Defius Maximus
A personalized leverage trading agent that increases your winrate with AI.
About
The problem it solves Trading is a high-risk skill game that demands extensive research, experience, and emotional control. Many aspiring traders struggle with: Time-Consuming Learning Curve – Technical and fundamental analysis require deep understanding and practice. Access to Alpha – Profitable trading signals are often locked behind exclusive groups. Emotional Trading – Retail traders frequently make impulsive decisions, leading to significant losses. This project democratizes access to profitable trading strategies by enabling users to own their trading IP, leverage AI-driven insights, and follow proven strategies in a transparent, decentralized way. User Interaction and Data Flow 1. Creating a Trader Profile Expert traders sign up and verify their identity via Humanity Protocol to claim a "Trader" credential. Upon signing up, a Story IP Collection is deployed, allowing traders to own the intellectual property of all their posted trades. 2. Strategy Selection & AI Assistance Experienced traders (Chefs) design and share reputable trading strategies. Users can invest in these strategies, with performance fees allocated to Chefs when trades are profitable. Before execution, a fine-tuned LLM with RAG-enhanced sentiment and trading data evaluates trades against user-defined criteria. 3. Trade Execution: Two Modes BLUE PILL (Beginner Mode) – One-click auto-trading. The AI handles everything, allowing users to earn without prior Web3 knowledge. RED PILL (Advanced Mode) – Users customize AI parameters, set personal trading goals, and selectively follow specific traders. This approach bridges the gap between experienced traders and newcomers, ensuring transparency, automation, and ownership in the trading ecosystem. The project architecture and development process Our development process follows an agile methodology with weekly sprints: Managing IPs on chain Solidity contracts for IP tokenization and profit distribution Extensive testing on multiple testnets before mainnet deployment AI Model Training: Fine-tuning of base models on proprietary trading datasets Continuous improvement via feedback loops from successful/unsuccessful trades Regular retraining with new market data Frontend Iterations: User testing with both beginner and experienced traders UX optimizations for both BLUE PILL and RED PILL modes Progressive enhancement for users with varying levels of Web3 experience Integration Testing: End-to-end testing of the complete trade lifecycle Load testing for concurrent users and high-frequency trading scenarios Cross-chain transaction verification and testing