Enables quant research, strategy generation, backtesting, and paper trading from natural language prompts, integrating with AI agents via an MCP server.
Enables AI-driven quant research by exposing backtesting, portfolio optimization, and performance analytics tools through MCP, allowing iterative strategy refinement with built-in overfitting guardrails.
Enables AI assistants to directly access quant research knowledge, including factor libraries, strategy backtesting, and research reports, through the MCP protocol.
An extensible framework that exposes quantitative research functions and financial data connectors, such as FRED, via an MCP server. It enables users to perform complex financial modelling, data retrieval, and autonomous research loops with built-in guardrails and pluggable components.
Enables quantitative trading research by providing tools to backtest strategies, list market datasets, review forward-test logs, and search previously rejected hypotheses, all through an MCP interface.
Enables AI agents to run WorldQuant BRAIN alpha backtests via MCP, supporting expression submission, status tracking, result analysis, and batch resumption.