Python投资组合开发嵌套函数neg_sharpe_ratio缺失参数报错求解

报错是scipy.optimize.minimize调用时传递的额外参数不匹配导致的，根因有两点：

• 你定义的嵌套函数neg_sharpe_ratio声明了4个入参：weights、riskfree_rate、er、cov，但minimize调用时args参数仅传了(cov,)一个值，缺失另外两个必要参数
• 即使外层msr已经定义了er、cov，只要你把这两个变量写在了neg_sharpe_ratio的入参列表里，Python就会要求调用时显式传值，不会自动复用外层的同名变量

方案1：利用闭包特性简化代码（更推荐） #

嵌套在msr内部的函数天然可以直接访问外层函数的所有局部变量，不需要把riskfree_rate、er、cov放到内层函数的入参列表里，修改后代码如下：

from scipy.optimize import minimize
import numpy as np
# 需提前导入你用到的erk模块

def msr(riskfree_rate, er, cov):
    n = er.shape[0]
    init_guess = np.repeat(1/n, n)
    bounds = ((0.0, 1.0),) * n
    weights_sum_to_1 = {
        'type': 'eq',
        'fun': lambda weights: np.sum(weights) - 1
    }
    # 内层函数仅保留weights参数，直接复用外层变量
    def neg_sharpe_ratio(weights):
        r = erk.portfolio_return(weights, er)
        vol = erk.portfolio_vol(weights, cov)
        return -(r - riskfree_rate) / vol

    results = minimize(
        neg_sharpe_ratio, 
        init_guess, 
        method="SLSQP", 
        options={'disp': False},
        constraints=(weights_sum_to_1),
        bounds=bounds
    )
    return results.x

方案2：补全args参数传递 #

如果你需要保留内层函数的完整参数声明，就按参数顺序补全args里的所有值：

from scipy.optimize import minimize
import numpy as np
# 需提前导入你用到的erk模块

def msr(riskfree_rate, er, cov):
    n = er.shape[0]
    init_guess = np.repeat(1/n, n)
    bounds = ((0.0, 1.0),) * n
    weights_sum_to_1 = {
        'type': 'eq',
        'fun': lambda weights: np.sum(weights) - 1
    } 
    def neg_sharpe_ratio(weights, riskfree_rate, er, cov):
        r = erk.portfolio_return(weights, er)
        vol = erk.portfolio_vol(weights, cov)
        return -(r - riskfree_rate)/vol

    results = minimize(
        neg_sharpe_ratio, 
        init_guess, 
        # 按参数顺序补全三个额外参数
        args=(riskfree_rate, er, cov), 
        method="SLSQP", 
        options={'disp': False},
        constraints=(weights_sum_to_1),
        bounds=bounds
    )
    return results.x

内容的提问来源于stack exchange，提问作者Roms