我有一个简单的股票投资组合模拟，我正在尝试建模，但尽管进行了一些尝试，但我无法找到一种矢量化它的方法。也许这是不可能的，但我想看看那里是否有人有任何想法。
我的症结在于，某一天的股票是两天前的账户价值和股票价格的函数。但是一天的账户价值是前一天的账户价值和今天的股票数量和股价变化的函数。所以股票和账户价值之间存在来回关系，我想不出一种矢量化的方法，因此我下面唯一的解决方案是下面的 for 循环。
import pandas as pd
import numpy as np
stats = pd.DataFrame(index = range(0,10))
stats['Acct Val'] = 0.0
stats['Shares'] = 0.0
stats['Stock Px'] = pd.Series([23,25,24,26,22,23,25,25,26,24],index=stats.index)
# Wgt is the percentage of the account value that should be invested in the stock on a given day
stats['Wgt'] = pd.Series([0.5,0.5,0.5,0.5,0.3,0.4,0.4,0.2,0.2,0.0,],index=stats.index)
stats['Daily PNL'] = 0.0
# Start the account value at $10,000.00
stats.ix[0:1, 'Acct Val'] = 10000.0
stats.ix[0:1, 'Wgt'] = 0
for date_loc in range(2, len(stats.index)):
# Keep shares the same unless 'wgt' column changes
if stats.at[date_loc,'Wgt'] != stats.at[date_loc-1,'Wgt']:
# Rebalanced shares are based on the acct value and stock price two days before
stats.at[date_loc,'Shares'] = stats.at[date_loc-2,'Acct Val'] * stats.at[date_loc,'Wgt'] / stats.at[date_loc-2,'Stock Px']
else:
stats.at[date_loc,'Shares'] = stats.at[date_loc-1,'Shares']
# Daily PNL is simply the shares owned on a day times the change in stock price from the previous day to the next
stats.at[date_loc,'Daily PNL'] = stats.at[date_loc,'Shares'] * (stats.at[date_loc,'Stock Px'] - stats.at[date_loc-1,'Stock Px'])
# Acct value is yesterday's acct value plus today's PNL
stats.at[date_loc,'Acct Val'] = stats.at[date_loc-1,'Acct Val'] + stats.at[date_loc,'Daily PNL']
In [44]: stats
Out[44]:
Acct Val Shares Stock Px Wgt Daily PNL
0 10000.000000 0.000000 23 0.0 0.000000
1 10000.000000 0.000000 25 0.0 0.000000
2 9782.608696 217.391304 24 0.5 -217.391304
3 10217.391304 217.391304 26 0.5 434.782609
4 9728.260870 122.282609 22 0.3 -489.130435
5 9885.451505 157.190635 23 0.4 157.190635
6 10199.832776 157.190635 25 0.4 314.381271
7 10199.832776 85.960448 25 0.2 0.000000
8 10285.793224 85.960448 26 0.2 85.960448
9 10285.793224 0.000000 24 0.0 -0.000000
In [45]:
编辑：2013 年 10 月 19 日晚上 11:01：
我尝试使用 foobarbecue 的代码，但无法到达：
import pandas as pd
import numpy as np
stats = pd.DataFrame(index = range(0,10))
stats['Acct Val'] = 10000.0
stats['Shares'] = 0.0
stats['Stock Px'] = pd.Series([23,25,24,26,22,23,25,25,26,24],index=stats.index)
# Wgt is the percentage of the account value that should be invested in the stock on a given day
stats['Wgt'] = pd.Series([0.5,0.5,0.5,0.5,0.3,0.4,0.4,0.2,0.2,0.0,],index=stats.index)
stats['Daily PNL'] = 0.0
# Start the account value at $10,000.00
#stats.ix[0:1, 'Acct Val'] = 10000.0
stats.ix[0:1, 'Wgt'] = 0
def function1(df_row):
#[stuff you want to do when Wgt changed]
df_row['Shares'] = df_row['Acct Val'] * df_row['Wgt2ahead'] / df_row['Stock Px']
return df_row
def function2(df_row):
#[stuff you want to do when Wgt did not change]
df_row['Shares'] = df_row['SharesPrevious']
return df_row
#Find where the Wgt column changes
stats['WgtChanged']=stats.Wgt.diff() <> 0 # changed ">" to "<>"
#Using boolean indexing, choose all rows where Wgt changed and apply a function
stats['Wgt2ahead'] = stats['Wgt'].shift(-2)
stats = stats.apply(lambda df_row: function1(df_row) if df_row['WgtChanged'] == True else df_row, axis=1)
stats['Shares'] = stats['Shares'].shift(2)
#Likewise, for rows where Wgt did not change
stats['SharesPrevious'] = stats['Shares'].shift(1)
stats = stats.apply(lambda df_row: function2(df_row) if df_row['WgtChanged'] == False else df_row, axis=1)