Python Finance is too var-covariance model external parameters

Python finance is too var-covariance external parameters

I have never understood what external parameter transfer is. Then I found that sys.argv in python seems to be available here... You don’t have to log in to a local editor such as Pycharm to run that piece of code online and pass it to that piece of code.
Use the dos command line It's okay too!

The operation is as follows:

Note that the first value passed is a list, the second is a decimal, and the third is an integer (decimals can also be used)

Attach code

import sys
import numpy as np
import scipy.stats as st
from pandas import DataFrame

# 设P0为固值
P0 = 1000

# 到时候外部传参下边可以用这段进行（这是ziliao的参数）
# 6.8272,6.8474,6.8647,6.8925,6.8855,6.9116,6.9180,6.9179,6.9143,6.9192,6.9215,6.9318,6.9501,6.9440,6.9363,6.9413,6.9611,6.9680,6.9535,6.9366,6.9735,6.9809


def aaa(arf,N,ziliao):
    # print(ziliao)
    #这个时候因为ziliao传入进来，是"6",".","8","2"这种形式，所以先对列表进行整合
    sum1 = ''
    for i in ziliao:
        sum1+=i
    # 这个时候sum1是一个字符串，内容包含6.8272,6.8474,6.8647,6.8925,6.8855,6.9116,6.9180,6.9179,6.9143,6.9192,6.9215,6.9318,6.9501,6.9440,6.9363,6.9413,6.9611,6.9680,6.9535,6.9366,6.9735,6.9809这些，所以进行切割
    # 同时为了方便还是取“ziliao”这个变量名，这个时候“ziliao”的内容为["6.8272","6.8474"]这些
    ziliao = sum1.split(",")

    # 为了让ziliao的内容变为[6.8272,6.8474],再用一次for循环（这是我笨人的笨方法..）
    sum=[]
    for i in ziliao:
        i = float(i)
        sum.append(i)
    # print(sum)
    ziliao = sum


    ziliao_return = DataFrame(ziliao)

    # 股票日收益
    stock_return = np.log(ziliao_return/ziliao_return.shift(1))

    # 删除缺失行
    stock_return = stock_return.dropna()
    # print(stock_return)
    a = stock_return.loc[:]
    # print(a)
    # a_mean为平均收益率  b为保留小数点后四位的a_mean
    a_mean =a.mean()
    b = "%.4f" % a_mean
    b = float(b)
    # a_mean = "%.2f%%" % (a.mean()*100)

    # p是置信水平在不同情况下（0.9或者0.95或者0.99）的平均收益率
    p = np.percentile(a, (1, 5, 10), interpolation='midpoint')

    if arf ==0.95:
        p = np.abs(p[1])
    elif arf==0.90 or arf==0.9:
        p = np.abs(p[0])
    elif arf==0.99:
        p = np.abs(p[2])
    # print(p)

    # z是置信水平在不同情况下（0.9或者0.95或者0.99）的取值
    z = st.norm.ppf(q=1 - arf)
    # z取绝对值
    z = np.abs(z)
    # print(z)

    # t是持有天数
    t = np.sqrt(N)

    # print(b,p,z,t)
    return b,p,z,t

# a1,a2,a3是外部传参，a1传参为列表
a2 = float(sys.argv[2])
# print(a2)
a3 = float(sys.argv[3])
# print(a3)
a1 = list(sys.argv[1])
# print(a1)

ttt = list(aaa(a2,a3,a1))
# print(ttt)
# print(ttt[0])
var_N = ttt[3]*P0*(ttt[2]*ttt[1]-float(ttt[0]))
print(var_N)