限制scipy.optimize抛出x0错误

如何解决限制scipy.optimize抛出x0错误

要获取一份股票清单并调整其在投资组合中的权重，直到整个投资组合beta为1.0时，“ stonkBetas”的输出都是静态的，并且是：

[3.19292010501853,0.7472001935364129,1.0889157697158605,0.8944059912707691,0.04192080860817828,1.0011520737327186,0.9155119223385676]

然后创建两个函数。一个定义beta加权的方式。第二个缺点就是约束最小化加权投资组合的总和的beta总和为1.0。

def betaOpp(weights):
    a,b,c,d,e,f,g=weights
    f=a*stonkBetas[0]+b*stonkBetas[1]+c*stonkBetas[2]+d*stonkBetas[3]+e*stonkBetas[4]+f*stonkBetas[5]+g*stonkBetas[6]
    return f

initial_guess = [.1,.1,.2,.2]
print('hi')
print(sum(initial_guess))
print('bye')

def con(t):
    print('this should be zero:')
    print(sum(t)-1)
    return sum(t) - 1.0

cons = {'type':'eq','fun': con}
bnds = ((.02,.8),(.02,.8))
res = optimize.minimize(betaOpp,initial_guess,bounds=bnds,constraints=cons)
print(res)

这给了我这个输出

hi
1.0
bye
this should be zero:
0.0
this should be zero:
0.0
this should be zero:
0.0
this should be zero:
1.4901161193847656e-08
this should be zero:
1.4901161193847656e-08
this should be zero:
1.4901161193847656e-08
this should be zero:
1.4901161193847656e-08
this should be zero:
1.4901161193847656e-08
this should be zero:
1.4901161193847656e-08
this should be zero:
1.4901161193847656e-08
this should be zero:
6.661338147750939e-16
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-30-6567109e94a4> in <module>
     16 cons = {'type':'eq','fun': con}
     17 bnds = ((.02,.8))
---> 18 res = optimize.minimize(betaOpp,x0=initial_guess,constraints=cons)
     19 print(res)

/opt/miniconda3/lib/python3.6/site-packages/scipy/optimize/_minimize.py in minimize(fun,x0,args,method,jac,hess,hessp,bounds,constraints,tol,callback,options)
    624     elif meth == 'slsqp':
    625         return _minimize_slsqp(fun,--> 626                                constraints,callback=callback,**options)
    627     elif meth == 'trust-constr':
    628         return _minimize_trustregion_constr(fun,/opt/miniconda3/lib/python3.6/site-packages/scipy/optimize/slsqp.py in _minimize_slsqp(func,maxiter,ftol,iprint,disp,eps,finite_diff_rel_step,**unkNown_options)
    424 
    425         if mode == -1:  # gradient evaluation required
--> 426             g = append(sf.grad(x),0.0)
    427             a = _eval_con_normals(x,cons,la,n,m,meq,mieq)
    428 

/opt/miniconda3/lib/python3.6/site-packages/scipy/optimize/_differentiable_functions.py in grad(self,x)
    186         if not np.array_equal(x,self.x):
    187             self._update_x_impl(x)
--> 188         self._update_grad()
    189         return self.g
    190 

/opt/miniconda3/lib/python3.6/site-packages/scipy/optimize/_differentiable_functions.py in _update_grad(self)
    169     def _update_grad(self):
    170         if not self.g_updated:
--> 171             self._update_grad_impl()
    172             self.g_updated = True
    173 

/opt/miniconda3/lib/python3.6/site-packages/scipy/optimize/_differentiable_functions.py in update_grad()
     90                 self.ngev += 1
     91                 self.g = approx_derivative(fun_wrapped,self.x,f0=self.f,---> 92                                            **finite_diff_options)
     93 
     94         self._update_grad_impl = update_grad

/opt/miniconda3/lib/python3.6/site-packages/scipy/optimize/_numdiff.py in approx_derivative(fun,rel_step,abs_step,f0,sparsity,as_linear_operator,kwargs)
    389 
    390     if np.any((x0 < lb) | (x0 > ub)):
--> 391         raise ValueError("`x0` violates bound constraints.")
    392 
    393     if as_linear_operator:

ValueError: `x0` violates bound constraints.

我只是不明白我要去哪里错了。 x0完美为1.0-我可以看到它！希望我在这里做些愚蠢的事情。请帮忙！