Sentinel FAQs

Question 1

What technologies are used to build Sentinel?

Accepted Answer

Sentinel leverages a Python 3.12/FastAPI engine for all core trading logic, including risk checks, order lifecycle, and strategy governance. The MCP server is built with TypeScript/Node 20 for routing AI agent commands, using PostgreSQL for persistent data storage and Redis for real-time operations like kill switches and caching.

Question 2

Is Sentinel suitable for live trading or only paper trading?

Accepted Answer

While Sentinel defaults to an integrated paper broker for development and testing, it is built as a production-grade system capable of live trading, with support for providers like Alpaca Markets. It includes comprehensive risk management and governance tools for both paper and live environments.

Question 3

What core functionalities does Sentinel offer for traders and developers?

Accepted Answer

Sentinel provides a comprehensive algorithmic trading engine, an integrated paper broker for simulation, a robust risk firewall with kill switch capabilities, advanced market regime classification, and an extensive suite of over 40 MCP tools for granular control over trading, portfolio, and audit functions.

Question 4

What is Sentinel and what is its primary purpose?

Accepted Answer

Sentinel is a production-grade algorithmic trading control plane designed to provide advanced trading capabilities, including a paper broker, robust risk firewall, and market regime classification. Its primary purpose is to empower AI agents to control and manage trading strategies efficiently via an MCP gateway.

Question 5

How does Sentinel enable AI agent control over trading operations?

Accepted Answer

Sentinel features an MCP (Multi-Modal Control Plane) gateway, which allows AI agents like Claude to interact with its trading engine using natural language. This enables agents to manage watchlists, classify market regimes, validate risk, and submit paper orders seamlessly.

Sentinel

Sentinel

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主要功能

使用案例