I need a fully-automated bot for the US options market that runs on n8n (or a similarly flexible low-code platform) and leverages AI to pinpoint the optimal strike price and exact entry/exit levels for each trade. The core of the logic should combine three strategies—Trend following, Mean reversion, and Arbitrage—while relying strictly on technical indicators to generate signals. Here’s the flow I have in mind: • Real-time market data feeds into n8n. • An AI module processes that data, applies the technical indicators, then chooses the most suitable strategy among the three above. • The bot returns a clear recommendation: buy/sell, strike, premium, and target exit. Deliverables 1. n8n workflow (export JSON) or equivalent code with clear node naming. 2. AI decision layer (could be a hosted model, Python micro-service, or n8n function) that ranks strikes and times entries. 3. Setup guide and comments so I can adjust indicator parameters or swap data sources without rewriting code. 4. Short back-test or paper-trade report proving the flow executes end-to-end. Acceptance criteria • All three strategies are selectable in the workflow and work independently. • The bot outputs strikes, pricing, and side (buy/sell) in under one minute after new data arrives. • No hard-coded secrets; environment variables or credential nodes only. If you have prior experience wiring n8n to brokerage or data APIs, that’s a plus, but I’m open to other platforms as long as the final result is easy for me to host and tweak. Let me know how you’d approach the AI selection logic and which data sources you prefer so we can get started.