Chapter 5 describes a market’s frame by a Markov process with a discrete state space and a continuous time space: Every individual agent, his/her endowment with shares and money, his/her individual preferences and last but not least, the set of actions feasible to any agent or trader has to be modeled explicitly. These are basic prerequisites to model interactions precisely. This innovative approach exploits the ample data volumes equally available today to the same extent as the computing power. The efficient management of the abundance of data requires methods that go beyond mean and variance of price’s time series. Orders are a trader’s vital acting tools at markets. The modelling of the trader’s decision process applies discrete choice: The utility of placing an order can be transformed in the probability of taking that order: the order under consideration changes the appropriate element in the Markov state of nature. It provides the Markov transition probability of that state under consideration. The connection of an action’s probability and a state’s transition probability may be termed “Doubly Stochastic Markov Process”, (DSMP for short) A. Roth has propagated for some time, that at markets, transactions are generated by matching appropriate opposite incentives. He was awarded the Nobel prize. The time has come to investigate the last mile of trading in finance too, i.e. to investigate order placing and matching. Trading has become a very important risk management tool. It is vital, that the trading facilities do not only exist on paper, but also in reality.