> LinAlgError Traceback (most recent call last)
<ipython-input-36-b8e9d23c399e> in <module>()
----> 1 a.opt_min_cvar()
<ipython-input-29-cc78a27bceb6> in opt_min_cvar(self, s, beta, random_state)
159 x0=self.initial_weights,
160 niter=1000,
--> 161 paired=False,
162 )
163 return result
C:\ProgramData\Anaconda3\lib\site-packages\noisyopt\main.py in minimizeSPSA(func, x0, args, bounds, niter, paired, a, c, disp, callback)
323 xplus = project(x + ck*delta)
324 xminus = project(x - ck*delta)
--> 325 grad = (funcf(xplus, **fkwargs) - funcf(xminus, **fkwargs)) / (xplus-xminus)
326 x = project(x - ak*grad)
327 # print 100 status updates if disp=True
C:\ProgramData\Anaconda3\lib\site-packages\noisyopt\main.py in funcf(x, **kwargs)
306 # freeze function arguments
307 def funcf(x, **kwargs):
--> 308 return func(x, *args, **kwargs)
309
310 N = len(x0)
<ipython-input-29-cc78a27bceb6> in _obj_cvar(self, wgts, ret_mat, s, beta, random_state)
129 # Sample from the historical distribution
130 print(pf_rets)
--> 131 dist = scipy.stats.gaussian_kde(pf_rets)
132 sample = dist.resample(s)
133 # Calculate the value at risk
C:\ProgramData\Anaconda3\lib\site-packages\scipy\stats\kde.py in __init__(self, dataset, bw_method)
170
171 self.d, self.n = self.dataset.shape
--> 172 self.set_bandwidth(bw_method=bw_method)
173
174 def evaluate(self, points):
C:\ProgramData\Anaconda3\lib\site-packages\scipy\stats\kde.py in set_bandwidth(self, bw_method)
497 raise ValueError(msg)
498
--> 499 self._compute_covariance()
500
501 def _compute_covariance(self):
C:\ProgramData\Anaconda3\lib\site-packages\scipy\stats\kde.py in _compute_covariance(self)
508 self._data_covariance = atleast_2d(np.cov(self.dataset, rowvar=1,
509 bias=False))
--> 510 self._data_inv_cov = linalg.inv(self._data_covariance)
511
512 self.covariance = self._data_covariance * self.factor**2
C:\ProgramData\Anaconda3\lib\site-packages\scipy\linalg\basic.py in inv(a, overwrite_a, check_finite)
973 inv_a, info = getri(lu, piv, lwork=lwork, overwrite_lu=1)
974 if info > 0:
--> 975 raise LinAlgError("singular matrix")
976 if info < 0:
977 raise ValueError('illegal value in %d-th argument of internal '
LinAlgError: singular matrix
The input data is the monthly simple returns of 3 stocks (APPLE, MICROSOFT AND GOOGLE) from Jan 2015 to Dec 2018 :
|AAPL.O|MSFT.O|GOOGL.O
2005-01-31|0.194099|-0.016467|0.014679
2005-02-28|-0.416645|-0.042618|-0.039004
2005-03-31|-0.071110|-0.039348|-0.039789
2005-04-29|-0.134629|0.046752|0.218769
2005-05-31|0.102579|0.019763|0.260318
2005-06-30|-0.074172|-0.037209|0.060879
2005-07-29|0.158653|0.030998|-0.021724
...
2018-06-29|-0.009418|-0.002327|0.026536
2018-07-31|0.027983|0.075753|0.086814
2018-08-31|0.196227|0.058918|0.003732
2018-09-28|-0.008303|0.018161|-0.020068
2018-10-31|-0.030478|-0.066101|-0.096514
2018-11-30|-0.184045|0.038199|0.017486
2018-12-31|-0.116698|-0.084047|-0.058298
I would appreciate help in solving this problem.
Thanks!
Thanks a lot for coding and sharing this awesome library!
When I use min_cvar() in value_at_risk.py, a LinAlgError raised:
The input data is the monthly simple returns of 3 stocks (APPLE, MICROSOFT AND GOOGLE) from Jan 2015 to Dec 2018 :
It seems one of iterations by noisyopt.minimizeSPSA is all zero matrix. Then scipy.stats.kde gives LinAlgError: singular matrix.
I would appreciate help in solving this problem.
Thanks!