Human traders face two primary limitations in crypto markets:
Physical Constraints: Manual monitoring of 24/7 markets is impossible, resulting in missed arbitrage and entry opportunities.
Psychological Bias: Emotional decision-making (panic selling or FOMO) leads to inconsistent execution and capital erosion.
Latency: Manual execution speeds are insufficient for capturing fleeting value in volatile tokens.
Objectives & Goals
Automation: Develop a "set-and-forget" system capable of autonomous market scanning and execution.
Precision: Achieve a high win rate by strictly adhering to technical parameters, ignoring market noise.
Value Accrual: Create a mechanism where trading profits directly impact the native token’s value through automated buy-backs.
Solution & Approach
The team developed Chef247, a multi-agent AI system. Unlike linear algorithmic bots, Chef247 uses a collaborative agent architecture where different logic units "discuss" a trade before execution.
Signal Detection: Ingests technical data via custom scripts.
Validation: A "Critic" agent reviews signals against risk parameters.
Execution: Automated wallet interactions on Solana and Base chains.
Technologies & Architecture
Tech Stack:
Core Logic: Large Language Models (LLMs) for decision synthesis.
Data Source: TradingView Scripts (Pine Script) for technical indicator feeds.
Frontend: Next.js for user dashboard and metrics.
Infrastructure: AWS (Lambda/EC2) for scalable, always-on hosting.
Development Methodology:
Agile: Bi-weekly sprints focusing on signal accuracy followed by execution latency.
Implementation
Execution Phases:
Strategy Formulation: codified technical analysis rules into TradingView scripts.
Agent Training: Tuned LLMs to interpret technical signals and assess risk probability.
Simulation: "Paper trading" phase to validate win rates without capital exposure.
Integration: Connected AWS backend to Solana/Base RPC nodes for wallet authority.
Live Deployment: Gradual capital release monitored by human supervisors.