H|ψ⟩ Quantum Finance
Quant finance MCP for AI agents, now in the Claude Directory
7 followers
Quant finance MCP for AI agents, now in the Claude Directory
7 followers
HPSILab brings quantitative finance research, market insights, and forecasting tools to AI agents through a platform, Python SDK, and MCP integration. Explore tools and build smarter financial research workflows.
This is the 5th launch from H|ψ⟩ Quantum Finance. View more
HPSILab
Launched this week
New: HPSILab is now listed in the Claude Directory, so you can connect it to Claude in one click. AI agents get price predictions, Monte Carlo simulation, options IV radar and pre-trade risk checks via MCP, a Python SDK and the web platform.
Launch Team
AWS Startup AdvisorPersonalized AWS guidance that meets you where you’re already building
Promoted
Hi everyone 👋
Meet HPSILab — quantitative finance built for AI agents.
🌐 Platform: https://hpsilab.com/register?invite=QZ9A8KPG
🤖 Claude Directory: https://claude.ai/directory/hpsilab
🐍 Python SDK / MCP: https://pypi.org/project/hpsilab-mcp/
Less time searching. More time understanding.
A concrete first workflow for anyone trying HPSILab — I'm Haiyun, the founder.
1. Create a free HPSILab account. In Claude, open the HPSILab Directory connector and authorize it. Other compatible MCP clients can use https://hpsilab.com/mcp.
2. Try this prompt: Analyze NVDA using HPSILab. Separate the model signal from implied volatility, options pressure and Monte Carlo context. Report available data dates and missing inputs. Explain where the results disagree; do not turn the output into a trade instruction.
3. Check the source dates before using the result. A directional score and a simulation range describe different things, and neither guarantees an outcome. Options coverage is for US stocks and ETFs; other supported markets have different coverage.
Client setup: https://hpsilab.com/connect?utm_source=producthunt&utm_medium=community&utm_campaign=agent_research_launch_20261102&utm_content=quickstart_comment
A free account includes trial Credits; API and MCP calls are metered. This is research information, with no trade execution.
If you try the workflow, which part needs improvement: setup, data freshness, output format, or tool cost? A specific example would help me improve it.
Written with AI assistance.