financialnoob / pairs_trading Goto Github PK

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using statsmodels 0.13.2

/home/geoff/anaconda3/envs/ML/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
/home/geoff/anaconda3/envs/ML/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
/home/geoff/anaconda3/envs/ML/lib/python3.9/site-packages/statsmodels/tsa/regime_switching/markov_regression.py:187: RuntimeWarning: divide by zero encountered in true_divide
-0.5 * resid2 / variance - 0.5 * np.log(2 * np.pi * variance))
/home/geoff/anaconda3/envs/ML/lib/python3.9/site-packages/statsmodels/tsa/regime_switching/markov_regression.py:187: RuntimeWarning: invalid value encountered in true_divide
-0.5 * resid2 / variance - 0.5 * np.log(2 * np.pi * variance))
/home/geoff/anaconda3/envs/ML/lib/python3.9/site-packages/statsmodels/tsa/regime_switching/markov_regression.py:187: RuntimeWarning: divide by zero encountered in log
-0.5 * resid2 / variance - 0.5 * np.log(2 * np.pi * variance))
/home/geoff/anaconda3/envs/ML/lib/python3.9/site-packages/statsmodels/tsa/regime_switching/markov_regression.py:187: RuntimeWarning: invalid value encountered in subtract
-0.5 * resid2 / variance - 0.5 * np.log(2 * np.pi * variance))

LinAlgError Traceback (most recent call last)
Input In [21], in <cell line: 6>()
19 while converged==False:
20 mod = sm.tsa.MarkovRegression(spread, k_regimes=2, switching_variance=True)
---> 21 res = mod.fit(search_reps=1000)
22 if res.smoothed_marginal_probabilities[0][-1] > 0: # check if converged
23 converged=True

File ~/anaconda3/envs/ML/lib/python3.9/site-packages/statsmodels/tsa/regime_switching/markov_switching.py:1113, in MarkovSwitching.fit(self, start_params, transformed, cov_type, cov_kwds, method, maxiter, full_output, disp, callback, return_params, em_iter, search_reps, search_iter, search_scale, **kwargs)
1111 # Get better start params through EM algorithm
1112 if em_iter and not self.tvtp:
-> 1113 start_params = self._fit_em(start_params, transformed=transformed,
1114 maxiter=em_iter, tolerance=0,
1115 return_params=True)
1116 transformed = True
1118 if transformed:

File ~/anaconda3/envs/ML/lib/python3.9/site-packages/statsmodels/tsa/regime_switching/markov_switching.py:1205, in MarkovSwitching._fit_em(self, start_params, transformed, cov_type, cov_kwds, maxiter, tolerance, full_output, return_params, **kwargs)
1203 delta = 0
1204 while i < maxiter and (i < 2 or (delta > tolerance)):
-> 1205 out = self._em_iteration(params[-1])
1206 llf.append(out[0].llf)
1207 params.append(out[1])

File ~/anaconda3/envs/ML/lib/python3.9/site-packages/statsmodels/tsa/regime_switching/markov_regression.py:214, in MarkovRegression._em_iteration(self, params0)
212 coeffs = None
213 if self._k_exog > 0:
--> 214 coeffs = self._em_exog(result, self.endog, self.exog,
215 self.parameters.switching['exog'], tmp)
216 for i in range(self.k_regimes):
217 params1[self.parameters[i, 'exog']] = coeffs[i]

File ~/anaconda3/envs/ML/lib/python3.9/site-packages/statsmodels/tsa/regime_switching/markov_regression.py:250, in MarkovRegression._em_exog(self, result, endog, exog, switching, tmp)
247 tmp_endog = tmp[i] * endog
248 tmp_exog = tmp[i][:, np.newaxis] * switching_exog
249 coeffs[i, switching] = (
--> 250 np.dot(np.linalg.pinv(tmp_exog), tmp_endog))
252 return coeffs

File <array_function internals>:180, in pinv(*args, **kwargs)

File ~/anaconda3/envs/ML/lib/python3.9/site-packages/numpy/linalg/linalg.py:1990, in pinv(a, rcond, hermitian)
1988 return wrap(res)
1989 a = a.conjugate()
-> 1990 u, s, vt = svd(a, full_matrices=False, hermitian=hermitian)
1992 # discard small singular values
1993 cutoff = rcond[..., newaxis] * amax(s, axis=-1, keepdims=True)

File <array_function internals>:180, in svd(*args, **kwargs)

File ~/anaconda3/envs/ML/lib/python3.9/site-packages/numpy/linalg/linalg.py:1648, in svd(a, full_matrices, compute_uv, hermitian)
1645 gufunc = _umath_linalg.svd_n_s
1647 signature = 'D->DdD' if isComplexType(t) else 'd->ddd'
-> 1648 u, s, vh = gufunc(a, signature=signature, extobj=extobj)
1649 u = u.astype(result_t, copy=False)
1650 s = s.astype(_realType(result_t), copy=False)

File ~/anaconda3/envs/ML/lib/python3.9/site-packages/numpy/linalg/linalg.py:97, in _raise_linalgerror_svd_nonconvergence(err, flag)
96 def _raise_linalgerror_svd_nonconvergence(err, flag):
---> 97 raise LinAlgError("SVD did not converge")

LinAlgError: SVD did not converge

I have found some syntax format for the markdown equations that can be improved to enhance readability. I also found that you can add ; at the end of plot generating cells to suppress the output to exclude string of text when generating plots, this can help with formatting for the notebook in general. Can I create a pull request to help you with these format issues?