In this paper, we describe practical results of an algorithmic trading prototype and performance optimization related experiments for end-user code generation from customized UML models. Our prototype includes high-performance computing solutions for algorithmic trading systems. The performance prediction feature can help the traders to understand how powerful the machine they need when they have a very diverse portfolio or help hem to define the max size of their portfolio for a given machine. The traders can use our Watch Monitor for supervising the PNL (Profit and Loss) of the portfolio and other information so far. A portfolio management module could be added later for aggregating all strategies information together in order to maintain the risk level of the portfolio automatically. The prototype can be modified by end-users on the UML model level and then used with automatic Java code generation and execution within the Eclipse IDE. An advanced coding environment was developed for providing a visual and declarative approach to trading algorithms development. We learned exact and quantitative conditions under which the system can adapt to varying data and hardware parameters.