Investment Management System

Abstract

Decision Decision-making systems that use actors That Can Be Done in the context of financial services include a variety of applications ranging from investment advice to financial trading. Order flow analysis presents many unresolved challenges with machine learning (ML) strategies to determine the right flexible trading strategy. The first step in this direction is represented by an analysis of the effect of systematic flow: the model must see strong predictions that determine the positive/negative outcome. The purpose of this work is to suggest an ML closed-loop method based on the tree decision (DT) model to be implemented.effect analysis of financial trading data. The whole process is included in the decision Outcome Analysis Support Program. Considering the complexity of the model, the DT algorithm enables the production of definitions that allow the user to understand (i) how this result is achieved (decision rules) and (ii) the most discriminating outcome predictions (feature value). The loop closure method allows users to interact directly with the proposed DSS-OA by retraining the algorithm for the purpose of fine-grained analysis. Test results and comparisons have demonstrated the high interpretation and performance of the proposed DSS-OA by providing a valid and fast system for analyzing results on financial trading data. In addition, conceptual evidence studies have shown the impact of the proposed DSS-OA on the outcome analysis situation.