MiroFish Explained: AI Agent Swarms, GraphRAG, and Deterministic Trading Bots

MiroFish: https://github.com/666ghj/MiroFish MiroFish is an AI simulation engine designed to model large populations of digital agents reacting to macroeconomic events. This video explains how massive AI agent swarms simulate social consensus and why those architectures fail in real trading environments. Instead of scaling thousands of AI personas, successful quantitative systems rely on deterministic multi-agent trading frameworks that divide tasks across analyst, researcher, risk management, and execution layers. You’ll also see how GraphRAG architecture structures financial data, why language models drift toward consensus, and how adversarial agent debate creates stronger trading signals. The breakdown also explores mixture-of-experts models, token cost mechanics, and reflect-agent feedback loops used to continuously improve AI trading bots. Timestamps: 0:00 MiroFish AI simulation engine and large scale agent swarms 0:47 The paradox between massive AI simulations and real trading execution 1:16 Why scaling more agents becomes a computational trap 1:40 GraphRAG architecture and knowledge graph memory systems 2:19 Injecting macroeconomic shocks into simulated AI societies 3:13 Latency, API failures, and infrastructure limits of swarm simulations 3:37 Alignment bias and herd behavior in large language model agents 4:35 Why prediction markets collapse under AI consensus behavior 4:59 Deterministic multi agent trading frameworks explained 5:32 Bull vs bear adversarial agents generating structured trading signals 6:38 Token costs and mixture of experts models reducing compute expense 7:40 Offline synthetic market training and flash crash simulations 8:39 Reflect agent feedback loops improving AI trading performance Massive AI simulations like MiroFish model digital societies, but real trading edges come from deterministic multi agent trading systems. GraphRAG knowledge graphs, adversarial bull and bear agents, and reflect agent feedback loops create measurable strategy improvements. The future of AI algorithmic trading lies in structured agent roles, disciplined evaluation, and cost-efficient mixture-of-experts architectures. #MiroFish #AITradingAgents #GraphRAG