Rapid growth of genetic databases means huge savings from improvements in their data compression, what requires better inexpensive statistical models. This article proposes automatized optimizations of Markov-like models, especially context binning and model clustering. The former allows to merge similar contexts to reduce model size, e.g. allowing inexpensive approximations of high order models. Model clustering uses k-means clustering in space of statistical models, allowing to optimize a few models (as cluster centroids) to be chosen e.g. separately for each read. There are also briefly discussed some adaptivity techniques to include data non-stationarity. This article is work in progress, to be expanded in the future.