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python - 未指定位置时如何查找 TypeError？

问题描述

我正在测试我找到的一些代码，但我无法识别错误。

编码：

import datetime as dt
from datetime import date
import pandas as pd
import pandas_datareader.data as web
import numpy as np
import time
import math
import scipy.optimize as optimize


start = dt.datetime(2016,12,1)
end = dt.datetime(2020,12,1)


tick = ['GOOG', 'AAPL', 'AMZN']


#pandas dataframe
data = web.DataReader(tick, 'yahoo', start, end)['Adj Close']
data = np.log(data/data.shift(1))


def sharpetest(wts, returns):
  weights = np.array(wts)
  port_return = np.sum(returns.mean() * weights) * 252
  port_vol = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))
  sharpe = port_return/port_vol
  sharpe = np.array(sharpe)
  return sharpe
  

num_assets = len(tick)

constraints = ({'type' : 'eq', 'fun': lambda x: np.sum(x) -1})
bounds = tuple((0,1) for x in range(num_assets))
args = (num_assets * [1./num_assets,], data)


optimal_sharpe=optimize.minimize(sharpetest,
                               args,
                               method = 'SLSQP',
                               bounds = bounds,
                               constraints = constraints)
print(optimal_sharpe)

输出：

   /usr/local/lib/python3.9/site-packages/numpy/core/_asarray.py:83:
   VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list- 
   or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If 
   you meant to do this, you must specify 'dtype=object' when creating the ndarray
   return array(a, dtype, copy=False, order=order)
   TypeError: float() argument must be a string or a number, not 'list'

如您所见，未指定 TypeError 的行。我如何找到错误？

我很抱歉问了这样一个基本的问题。

标签： pythonarrayspandasnumpyscipy

解决方案

类型错误来自函数的应用程序sharpetest。它来自将权重与数据相结合。以下是如何更正代码的示例。

constraints = ({'type' : 'eq', 'fun': lambda x: np.sum(x) -1})
bounds = tuple((0,1) for x in range(num_assets))
x0 = num_assets * [1./num_assets,]
args =  (data)

print(sharpetest(num_assets * [1./num_assets,], data))

optimal_sharpe=optimize.minimize(sharpetest,
                               x0,
                               args,
                               method = 'SLSQP',
                               bounds = bounds,
                               constraints = constraints)
print(optimal_sharpe)

您可以看到它x0被分解为它自己的参数，然后附加数据（股票的回报）作为参数传入。你有一个非常有趣的例子！

我得到的输出是

     fun: array(0.79108107)
     jac: array([-7.45058060e-09,  7.86704488e-01,  7.25132324e-01])
 message: 'Optimization terminated successfully'
    nfev: 12
     nit: 3
    njev: 3
  status: 0
 success: True
       x: array([1.00000000e+00, 1.38777878e-16, 0.00000000e+00])

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