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Modular and scalable AI-based financial portfolio management


Fig 1. Overview of the surjection relationship between Evolving Agent Modules (EAMs) and Strategic Agent Modules (SAMs). Every EAM is chargeable for a single asset and employs a DQN agent, and it makes use of heterogeneous information to provide signal-comprised data. Every SAM is a module for a portfolio that employs a PPO agent to reallocate the property with stacked signal-comprised 3-D tensor profound state V+ from EAMs related. Furthermore, skilled EAMs are reusable for various portfolios and subsequently might be mixed and related to any SAMs at will. By parallel computing, capital reallocation could also be carried out for varied portfolios at scale concurrently. Credit:  DOI: 10.1371/journal.pone.0263689

The power to foretell actions within the inventory market might be a particularly profitable talent. For portfolio managers, who reallocate capital into the a number of property of a portfolio, predicting worth traits permits them to maximise capital returns.

Many approaches to cost prediction have been taken over time, and the formulation and patterns that make up technical evaluation are actually being changed by deep learning-based strategies, particularly these primarily based on a kind of studying known as deep reinforcement learning. Nonetheless, present reinforcement learning-based portfolio administration methods are inclined to have a set structure and lack a modular design, in order that they can’t be expanded with extra reinforcement studying brokers or be utilized to a number of portfolios. Furthermore, they’ll solely deal with a restricted variety of property or sorts of market data.

In a latest paper printed on PLOS ONE, researchers from the University of Tsukuba describe a deep reinforcement learning-based framework for portfolio administration that overcomes these issues. “By constructing this framework with a modular design,” says Zhenhan Huang, lead creator of the paper, “systems targeting different portfolios can share and be built with pre-trained modules, just like assembling LEGO bricks, in different configurations.”

The proposed system consists of evolving agent modules, one for every asset, and strategic agent modules, one for every portfolio. An evolving agent module makes use of a deep Q-network to foretell worth traits primarily based on historic prices and net information sentiment. A strategic agent module makes use of a proximal coverage optimization agent to reallocate property in response to the data generated by the evolving agent modules.

“Separating the tasks of predicting trends and making strategic decisions has several advantages,” Huang says. The evolving agent module solely must be skilled as soon as for an asset like Alphabet Inc. earlier than it may be used (and reused) for any portfolio that features that asset. Furthermore, the scalability of the system permits new property with heterogeneous information or completely different reinforcement-learning brokers to be added into present portfolios with out retraining the entire system. The modules within the system may also be run in parallel, rising effectivity and scalability.

The researchers in contrast the proposed system with a number of standard portfolio administration methods and one cutting-edge RL-based methodology. They discovered that the system carried out one of the best with respect to efficiency metrics such because the gathered charge of return and every day charge of return, even underneath the intense situations of the US inventory market throughout the international pandemic within the 12 months 2020.

The modularity of the proposed system opens up thrilling alternatives for its additional growth. The workforce used the deep Q-network and proximal coverage optimization within the present implementation, however plan to implement different algorithms. Additionally they plan to make use of different, unconventional sources of knowledge reminiscent of satellite tv for pc photos to foretell asset price trends.


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Extra data:
Zhenhan Huang et al, MSPM: A modularized and scalable multi-agent reinforcement learning-based system for monetary portfolio administration, PLOS ONE (2022). DOI: 10.1371/journal.pone.0263689

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Modular and scalable AI-based monetary portfolio administration (2022, March 2)
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