Multi-Graph Tensor Networks
This work addresses the problem of processing complex data in algorithmic trading for financial applications, representing an incremental improvement by integrating existing graph and tensor methods.
The paper tackled the challenge of handling irregular and multi-modal data by introducing the Multi-Graph Tensor Network (MGTN) framework, which combines graphs for irregular data and tensor networks for compression, resulting in a deep Q agent for FOREX trading that achieved highly superior performance against three competing models at drastically lower complexity.
The irregular and multi-modal nature of numerous modern data sources poses serious challenges for traditional deep learning algorithms. To this end, recent efforts have generalized existing algorithms to irregular domains through graphs, with the aim to gain additional insights from data through the underlying graph topology. At the same time, tensor-based methods have demonstrated promising results in bypassing the bottlenecks imposed by the Curse of Dimensionality. In this paper, we introduce a novel Multi-Graph Tensor Network (MGTN) framework, which exploits both the ability of graphs to handle irregular data sources and the compression properties of tensor networks in a deep learning setting. The potential of the proposed framework is demonstrated through an MGTN based deep Q agent for Foreign Exchange (FOREX) algorithmic trading. By virtue of the MGTN, a FOREX currency graph is leveraged to impose an economically meaningful structure on this demanding task, resulting in a highly superior performance against three competing models and at a drastically lower complexity.