AI Agent Architecture

Introduction

HYYY's approach to market intelligence and position management leverages state-of-the-art AI agents to achieve superior risk-adjusted returns. Unlike traditional algorithmic trading systems that focus on microsecond-level advantages, our architecture emphasizes understanding broader market psychology, on-chain patterns, and cross-chain correlations through a sophisticated multi-agent framework inspired by recent advances in LLM-based trading systems.

System Architecture

graph TB A1[(Market Data)] A2[(Chain Data)] A3[(News & Events)] A4[(Social Signals)] B[Analysis Agent] C1[[Strategy Agent]] C2[[Risk Agent]] C3[[Execution Agent]] C4[[Market Context Agent]] A1 & A2 & A3 & A4 ---> B B --> C1 & C2 & C3 & C4 C4 --> C1 C4 --> C2

Core Components

1. Multi-Modal Data Processing

Our agents process diverse data streams through specialized neural architectures:

  • On-chain metrics (TVL, transaction volumes, wallet behaviors)
  • Market sentiment indicators with NLP-based analysis
  • Cross-chain correlation patterns and arbitrage opportunities
  • Social signals and market psychology indicators
  • News and event analysis with temporal attention mechanisms

2. Hierarchical Agent Structure

The system employs a hierarchical structure of specialized agents with distinct roles:

  • Strategy Agents: Focus on high-level market direction and opportunity identification across different vaults, utilizing transformer-based architectures for pattern recognition
  • Risk Management Agents: Monitor and adjust position risk parameters using probabilistic modeling and stress testing
  • Execution Agents: Optimize entry/exit timing and position sizing with reinforcement learning
  • Market Context Agents: NEW! Provide real-time market context synthesis and maintain a dynamic market state representation

3. Adaptive Learning

The agent system continuously improves through:

  • Real-time performance feedback loops with multi-objective optimization
  • Dynamic strategy adaptation using meta-learning approaches
  • Risk parameter optimization with Bayesian updating
  • Cross-agent knowledge sharing and consensus mechanisms

Why Not Existing Frameworks?

While open-source frameworks like eliza offer interesting capabilities, we chose to build our own minimal architecture for three key reasons:

  1. AI Moves Too Fast: The pace of AI advancement is unprecedented. No single framework can keep up with integrating all the latest models and the toolchain. We need the freedom to adopt new technologies immediately, not wait for framework updates.

  2. Modularity Over All-in-One: Most of our core logic needs to be custom-built anyway. Gluing together specialized components is relatively straightforward - we don't need a complex framework with features we won't use (like character subsystems).

  3. Less Code, More Security: A minimal, purpose-built system means less code, which directly translates to fewer bugs and vulnerabilities. In DeFi, this simplicity is a feature, not a limitation.

Example: Market Psychology Analysis

Let's walk through how our agents process a market event with a hypothetical scenario:

Hypothetical Event: "Ordinals inscription volume spikes 300% after $TAO airdrop announcement to BRC-20 holders"

sequenceDiagram participant MC as Market Context participant S as Strategy participant E as Execution Note over MC: Synthesizes context:<br/>- Historical airdrop patterns<br/>- Market regime detection<br/>- Liquidity conditions MC->>S: Context: High volatility regime Note over S: Analyzes:<br/>- Inscription volume trend<br/>- Cross-chain sentiment<br/>- Similar airdrops impact S->>E: Execute strategy:<br/>Spot $TAO + Short $TAO perp<br/>- 70% correlation hedge<br/>- Max $500K position<br/>- 4hr window Note over E: Implements via:<br/>- Split orders<br/>- Cross-exchange routing<br/>- Adaptive execution speed E-->>S: Execution feedback Note over S: Adjusts parameters<br/>based on execution

In this example:

  1. Market Context Agent provides crucial regime information and historical patterns
  2. Strategy Agent performs both opportunity identification and risk assessment in an integrated loop
  3. Execution Agent implements the strategy with dynamic feedback to Strategy Agent for parameter adjustment

This demonstrates how our agents focus on strategy-level opportunities rather than single-asset directional bets, enabling more robust and market-neutral returns.

Looking Forward

Our minimal yet sophisticated architecture enables rapid integration of new AI models and market-specific optimizations, while maintaining the security standards critical for DeFi. By incorporating latest advances in LLM-based trading systems and multi-agent architectures, we're well-positioned to adapt to evolving market dynamics while maintaining robust performance.