使用Python熊猫计算调整后的成本基础股票买卖组合分析

问题描述

我正在尝试对我的交易进行投资组合分析，并试图计算调整后的成本基准价格。我已经尝试了几乎所有内容，但似乎没有任何效果。我可以计算调整后的数量，但无法获取调整后的购买价格，有人可以帮忙吗？

这是示例贸易日志原始数据

import pandas as pd
import numpy as np

raw_data = {'Date':  ['04-23-2020','05-05-2020','05-11-2020','05-12-2020','05-27-2020','06-03-2020','06-03-2020'],'Type': ['Buy','Buy','Sell','Sell'],'Symbol': ['TSE:AC','TSE:AC','TSE:HEXO','TSE:BPY.UN','TSE:HEXO'],'Quantity': [75,100,1450,200,50,80,150,125,1450],'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，但是我无法获得正确的每单位调整后的价格。这里的条件是，如果我卖出股票，则我的每单位调整后价格不应更改，并应与购买该股票时的最后调整后价格相同。

#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。另外，由于我要买卖相同数量的股票，最后一行的调整后价格应为0.73（与索引2的行相同）。

例如2：在指数6，我以12.65的价格购买了BPY的80股股票，这使我的平均价格下降到12.94，共计330股（250 + 80）。现在，我以15.01（指数7）的价格出售100股。我的代码将调整后的成本提高到12.05。我需要调整后的费用是12.94，而不是12.05。简而言之，如果交易类型为卖出，则忽略调整价格。使用该特定股票的上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。您能帮我正确计算调整后的单价吗？谢谢：）

解决方法

如果您不按照评论来计算销售的调整价格，则可以将销售行作为NA处理，并用同一股票的前一个值填写。作为代码中的确认，在开始计算“调整数量”时，您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   |