向xlwings Python函数添加参数会使其崩溃

Question

我使用xlwings在Excel中实现Python代码。我将下面的代码放在Spyder中，并使用xlwings将其导入Excel，以便对金融资产的7资产组合执行scipy优化

@xw.func
def risk_budget_objective_error(weights,*args):

    #Covariance table occupies the first position in args variable
    covariances = args[0]

    #State risk budgets
    assets_risk_budget = args[1]



    weights = np.matrix(weights)


    #Calculate portfolio st_dev
    portfolio_stdev = calculate_portfolio_risk(weights,covariances)

    #Calculate risk contributions
    assets_risk_contribution = calculate_risk_contribution(weights,covariances)

    #Calculate desired risk contribution of each asset
    assets_risk_target = np.multiply(portfolio_stdev,assets_risk_budget).astype(float)

    #Calculate error between desired contribution and calculated distribution of each asset
    squared_error = np.square(assets_risk_contribution-assets_risk_target.T)
    sse = sum(squared_error)

    return sse

@xw.func 
def erc_weights(covariances,assets_risk_budget, num_assets):

    #Constraints to optimization

    cons = ({'type':'eq','fun':lambda x: np.sum(x) - 1.5},
                  {'type':'ineq','fun':lambda x: x})

    bounds = ((0,.50),(0,None),(0,None),(0,None),(0,None),(0,.50),(0,None),(0,.10),(0,.25))

    init_weights = [.5]*num_assets

    #Optimization in scipy
    optimize_result = minimize(risk_budget_objective_error,
                               x0 = init_weights,
                               method = 'SLSQP',
                               args = (covariances, assets_risk_budget),
                               constraints = cons, 
                               options = {'disp':True,'ftol':1e-50}
                               )

    #Get optimized weights
    weights = optimize_result.x

    return weights

我的问题是erc\ U权重函数。我试着让投资组合中的资产数量成为函数中的一个参数

反过来，我把numèassets作为一个参数，它是投资组合中的资产数量（下面显示的是经过编辑的代码），但是每次我把它作为一个参数添加时，这个函数就会崩溃并产生一个错误。要清楚的是，下面代码中唯一的更改是将num\u assets包含为参数，并将init\u weights的公式更改为[.5]*num\u assets

当我试图使其他参数内生时也会发生同样的情况。例：总重量上限，界限等，所以任何解决这个问题的方法在这些领域也一定有用。谢谢

@xw.func 
def erc_weights(covariances,assets_risk_budget, num_assets):

    #Constraints to optimization

    cons = ({'type':'eq','fun':lambda x: np.sum(x) - 1.5},
                  {'type':'ineq','fun':lambda x: x})

    bounds = ((0,.50),(0,None),(0,None),(0,None),(0,None),(0,.50),(0,None),(0,.10),(0,.25))

    init_weights = [.5]*num_assets

    #Optimization in scipy
    optimize_result = minimize(risk_budget_objective_error,
                               x0 = init_weights,
                               method = 'SLSQP',
                               args = (covariances, assets_risk_budget),
                               constraints = cons, 
                               options = {'disp':True,'ftol':1e-50}
                               )

    #Get optimized weights
    weights = optimize_result.x

    return weights