"Qwant" matching MCP servers:

Institutional-grade quantitative stock analysis and research signals for AI agents via the Model Context Protocol (MCP).

MCP server for Quant Data, providing measured market statistics for index futures and equities, including day-type probabilities, Weis volume-price waves, options max pain, and dealer gamma exposure. Includes free keyless access and emphasizes honest reporting of model limitations.

Quant tools for AI agents on Kalshi & Polymarket. 32 tools, 25 free with no key: EV, Kelly sizing, Bayes updates, odds conversion, base-rate gaps, combo grading, Fed odds, the NFL model, and Kalshi 15-minute markets and perps (live board + liquidation estimates). Pro adds commodity and NFL edge models, the mispricing scanner and edge alerts.

An MCP server exposing a registry of paper-backed quantitative trading methods plus a deterministic, no-LLM decision helper for reproducible trading research.

Provides retrieval and analysis tools for a quantitative-finance research library, enabling whole-passage answers with verifiable citations, citation checking, entity graph exploration, and corpus inspection.

AI-native quantitative trading signal engine for crypto and TradFi perpetuals. Multi-factor composite BUY/SELL/HOLD signals, cross-venue funding rate arbitrage scanning, and market regime detection powered by Hyperliquid data.

Enables AI agents to perform market analysis through typed access to deterministic market data, technical indicators, backtesting, and position sizing via the Model Context Protocol.

Deterministic Risk Oracle for DeFAI Agents.

Analyzes the instrument currently open on your TradingView chart using 311 published quantitative models and free market data, returning a confidence-scored verdict with position sizing. Supports universe scanning and verified Pine Script export.

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 running quantitative finance coding tasks through MCP, where an agent writes, executes, and repairs Python code using discovered library functions and returns structured results along with saved artifacts.