DeFi

📋 本文定位：Part 2 — 轻量级落地指南进阶版 在 Part 1 我们梳理了 AI 时代的套利认知架构与技术拐点。本文作为延续，重点落地如何将认知转化为实操闭环。 🎯 第一层进阶：缝隙监控的自动化 Part 1 提到「信息获取优势」，但手动监控仍有成本。Part 2 解决如何自动化： 核心架构：信号采集自动化集群 graph LR subgraph Source[信源抓取层] RSS[RSS/Atom订阅] API[Twitter API / Reddit] Web[网页抓取] end subgraph Process[AI 结构化层] Parser[LLM 结构化] Translator[多语言翻译] Filter[重复过滤] end subgraph Act[行动输出层] Push[Telegram推送] Alert[邮件/钉钉告警] n8n[自动化触发] end Source --> Parser --> Translator --> Filter --> Push Parser --> n8n 实操：区县政务政策监控自动化 # 架构示例：n8n + DeepSeek workflow: trigger: cron (every 30 min) 1. scrape: local.gov.cn/search "政策公告" 2. filter: keyword = [政策, 通知, 公告] 3. llm: deepseek-v4-pro structure: - 主题 - 部门 - 影响范围 - 行动 deadlinE 4. store: Notion [政府-政策库] 5. deliver: telegram daily summary 🤖 第二层进阶：DeFi 自动化收益优化 Agent 从「手动优化」跨越到「Agent 自动再平衡」。 ...

Part 3: From Theory to Practice - The Toolkit In Part 1 we defined the cognitive architecture, and in Part 2 we outlined the lightweight and DeFi layers. This final part reveals the source code structure, template library, and operational guide for the Personal AI Arbitrage Toolkit. Repository Structure ai-arbitrage-toolkit/ ├── README.md # Project entry & quick start ├── n8n_templates/ # Visual workflow templates │ ├── gov_monitor.json # County gov policy monitoring │ ├── sentiment_scanner.json # Reddit/Twitter sentiment collection │ └── arbitrage_scanner.json # Funding rate / cross-exchange arbitrage ├── agent_prompts/ # Standardized Agent Prompt library │ ├── analyst_prompt.md # Analyst Agent: signal extraction │ ├── executor_prompt.md # Executor Agent: on-chain operations │ ├── risk_manager_prompt.md # Risk Manager: circuit breaker │ └── coordinator_prompt.md # Coordinator Agent: multi-agent collaboration ├── monitoring_dashboard/ # Personal ops dashboard │ ├── daily_summary.py # Daily auto summary script │ └── weekly_report.py # Weekly report generator ├── risk_parameters.json # Configurable risk parameters └── README_DEPLOY.md # Deployment guide