Industry: FinTech / Cryptocurrency / Artificial Intelligence
See the product here: https://chef247.ai/
Problem Statement
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.
Team Composition:
- 1 AI Engineer (Agent logic & prompting).
- 1 Blockchain Developer (Wallet integration & smart contract interaction).
- 1 Full Stack Developer (AWS/Next.js).
Challenges & Resolutions:
- Challenge: LLM hallucinations occasionally interpreting weak signals as strong.
- Resolution: Implemented a "Council of Agents" architecture where three distinct agents must agree on a trade score before execution.
7. Results & Impact
Quantitative Outcomes:
- Accuracy: Achieved 8/10+ (80%) success rate on identified high-probability setups.
- Tokenomics: Successfully automated a 20% profit-sharing mechanism, utilizing revenue to buy back and burn/hold native tokens.
Qualitative Outcomes:
- Zero Downtime: established true 24/7 market presence.
- Emotionless Execution: Eliminated "revenge trading" and hesitation, adhering strictly to stop-loss/take-profit logic.