如何解决使用Python熊猫计算调整后的成本基础股票买卖组合分析
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 |
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