python - 使用 Python pandas 计算调整后的成本基础(股票买入/卖出的投资组合分析)

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。我能够计算调整后的数量，但无法获得调整后的购买价格 谁能帮忙？
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，但无法获得正确的每单位调整后价格。这里的条件是，如果我卖出一只股票，我的每单位调整价格不应改变，并与买入该股票时的最后调整价格保持一致。

#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'])

运行此代码将导致以下结果

例如:索引 7 行的调整后价格应为 12.948(与索引 6 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 中的行相同)，因为我正在买卖相同数量的股票。
例 2:在指数 6 时，我以 12.65 的价格买入了 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)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是 12.94 而不是 12.05。简单地说，如果交易类型为卖出，则忽略调整价格。使用该特定股票的最后一次买入类型交易中的最后调整价格。
我的代码的最后 2 行不正确。你能帮我正确计算每单位调整后的价格吗？谢谢 :)

Answer 1

如果您没有像您评论的那样计算销售的调整后价格，那么您可以将销售行作为 NA 处理，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“调整后数量”时是否不需要考虑相同的库存？

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   |