"A quantitative trading system" matching MCP servers:

A custom MCP protocol service that enhances AI models by providing multilingual translation capabilities and resource management, allowing for automatic text extraction and translation through external APIs.

一个基于Model Context Protocol的SQLite数据库查询服务，为AI应用提供标准化的数据库访问、安全的SQL查询执行以及表结构预览功能。

Enables MCP clients to securely query databases, list tables, inspect table schemas, and test connections across Oracle, SQL Server, MySQL, PostgreSQL, and SQLite using read-only SQL tools.

An MCP server that enables local AI models to receive guidance from remote 'senior' AI providers like OpenAI, Anthropic, and Gemini to solve programming problems. It features intelligent multi-turn dialogue management, context synchronization, and automated session history tracking.

A smart documentation system that helps AI assistants understand and follow your project's conventions by automatically discovering, routing, and managing AI_README.md guide files.

Provides accurate system time to LLM applications with multi-timezone support via a simple tool call.

Alpaca trading API with a hard daily USD cap enforced server-side. Paper trading by default — live trading blocked until you explicitly opt in.

Provides comprehensive system diagnostics and hardware analysis through 10 specialized tools for troubleshooting and environment monitoring. Offers targeted information gathering for CPU, memory, network, storage, processes, and security analysis across Windows, macOS, and Linux platforms.

Read-only MCP tools for coding agents to audit deployment targets, detect fabricated code, review backlog, database schema, analytics, ML models, architecture docs, and decision lenses.

Automatically converts JoinQuant quantitative trading strategies to Ptrade platform format, supporting direct code input or file paths, with automatic API mapping and detailed conversion reports.

Analyzes Playwright test results to provide structured QA insights, including test metrics, failure analysis, bug report drafts, and release-quality recommendations.

Provides intelligent file system operations with advanced features including code analysis and modification across multiple languages, version control (Git/GitHub), file compression, encryption, semantic search, batch operations, and secure shell command execution.

Enables AI coding agents to discover, compare, select, and enforce design systems and component libraries, with installation guidance, component APIs, and strict adherence directives to prevent default substitutions.

A Model Context Protocol server that exposes an airline design system as a queryable knowledge base, enabling AI to discover components, find components for use cases, and scaffold prototypes.

Provides comprehensive code quality analysis with quantitative metrics, historical trends, and refactoring risk prediction for C#, Python, and TypeScript codebases.

Provides access to Appian's Aurora design system documentation via GitHub, enabling LLMs to query components, layouts, and patterns. It supports both public and internal repositories with features for keyword searching and detailed component guidance.

Enables coding agents to add a bounded semantic-judgment layer for routing, ranking, extraction, verification, and escalation, returning typed signals and review recommendations.

Server that enables AI assistants to interact with Storybook design systems. Extract component HTML, analyze styles, and help with design system adoption and refactoring.

Automated MCP server for the Insider Design System. Enables AI assistants to discover, understand, and generate code for over 60 Design System components with automated extraction from source code.

A beginner-friendly MCP server that enables AI to explore file systems through tools for listing directories, reading files, searching with wildcards, and getting file metadata. Perfect for learning MCP development while providing practical file system interaction capabilities.