Question

我对功能的最小化有疑问。我将发布所有代码。我知道代码太多，但是我不知道错误在哪里：首先是数据：

Data:
date = dt.datetime(2018, 6, 26)

maturity_dtime = DatetimeIndex(['2020-04-07', '2020-08-07', '2020-12-07', '2023-12-07',
       '2027-12-07', '2032-12-07', '2040-02-07'],
      dtype='datetime64[ns]', freq=None)

data['Price']
           Price
    0    108.360589
    1    102.579383
    2    104.241200
    3    106.449767
    4    124.687315
    5    133.424299
    6    107.836756
    Name: Dirty Price, dtype: float64
YF_tenors = [0.0027397260273972603, 0.0821917808219178, 0.25205479452054796, 0.5013698630136987, 0.7479452054794521, 1.0, 2.0027397260273974]
tenors_dates = [datetime.datetime(2018, 6, 27, 0, 0), datetime.datetime(2018, 7, 26, 0, 0), datetime.datetime(2018, 9, 26, 0, 0), datetime.datetime(2018, 12, 26, 0, 0), datetime.datetime(2019, 3, 26, 0, 0), datetime.datetime(2019, 6, 26, 0, 0), datetime.datetime(2020, 6, 26, 0, 0)]

然后，我开始计算费率。我使用6个参数，这些参数是我必须最小化的参数，以使平方误差之和最小：

''' Calculation the rates '''
parameters = [0.03,-0.03,0.0,0.0,1.0,1.0]
# Here is the function that use the parameters
def rates(params, t):
    beta0, beta1, beta2, beta3, tau1, tau2 = params
    rate =  np.exp(beta0 + (beta1 + beta2) * (1 - np.exp(-t/tau1)) * tau1/t - beta2 * np.exp(-t/tau1) + beta3 * (1 - np.exp(-t/tau2)) * tau2 /t - beta3 * np.exp(-t/ tau2)) -1
    return rate

def tenors_rates():
    rates_tenors=[]
    for aux in range(len(YF_tenors)):
        rate_aux=rates(parameters,YF_tenors[aux])
        rates_tenors.append(rate_aux)
    return rates_tenors

curve=['act/365','Lineal','Anual']
def curve_data(curve):
    for aux in range(len(tenors_rates())):
        curve_aux=[tenors_dates[aux],tenors_rates()[aux]]
        curve.append(curve_aux)
'''This is the interpolation function for the curve'''
def interpol_curva(value_date,maturity_date,curve):
    base=curve[0]
    interpol=curve[1]
    #compo_fg=curve[2]
    nrows=int(len(curve))
    if (maturity_date > curve[nrows-1][0]).any(): 
        maturity_date=curve[nrows-1][0]
    if maturity_date<curve[3][0]: #
        maturity_date=curve[3][0]
    r1=3
    while maturity_date>curve[r1][0] and r1<nrows-1:
        r1=r1+1
    r1=r1-1
    if r1==2:
        r1=3
    if r1>=nrows-1:
        r1=nrows-2
    r2=r1+1
    if base=='act/360' or base=='act/365':
        yf1=(maturity_date-curve[r1][0]).days
        yf2=(curve[r2][0]-maturity_date).days
        yftt=(curve[r2][0]-curve[r1][0]).days
    else:
        print("fail")
    if interpol=='Lineal':
        return (curve[r1][1]*yf2+curve[r2][1]*yf1)/yftt

''' Day Count '''
def day_count(start_date, end_date, basis):
    if basis == 'act/365':
        days = (end_date - start_date).days
        return days
    else:
        print('Basis not defined')

''' Year Fraction '''
def year_fraction(start_date, end_date, basis):
    if basis == 'act/365':
        yf = day_count(start_date ,end_date,basis) / 365.0
    return yf

def Discount_Factor_2(value_date,maturity_date,curve):
    basis=curve[0]
    Composition=curve[2]
    yf = year_fraction(value_date, maturity_date, basis)
    r=interpol_curva(value_date,maturity_date,curve) valor?

    if Composition == "Anual":
        df = 1 / (1 + r) ** yf
    else:
        print("fail")
    return df

所有这些代码之后，我尝试最小化下一个目标函数，即平方误差之和：

'''Minimization'''
def objective(params):
    beta0, beta1, beta2, beta3, tau1, tau2 = parameters
    return (((100 * Discount_Factor_2(value_date, maturity_dtime, curve)) - data['Price'])**2).sum()
x0 = [0.1,0.1,0.1,0.1,1,1] #Initial values of beta0, beta1...

我执行以下操作：

res = minimize(objective, x0,method="SLSQP", tol=0.00000000001)

，它不会失败，但不会改变任何内容。我认为问题在于目标函数不使用参数（x0），而是向后移动，在某些情况下，它应该到达参数所在的rates函数。抱歉，冗长的代码，但我坚持这样做。任何帮助都是完美的，非常感谢您抽出宝贵的时间并尝试提供帮助。

如何使用参数inscipy最小化此功能？

0 个答案: