数据源：riquant下载到本地，data = herramiento.base_analysis()分析没有问题。
多时间周期分析出错。

intervals = ["5min","15min","30min","1h","2h","4h"]
herramiento.multi_time_frame_analysis(intervals=intervals)

2020-03-07 16:27:12.986966  第二步：随机性检验：纯随机性
2020-03-07 16:27:12.986966  白噪声检验结果:(array([nan]), array([nan]))

---------------------------------------------------------------------------
MissingDataError                          Traceback (most recent call last)
<ipython-input-5-7cd45c2bb58a> in <module>
      1 # 多时间周期分析
      2 intervals = ["5min","15min","30min","1h","2h","4h"]
----> 3 herramiento.multi_time_frame_analysis(intervals=intervals)

~\qvirs_tools\data_analysis.py in multi_time_frame_analysis(self, intervals, df)
    257             data["volume"] = df["volume"].resample(interval, how="sum")
    258 
--> 259             result = self.base_analysis(data)
    260             self.results[interval] = result
    261 

~\qvirs_tools\data_analysis.py in base_analysis(self, df)
    128 
    129         random_test(close_price)
--> 130         stability_test(close_price)
    131         autocorrelation_test(close_price)
    132 

~\qvirs_tools\data_analysis.py in stability_test(close_price)
    304 def stability_test(close_price):
    305     """"""
--> 306     statitstic = ADF(close_price)
    307     t_s = statitstic[1]
    308     t_c = statitstic[4]["10%"]

c:\vnstudio\lib\site-packages\statsmodels\tsa\stattools.py in adfuller(x, maxlag, regression, autolag, store, regresults)
    266         if not regresults:
    267             icbest, bestlag = _autolag(OLS, xdshort, fullRHS, startlag,
--> 268                                        maxlag, autolag)
    269         else:
    270             icbest, bestlag, alres = _autolag(OLS, xdshort, fullRHS, startlag,

c:\vnstudio\lib\site-packages\statsmodels\tsa\stattools.py in _autolag(mod, endog, exog, startlag, maxlag, method, modargs, fitargs, regresults)
     93     method = method.lower()
     94     for lag in range(startlag, startlag + maxlag + 1):
---> 95         mod_instance = mod(endog, exog[:, :lag], *modargs)
     96         results[lag] = mod_instance.fit()
     97 

c:\vnstudio\lib\site-packages\statsmodels\regression\linear_model.py in __init__(self, endog, exog, missing, hasconst, **kwargs)
    857                  **kwargs):
    858         super(OLS, self).__init__(endog, exog, missing=missing,
--> 859                                   hasconst=hasconst, **kwargs)
    860         if "weights" in self._init_keys:
    861             self._init_keys.remove("weights")

c:\vnstudio\lib\site-packages\statsmodels\regression\linear_model.py in __init__(self, endog, exog, weights, missing, hasconst, **kwargs)
    700             weights = weights.squeeze()
    701         super(WLS, self).__init__(endog, exog, missing=missing,
--> 702                                   weights=weights, hasconst=hasconst, **kwargs)
    703         nobs = self.exog.shape[0]
    704         weights = self.weights

c:\vnstudio\lib\site-packages\statsmodels\regression\linear_model.py in __init__(self, endog, exog, **kwargs)
    188     """
    189     def __init__(self, endog, exog, **kwargs):
--> 190         super(RegressionModel, self).__init__(endog, exog, **kwargs)
    191         self._data_attr.extend(['pinv_wexog', 'wendog', 'wexog', 'weights'])
    192 

c:\vnstudio\lib\site-packages\statsmodels\base\model.py in __init__(self, endog, exog, **kwargs)
    234 
    235     def __init__(self, endog, exog=None, **kwargs):
--> 236         super(LikelihoodModel, self).__init__(endog, exog, **kwargs)
    237         self.initialize()
    238 

c:\vnstudio\lib\site-packages\statsmodels\base\model.py in __init__(self, endog, exog, **kwargs)
     75         hasconst = kwargs.pop('hasconst', None)
     76         self.data = self._handle_data(endog, exog, missing, hasconst,
---> 77                                       **kwargs)
     78         self.k_constant = self.data.k_constant
     79         self.exog = self.data.exog

c:\vnstudio\lib\site-packages\statsmodels\base\model.py in _handle_data(self, endog, exog, missing, hasconst, **kwargs)
     98 
     99     def _handle_data(self, endog, exog, missing, hasconst, **kwargs):
--> 100         data = handle_data(endog, exog, missing, hasconst, **kwargs)
    101         # kwargs arrays could have changed, easier to just attach here
    102         for key in kwargs:

c:\vnstudio\lib\site-packages\statsmodels\base\data.py in handle_data(endog, exog, missing, hasconst, **kwargs)
    670     klass = handle_data_class_factory(endog, exog)
    671     return klass(endog, exog=exog, missing=missing, hasconst=hasconst,
--> 672                  **kwargs)

c:\vnstudio\lib\site-packages\statsmodels\base\data.py in __init__(self, endog, exog, missing, hasconst, **kwargs)
     85         self.const_idx = None
     86         self.k_constant = 0
---> 87         self._handle_constant(hasconst)
     88         self._check_integrity()
     89         self._cache = {}

c:\vnstudio\lib\site-packages\statsmodels\base\data.py in _handle_constant(self, hasconst)
    131             exog_max = np.max(self.exog, axis=0)
    132             if not np.isfinite(exog_max).all():
--> 133                 raise MissingDataError('exog contains inf or nans')
    134             exog_min = np.min(self.exog, axis=0)
    135             const_idx = np.where(exog_max == exog_min)[0].squeeze()

MissingDataError: exog contains inf or nans

源码都看了，是数据本身的问题?

数据中包括nan或者无限大的数据，检查下数据吧