Question

我有一个熊猫数据框，其中包含并非每天都发生且并非针对每种股票的股票交易：

目标是获取每天每只股票的（每日）权重。

这意味着 -创建完整的日期日历 -在每个日期重复每只股票的累积股份 -并最终计算出该日期的权重

请问有人可以帮助我吗？我已经在搜索多个线程，但是找不到任何可行的解决方案。

Answer 1

感谢您的提问。由于要构建用于投资的数据框，因此在此代码上花了点功夫，所以这是个好习惯。试试看，我认为它可以满足您的要求。

import pandas as pd
import datetime

# create df
trades = pd.DataFrame(index=['2011-02-16', '2011-02-16', '2011-02-17', '2014-03-20','2014-03-20', '2018-01-04'])

# build data
trades['stock'] = ['A', 'B', 'A', 'B', 'C', 'B']
trades['shares_Tr'] = [5,10,5,10,15,-20]

# create a range of dates for the balance dataframe
index_of_dates = pd.date_range(('2011-02-10'), ('2018-01-05')).tolist()

# create a balance dataframe with columns for each stock. 
bal = pd.DataFrame(data = 0, index=index_of_dates, columns=['A', 'A_sum', 'A_weight', 'B', 'B_sum', 'B_weight',  'C', 'C_sum', 'C_weight', 'Total' ])

# populate the trades from trades df to the balance df.
for index, row in trades.iterrows():
    bal.loc[index, row['stock']] = row['shares_Tr']

# track totals
bal['A_sum'] = bal['A'].cumsum()
bal['B_sum'] = bal['B'].cumsum()
bal['C_sum'] = bal['C'].cumsum()
bal['Total'] = bal.iloc[:,[1,4,7]].sum(axis=1)
bal['A_weight'] = bal['A_sum'] / bal['Total']
bal['B_weight'] = bal['B_sum'] / bal['Total']
bal['C_weight'] = bal['C_sum'] / bal['Total']

您将有两个数据框，一个称为交易，另一个称为bal，用于保存结果。

Answer 2

太棒了！这激发了我找到解决问题的方法！解决方案中的问题是，如果库存D出现在初始数据集中（添加到下面的集合中），它将不再起作用。

我可以通过以下方法解决此问题：

import pandas as pd
import datetime

# create df // build data // adding date as column
trades = pd.DataFrame()
trades['Date'] = pd.to_datetime(['2011-02-16', '2011-02-16', '2011-02-17', '2014-03-20','2014-03-20', '2018-01-04', '2011-02-18'])
trades['stock'] = ['A', 'B', 'A', 'B', 'C', 'B', 'D']
trades['shares_Tr'] = [5,10,5,10,15,-20,5]

# create a range of dates for the merged dataframe
index_of_dates = pd.date_range('2011-02-10', pd.datetime.today()).to_frame().reset_index(drop=True).rename(columns={0: 'Date'})

# create a merged dataframe with columns date / stock / stock_Tr. 
merged = pd.merge(index_of_dates,trades,how='left', on='Date')

# create a pivottable showing the shares_TR of each stock for each date 
shares_tr = merged.pivot(index='Date', columns='stock', values='shares_Tr').dropna(axis=1, how='all').fillna(0)

# calculate individual pivottables for the cumsum and weights 
cumShares = shares_tr.cumsum()
weights = ((cumShares.T / cumShares.T.sum()).T*100).round(2)

# finally combine all data into one dataframe
all_data = pd.concat([shares_tr, cumShares, weights], axis=1, keys=['Shares','cumShares', 'Weights'])
all_data

熊猫数据框中每天每种类别的运行总计

2 个答案: