的成本基础（股票投资组合分析 Buy/Sell）

Question

我正在尝试对我的交易进行投资组合分析，并尝试计算调整后的成本基价。我几乎尝试了所有方法，但似乎没有任何效果。我可以计算出调整后的数量，但无法获得调整后的购买价格有人可以帮忙吗？

这是样本交易日志原始数据

import pandas as pd
import numpy as np

raw_data = {'Date':  ['04-23-2020', '05-05-2020', '05-05-2020', '05-11-2020', '05-11-2020', 
              '05-12-2020', '05-12-2020', '05-27-2020', '06-03-2020', '06-03-2020', 
              '06-03-2020', '06-03-2020', '06-03-2020'],
    'Type': ['Buy', 'Buy', 'Buy', 'Buy', 'Buy', 'Buy', 'Buy', 
             'Sell', 'Sell', 'Sell', 'Buy', 'Sell', 'Sell'],
    'Symbol': ['TSE:AC', 'TSE:AC', 'TSE:HEXO', 'TSE:BPY.UN', 'TSE:BPY.UN', 
               'TSE:BPY.UN', 'TSE:AC', 'TSE:BPY.UN', 'TSE:AC', 'TSE:BPY.UN', 
               'TSE:AC', 'TSE:BPY.UN', 'TSE:HEXO'],
    'Quantity': [75, 100, 1450, 200, 50, 80, 150, 100, 125, 100, 100, 50, 1450],
    'Amount per unit': [18.04, 17.29, 0.73, 13.04, 13.06, 12.65, 15.9, 15.01, 
                        18.05, 14.75, 15.8, 14.7, 1.07],
    'Turnover': [1353, 1729, 1058.5, 2608, 653, 1012, 2385, 1501, 2256.25, 1475, 1580, 735, 1551.5],
    }
df = pd.DataFrame (raw_data, columns = ['Date','Type','Symbol','Quantity','Amount per unit', 'Turnover']).sort_values(['Date','Symbol']).reset_index(drop = True)

我能够毫无问题地获得调整后的数量，但我无法获得正确的调整后单价。这里的条件是，如果我卖出一只股票，我的调整后单价不应改变，并保持与买入该股票时的最后调整价相同。

#to calculate adjusted quantity. this works as expected
df['Adjusted Quantity'] = df.apply(lambda x: ((x.Type == "Buy") - (x.Type == "Sell")) * x['Quantity'], axis = 1)
df['Adjusted Quantity'] = df.groupby('Symbol')['Adjusted Quantity'].cumsum()


#section where I am having problem. Works good until I reach the row where sell was made
df['Adjusted Price Per Unit'] = df.apply(lambda x: ((x.Type == "Buy") - (x.Type == "Sell")) * x['Turnover'], axis = 1)
df['Adjusted Price Per Unit'] = df.groupby('Symbol')['Adjusted Price Per Unit'].cumsum().div(df['Adjusted Quantity'])

运行此代码将产生以下结果

例如：索引 7 处行的调整后价格应为 12.948（与索引 6 处的行相同）而不是 12.052。此外，最后一行调整后的价格应为 0.73（与索引 2 处的行相同），因为我正在买卖相同数量的股票。

示例 2：在指数 6，我以 12.65 的价格购买了 80 股 BPY，这使我的平均价格降至 12.94，总共 330 股 (250+80)。现在，我以 15.01（指数 7）的价格卖出 100 股。我的代码将调整后的成本调整为 12.05。我需要调整后的成本为 12.94 而不是 12.05。简单地说，如果交易类型是卖出，则忽略调整价格。使用该特定股票的最后一次购买类型交易中的最后调整价格。

我的代码的最后两行不正确。你能帮我正确计算调整后的单价吗？谢谢:)

Answer 1

如果您不计算销售的调整价格，就像您评论的那样，那么您可以将销售行处理为 NA，并用同一股票的前一个值填充它。作为您代码中的确认，您在开始计算'Adjusted Quantity'时是否不需要考虑相同的股票？

df.sort_values(['Symbol','Date','Type'], ascending=[True, True, True], inplace=True)
# your code
df['Adjusted Quantity'] = df.apply(lambda x: ((x.Type == "Buy") - (x.Type == "Sell")) * x['Quantity'], axis = 1)
df['Adjusted Quantity'] = df.groupby('Symbol')['Adjusted Quantity'].cumsum()
df['Adjusted Price Per Unit'] = df.apply(lambda x: ((x.Type == "Buy") - (x.Type == "Sell")) * x['Turnover'], axis = 1)
df['Adjusted Price Per Unit'] = df.groupby('Symbol')['Adjusted Price Per Unit'].cumsum().div(df['Adjusted Quantity'])

df.loc[df['Type'] == 'Sell',['Adjusted Price Per Unit']] = np.NaN
df.fillna(method='ffill', inplace=True)

|    | Date       | Type   | Symbol     |   Quantity |   Amount per unit |   Turnover |   Adjusted Quantity |   Adjusted Price Per Unit |
|---:|:-----------|:-------|:-----------|-----------:|------------------:|-----------:|--------------------:|--------------------------:|
|  0 | 04-23-2020 | Buy    | TSE:AC     |         75 |             18.04 |    1353    |                  75 |                   18.04   |
|  1 | 05-05-2020 | Buy    | TSE:AC     |        100 |             17.29 |    1729    |                 175 |                   17.6114 |
|  5 | 05-12-2020 | Buy    | TSE:AC     |        150 |             15.9  |    2385    |                 325 |                   16.8215 |
|  9 | 06-03-2020 | Buy    | TSE:AC     |        100 |             15.8  |    1580    |                 425 |                   16.5812 |
|  8 | 06-03-2020 | Sell   | TSE:AC     |        125 |             18.05 |    2256.25 |                 300 |                   16.5812 |
|  3 | 05-11-2020 | Buy    | TSE:BPY.UN |        200 |             13.04 |    2608    |                 200 |                   13.04   |
|  4 | 05-11-2020 | Buy    | TSE:BPY.UN |         50 |             13.06 |     653    |                 250 |                   13.044  |
|  6 | 05-12-2020 | Buy    | TSE:BPY.UN |         80 |             12.65 |    1012    |                 330 |                   12.9485 |
|  7 | 05-27-2020 | Sell   | TSE:BPY.UN |        100 |             15.01 |    1501    |                 230 |                   12.9485 |
| 10 | 06-03-2020 | Sell   | TSE:BPY.UN |        100 |             14.75 |    1475    |                 130 |                   12.9485 |
| 11 | 06-03-2020 | Sell   | TSE:BPY.UN |         50 |             14.7  |     735    |                  80 |                   12.9485 |
|  2 | 05-05-2020 | Buy    | TSE:HEXO   |       1450 |              0.73 |    1058.5  |                1450 |                    0.73   |
| 12 | 06-03-2020 | Sell   | TSE:HEXO   |       1450 |              1.07 |    1551.5  |                   0 |                    0.73   |

使用 Python pandas 计算调整后的成本基础（股票投资组合分析 Buy/Sell）

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