我有一个pd.DataFrame
回报系列,对应于固定支出率为5%的年份。我希望找到每年支出后的期末投资组合价值。年val_after_spending
中的t
等于年t
val_before_spending
的平均值,其中年t-1
val_after_spending乘以支出率。对于第一年,假定val_after_spending
中的t-1
为1。
我现在有一个可行的实现(如下),但是它非常慢。有没有更快的方法来实现呢?
import pandas as pd
import numpy as np
port_rets = pd.DataFrame({'port_ret': [.10,-.25,.15]})
spending_rate = .05
for index, row in port_rets.iterrows():
if index != 0:
port_rets.at[index, 'val_before_spending'] = port_rets['val_after_spending'][index - 1] * (1 + port_rets['port_ret'][index])
port_rets.at[index, 'spending'] = np.mean([port_rets['val_after_spending'][index - 1], port_rets['val_before_spending'][index]]) * spending_rate
else:
port_rets.at[index, 'val_before_spending'] = 1 * (1 + port_rets['port_ret'][index])
port_rets.at[index, 'spending'] = np.mean([1, port_rets['val_before_spending'][index]]) * spending_rate
port_rets.at[index, 'val_after_spending'] = port_rets['val_before_spending'][index] - port_rets['spending'][index]
# port_ret val_before_spending spending val_after_spending
#0 0.100000 1.100000 0.052500 1.047500
#1 -0.250000 0.785625 0.045828 0.739797
#2 0.150000 0.850766 0.039764 0.811002
最佳答案
您在代码中与熊猫的接口非常紧密,就性能而言,这似乎不是一个好主意。为了使它易于使用,熊猫需要做很多记账工作,从而导致性能下降。
我们以numpy进行所有计算,然后获得所有构件,最后构建数据帧。因此,代码转换为:
def get_vals(rates, spending_rate):
n = len(rates)
vals_after_spending = np.zeros((n+1, ))
vals_before_spending = np.zeros((n+1, ))
vals_after_spending[0] = 1.0
for i in range(n):
vals_before_spending[i+1] = vals_after_spending[i] * (1 + rates[i])
spending = np.mean(np.array([vals_after_spending[i], vals_before_spending[i+1]])) * spending_rate
vals_after_spending[i+1] = vals_before_spending[i+1] - spending
return vals_before_spending[1:], vals_after_spending[1:]
rates = np.array(port_rets["port_ret"].tolist())
vals_before_spending, vals_after_spending = get_vals(rates, spending_rate)
port_rets = pd.DataFrame({'port_ret': rates, "val_before_spending": vals_before_spending, "val_after_spending": vals_after_spending})
我们可以通过JIT编译代码来进一步改进,因为python循环很慢。
下面我用numba:
import numba as nb
@nb.njit(cache=True) # as easy as putting this decorator
def get_vals(rates, spending_rate):
n = len(rates)
vals_after_spending = np.zeros((n+1, ))
vals_before_spending = np.zeros((n+1, ))
# ... code remains same, we are just compiling the function
如果我们考虑这样的随机费率列表:
port_rets = pd.DataFrame({'port_ret': np.random.uniform(low=-1.0, high=1.0, size=(100000,))})
我们得到了性能比较:
您的代码:15.758秒
get_vals:1.407s
JITed get_vals:0.093s(第二次运行可享受折扣的编译时间)