hpsilab-mcp-sdk
Build AI-powered stock analysis agents with MCP
hpsilab-mcp-sdk launched on June 23, 2026 with 2 votes.
What it does
AI-powered quantitative finance tools via MCP，SDK and examples for H|ψ⟩ Quantum Finance MCP - haiyunsky/hpsilab-mcp-sdk hpsilab-mcp-sdk is an open-source MCP server and SDK focused on quantitative finance. It provides stock screening, technical indicators, market analysis, and AI-agent integrations through the Model Context Protocol (MCP), enabling Claude, ChatGPT, and other MCP-compatible clients to access financial intelligence tools.
Quant Finance MCP Server for Stock Analysis and Options Analytics - HPSILab - haiyunsky/hpsilab-mcp-sdk
hpsilab-mcp is the official Python SDK for the hosted hpsilab.com REST API — quantitative finance and options analytics (IV surface, Monte Carlo simulation, AI predictions, pre-trade risk scans, and more). Note: This package wraps REST endpoints and can decode results supplied by an optional MCP transport adapter. It does not implement MCP transport. Keep the API key private. Replace YOUR_API_KEY below with the complete hpsi_... value: from hpsilab_mcp import HpsiMcpClient try : client = HpsiMcpClient ( api_key = "hpsi_your_api_key_here" ) print ( client . analyze_stock ( "NVDA" )) except Exception as e : print ( f"HPSILab error: { e } " ) Pass only the key value. Do not add a Bearer…from github.com
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LangAlpha – what if Claude Code was built for Wall Street?Apr 2026 · github.com · ▲148Some technical context on what we ran into building this. MCP tools don't really work for financial data at scale. One tool call for five years of daily prices dumps tens of thousands of tokens into the context window. And data vendors pack dozens of tools into a single MCP server, schemas alone can eat 50k+ tokens before the agent does anything useful. So we auto-generate typed Python modules from the MCP schemas at workspace init and upload them into the sandbox. The agent just imports them like a normal library. Only a one-line summary per server stays in the prompt. We have around 80…
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