Airdrop Optimizer MultiAgent System for Airdrop

📖 3. Long Description 🎯 The Problem Over US$1.2 billion in crypto airdrops go unclaimed each year. Hundreds of campaigns launch monthly, with complex requirements. Manual monitoring and execution are inefficient and unsustainable. 💡 Our Solution A multi-agent system automates campaigns in four stages: 1. Airdrop Scout Agent Simulates scraping sources (e.g., Binance). Extracts data: token, volume, reward, duration, URL. Assigns viability score (0–10). 2. Campaign Creator Agent Filters viable campaigns (score ≥ 5.0). Assigns risk (LOW / MEDIUM / HIGH). Launches Trading Agents per campaign. 3. Prediction Agent Performs technical analysis (MA, RSI), sentiment simulation, and forecasting. Produces recommendations (BUY / SELL / HOLD). 4. Trading Agent Simulates spot trades to meet volume targets. Uses stop-loss/take-profit for risk management. Tracks trades, P&L, success rate, and reports updates. 🔧 Architecture & Infrastructure Orchestrated via Celery + Redis for scalable execution. CLI entry: main.py. RESTful API with FastAPI (api/main.py) enables: Listing campaigns Starting/stopping agents Monitoring agent status Swagger UI for testing 🌐 Deployment on Vultr Hosted on Vultr SSD VPS—high-performance cloud with: Fast CPUs + SSDs for low-latency task and API performance Scalability to provision new servers for agent load and queue growth Global reach for latency-optimized deployments Cost efficiency via hourly/monthly billing Flexible deployment (Docker/K8s or direct VM) REST API exposed via load balancer for resilience 📈 Scalability & Modularity Celery allows parallel agent execution across campaigns. Each Trading Agent runs independently with unique token IDs. Simulated results include trade count, success rate, P&L, and duration.