Multi-Agent Trading System Using Artificial Intelligence

Multi-agent systems are concerned with decision-making tasks where multiple agents act in a shared environment. Agents can observe their environment (partially or fully), act to impact the environment, and might have different or aligned goals. Multi-Agent Systems Artificial Intelligence (MAAI) is used for simulating enduser requirements. The models designed are examples of the use of AI in the business world.

The concept of reinforcement learning can be applied to stock price prediction for a specific stock, working in an agent-based system to predict higher returns based on the current environment. The agent's reward will be either profit or loss. A multi-agent system will use three types of agents: agent 1 (forecasting agent using a basic machine learning algorithm), agent 2 (judgmental agent; the background algorithms to work on it are reinforcement learning or fuzzy neural networks), and agent 3 (based on simple trading rules or neural networks). Alert Agent (AA) guarantees proficient conveying inside the schema. Signals are one of the alerts. The alert agent sends the foundation agents (Agent 1, Agent 2, and Agent 3) signals (verdict) delivered by the superior agent. Depending on these verdicts, the superior policies are understood to be presented to the users (traders). Depending on the verdict by Superior, investment risk can be minimized. The multi-agent framework verdict is combined with sentiment collected from finance news for a particular company. The cognizant behavior of agents in the stock market is also considered future research work for this framework. AI-based stock trading systems must be strengthened in the future with the use of various security measures.