# OLAXBT Agent ## Vision OLAXBT Agent is not another trading bot — it is the first adaptive, user-owned intelligence layer for digital assets. By combining continuous reinforcement learning with on-chain and off-chain context, each agent evolves in real time to match its owner’s unique trading style, risk tolerance, and market philosophy. ## Core Concept The Living Co-PilotUnlike static indicators or rule-based bots, an OLAXBT Agent is a persistent, stateful entity that: * Learns from every trade its owner executes (win or loss) * Ingests live on-chain flows, order-book dynamics, and sentiment signals * Remembers past conversations and user feedback * Autonomously refines its decision framework without ever taking custody of funds The result is a co-pilot that gets sharper the longer you use it, effectively turning personal trading experience into compounding intellectual capital. ## Powered by Model Context Protocol (MCP) MCP is the open framework that allows agents to be modular, composable, and marketable: * Agents are built from interchangeable “skills” (technical analysis, funding-rate arbitrage, KOL sentiment tracking, narrative detection, etc.) * Users can assemble agents without writing code using the no-code IDE * Finished agents can be listed on the MCP Marketplace and monetized as Agent-as-a-Service (AaaS) * Third-party developers earn revenue when their skills or full agents are used This transforms trading tools from closed products into an open, collaborative economy. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://olaxbt-docs.gitbook.io/olaxbt-doc/project-info/olaxbt-agent.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.