x and y must have same first dimension, but have shapes (1,) and (50,)

[asja2010]

The error message is the following:

ValueError                                Traceback (most recent call last)
<ipython-input-182-1237a1515f04> in <module>
      5 plt.colorbar(label='sharpe ratio')
      6 plt.scatter(expectedVolatility[maxIndex],expectedReturn[maxIndex],c='red')
----> 7 plt.plot(volatility_opt,returns, '--')
      8 plt.show()

C:\Anaconda3\lib\site-packages\matplotlib\pyplot.py in plot(scalex, scaley, data, *args, **kwargs)
   2838 @_copy_docstring_and_deprecators(Axes.plot)
   2839 def plot(*args, scalex=True, scaley=True, data=None, **kwargs):
-> 2840     return gca().plot(
   2841         *args, scalex=scalex, scaley=scaley,
   2842         **({"data": data} if data is not None else {}), **kwargs)

C:\Anaconda3\lib\site-packages\matplotlib\axes\_axes.py in plot(self, scalex, scaley, data, *args, **kwargs)
   1741         """
   1742         kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)
-> 1743         lines = [*self._get_lines(*args, data=data, **kwargs)]
   1744         for line in lines:
   1745             self.add_line(line)

C:\Anaconda3\lib\site-packages\matplotlib\axes\_base.py in __call__(self, data, *args, **kwargs)
    271                 this += args[0],
    272                 args = args[1:]
--> 273             yield from self._plot_args(this, kwargs)
    274 
    275     def get_next_color(self):

C:\Anaconda3\lib\site-packages\matplotlib\axes\_base.py in _plot_args(self, tup, kwargs)
    397 
    398         if x.shape[0] != y.shape[0]:
--> 399             raise ValueError(f"x and y must have same first dimension, but "
    400                              f"have shapes {x.shape} and {y.shape}")
    401         if x.ndim > 2 or y.ndim > 2:

ValueError: x and y must have same first dimension, but have shapes (1,) and (50,)

For what concerns volatility_opt:

def negativeSR(w):
    w = np.array(w)
    R = np.sum(meanlogReturns*w)
    V = np.sqrt(np.dot(w.T, np.dot(Sigma,w)))
    SR = R/V
    return -1*SR

def b(w):
    return np.sum(w)-1

w0 = [0.25, 0.25, 0.25, 0.25] 
bounds = ((0,1), (0,1), (0,1), (0,1))
constraints = ({'type': 'eq', 'fun': b})
w_opt = minimize(negativeSR, w0, method='SLSQP', bounds=bounds, constraints=constraints)
w_opt

returns = np.linspace(0, 1.50, num= 50, endpoint=True, retstep=False, dtype=None, axis=0)
volatility_opt = []

def GR(w):
    w = np.array(w)
    R = np.sum(meanlogReturns*w)
    return R

def minimizeMyvolatility(w):
    w =np.array(w)
    V = np.sqrt(np.dot(w.T, np.dot(Sigma, w)))
    return V

for R in returns:
    constraints = ({'type': 'eq', 'fun': b},
                   {'type': 'eq', 'fun': lambda w: GR(w) - R})
    opt = minimize(minimizeMyvolatility, w0, method = 'SLSQP', bounds = bounds, constraints = constraints)

volatility_opt.append(opt['fun'])

with b as :

def b(w):
    return np.sum(w)-1

and Sigma and w as :

weight = np.zeros((a, 4))
expectedReturn = np.zeros(a)
expectedVolatility = np.zeros(a)
sharpeRatio = np.zeros(a)

meanlogReturns = logReturns.mean()
Sigma = logReturns.cov()
for k in range(a):
    w = np.array(np.random.random(4))
    w = w / np.sum(w)
    weight[k,:] = w

Returns of 4 tickers from yahoo lib with their closing price from 4/01/22 to 4/01/22 (or 01/4/22 to 10/4/22). LogReturns is just the log function of returns multiplied by 250 days.
In fact, till the Sharpe Ratio and the frontier's plot everything was running smoothly.