1. A method performed by a computer-implemented node, the method comprising: receiving a complete set of training data, wherein the complete set of training data comprises a plurality of feature vectors, and wherein the plurality of feature vectors comprises vectors representing completed financial product transactions including transaction prices; receiving instructions to train a predictive model having a plurality of parameters on an initial subset of the complete set of training data, wherein the predictive model is a model predicting a next transaction price or a next transaction price direction for one or more financial products; training the predictive model on the initial subset using a model training process to generate a respective first parameter value for each of the model parameters; storing data representing a first state of the predictive model after training the predictive model on the initial subset, wherein the first state includes the first parameter values and components of the model training process used to generate the first parameter values; receiving updated parameter values and instructions to train the predictive model on a new subset of the complete set of training data, wherein the new subset contains feature vectors from the initial subset and new feature vectors from the complete set of training data that have not been used to train the predictive model; and training the predictive model on the new subset beginning from the first state of the predictive model with the updated parameter values in place of the first parameter values using the model training process to generate a respective second parameter value for each of the model parameters.