numpy.polyfit  多项式拟合
numpy.polyfit(x, y, deg)
x 自变量数组
y 因变量数组
deg  要拟合的多项式的次数


import numpy as np
 
x = np.array([1, 2, 3, 4, 5])
y = np.array([3, 4, 5, 6, 7])
 
coefficients = np.polyfit(x, y, deg=1)
print(coefficients)

[1. 2.]
y = x + 2


金融时序数据分析
斜率  趋势线

沪深300 20230818 到 20230825的数据

20230818 3784.00
20230821 3729.56
20230822 3758.23
20230823 3696.63
20230824 3723.43
20230825 3709.15 

[3784.00,3729.56,3758.23,3696.63,3723.43,3709.15]

x = np.array([1,2,3,4,5,6])

y = np.array([3784.00,3729.56,3758.23,3696.63,3723.43,3709.15])
 
coefficients = np.polyfit(x, y, deg=1)
print(coefficients)
print(coefficients[0] / coefficients[1])

[ -12.97828571 3778.924     ]
-0.0034343865381482983

同期ETF 数据
y = np.array([3.852,3.800,3.823,3.768,3.791,3.784])

[-0.01205714  3.8452    ]
-0.003135634780282715





数组数据 除以 第一个值  

x = np.array([1,2,3,4,5,6])
y = np.array([3784.00,3729.56,3758.23,3696.63,3723.43,3709.15])
# y = np.array([3.852,3.800,3.823,3.768,3.791,3.784])
y = y / y[0]
print(y)
coefficients = np.polyfit(x, y, deg=1)
print(coefficients)



沪深300指数
[1.         0.98561311 0.99318975 0.97691068 0.98399313 0.98021934]
[-0.00342978  0.99865856]

沪深300ETF
[1.         0.98650052 0.99247144 0.97819315 0.98416407 0.98234683]
[-0.0031301   0.99823468]