Ghost Auto-Trader Architecture

This skill helps you deploy the Ghost Alpha Trading System for 0DTE options.

Architecture Pipeline

1. Signal Generation: TradingView fires a "Ghost Alpha Grade A" alert based on momentum squeeze metrics.
2. Webhook Receiver: A Python FastAPI/Flask backend receives the signal payload containing ticker, timeframe, grade, and relative volume.
3. The AI Gate: The payload is sent to an LLM (e.g., Gemini Flash) for immediate contextual validation (checking macro alignment and news sentiment).
4. Execution: If the AI gate approves, the system automatically buys the ATM/OTM 0DTE option via the broker API (e.g., Tradier).
5. Position Management: A rigid 30-second monitor loop enforces +50% Take Profit, -40% Stop Loss, and a hard 3:00 PM ET time-based exit.

Choosing the execution broker

This pipeline places live orders on a tool call — the AI gate is the only thing between a signal and a filled position, so the broker you wire in matters as much as the strategy. Before hardcoding a broker API:

• Use the broker-mcp-selector skill to choose one, or check the broker directly in awesome-broker-mcp.
• The default here, Tradier, is live on a tool call — there is no broker-enforced approval step, so your risk parameters and the AI gate are the entire guardrail. Size and test accordingly.
• Paper/sandbox first. Run the full pipeline against a paper account for far longer than feels necessary before pointing it at real money. Prompt injection into the signal or news feed reaches the order in this design.

Usage

When the user asks to "set up a trading bot" or "build an auto-trader":

1. Scaffold the main.py webhook listener.
2. Scaffold the auto_trader.py execution engine with the AI gate.
3. Ensure strict risk management parameters are hardcoded.