code: def stability_of_timeseries(returns):
      
      if len(returns) < 2:
          return np.nan
  
      returns = np.asanyarray(returns)
      returns = returns[~np.isnan(returns)]
  
      cum_log_returns = np.log1p(returns).cumsum()
      rhat = stats.linregress(np.arange(len(cum_log_returns)),
                              cum_log_returns)[2]
  
      return rhat ** 2
docs: Determines R-squared of a linear fit to the cumulative
      log returns. Computes an ordinary least squares linear fit,
      and returns R-squared.
  
      Parameters
      ----------
      returns : pd.Series or np.ndarray
          Daily returns of the strategy, noncumulative.
          - See full explanation in :func:`~empyrical.stats.cum_returns`.
  
      Returns
      -------
      float
          R-squared.