I'm in the process of implementing the algorithms described in the Barber paper on Bayesian co-integration because I feel that they could be useful for various types of relative-value trading (e.g. simple pairs). This paper proposes a Bayesian approach to estimating whether or not two signals are co-integrated that avoids some of the pitfalls involved in using Dickey-Fuller methodology.
Initially I want to test the algorithm as described in the paper within Mathematica on simulated stock signals to verify both correctness of the implementation and to study how well it performs under the metrics of most interest to a trader. This investigation will likely suggest other ways to extend the algorithm after which it would make sense to translate the code into Python in order to test it against historical data on the Quantopian platform.
As @Thomas points out above, it will likely take us at least another few weeks to reach that point since there are more than a few steps involved here. You mention the possibility of contacting the authors for guidance. Perhaps they have some Matlab code that they are willing to share. If so, I wouldn't be surprised if it uses some of the routines from the toolbox that accompanies Barber's book on machine learning, which incidentally I recommend downloading from his website for additional background on this subject area.