daily_stock_analysis vs ClickHouse MCP Server

Fetches OHLCV data, real-time quotes, and chip distribution across A-shares, HK, and US markets from a 7-tier provider hierarchy (EFinance → AkShare → Tushare → Pytdx → Baostock → YFinance → Longbridge) with automatic circuit-breaker failover and data validation. Each provider is prioritized by reliability and latency; if one fails or times out, the system transparently falls back to the next tier without interrupting the analysis pipeline.

Unique: Implements a 7-tier provider priority system with automatic circuit-breaker failover rather than simple round-robin or single-provider approaches; EFinance (Priority 0) is free and near real-time, eliminating the need for paid APIs for basic analysis. The system validates data quality and latency at each tier before falling back, ensuring analysis uses the freshest available data.

vs alternatives: Outperforms single-provider solutions (e.g., yfinance-only) by guaranteeing data availability across market disruptions; more cost-effective than commercial data APIs (Bloomberg, FactSet) by leveraging free Chinese data sources (AkShare, Tushare) as primary tiers.

Routes stock data through a unified LiteLLM interface to multiple LLM backends (Gemini, Claude, DeepSeek, OpenAI, Ollama) with embedded trading philosophy rules and 11 built-in strategies (Bull Trend, Golden Cross, Wave Theory, etc.). Each strategy is implemented as a 'skill' that guides the LLM's reasoning via system prompts and structured output templates, ensuring analysis adheres to quantitative trading principles rather than generating arbitrary commentary.

Unique: Embeds 11 quantitative trading strategies as reusable 'skills' with LLM-guided reasoning rather than hardcoded technical indicators; uses LiteLLM abstraction to support 5+ LLM backends (Gemini, Claude, DeepSeek, OpenAI, Ollama) with unified interface, enabling provider-agnostic analysis and cost optimization. Trading philosophy rules are enforced via system prompts, ensuring recommendations align with quantitative discipline.

vs alternatives: More flexible than rule-based technical analysis (TA-Lib) because LLM reasoning adapts to market context; more disciplined than pure LLM chat because strategies constrain reasoning to specific trading frameworks. Supports local Ollama deployment for zero-cost inference, unlike cloud-only solutions (ChatGPT, Gemini API).

Integrates with messaging platform bots (Telegram Bot API, Discord Webhooks, WeChat Work Bot API) to enable interactive analysis queries and report delivery. Users can send commands to the bot (e.g., '/analyze AAPL' or '/portfolio') and receive analysis results directly in the chat. The bot supports slash commands, inline buttons for quick actions (buy/sell/hold), and rich message formatting (embeds, cards, rich text). Bots run as separate processes and poll for messages or listen to webhooks.

Unique: Implements native bot integrations for Telegram, Discord, and WeChat Work (Chinese platform) with slash commands, inline buttons, and platform-specific rich formatting. Enables interactive analysis queries directly in chat without leaving the messaging app. Supports group chat usage with optional rate limiting to prevent abuse.

vs alternatives: More convenient than web UI because users don't need to open a browser; analysis is delivered in their existing chat workflow. More interactive than report-only notifications because users can query analysis on-demand and execute actions via inline buttons. Supports Chinese platforms (WeChat Work) natively, unlike most Western financial APIs.

Enables deployment of the analysis system to GitHub Actions, a free CI/CD platform that runs workflows on a schedule (cron) or on-demand. The system is packaged as a Docker container or Python script that runs in the GitHub Actions environment, fetches stock data, runs analysis, and sends notifications. No server hosting is required; GitHub Actions provides free compute for public repositories (2000 min/month) and paid plans for private repositories. Workflows are defined in YAML and version-controlled alongside the code.

Unique: Leverages GitHub Actions free tier (2000 min/month for private repos, unlimited for public) to run scheduled analysis without paying for cloud hosting. Workflows are defined in YAML and version-controlled alongside code, enabling reproducible deployments. Integrates with GitHub Secrets for secure credential management.

vs alternatives: More cost-effective than cloud-based scheduling (AWS Lambda, Google Cloud Scheduler) because GitHub Actions is free for public repos and cheap for private repos. More maintainable than local cron jobs because workflows are version-controlled and visible in the GitHub UI. More scalable than single-machine deployments because GitHub Actions can run multiple workflows in parallel.

Packages the entire analysis system (backend, frontend, database, notification services) as a Docker Compose stack that can be deployed locally or to cloud platforms (AWS, Google Cloud, DigitalOcean). The Compose file defines services for the FastAPI backend, React frontend, PostgreSQL database, and optional Redis cache. Deployment is as simple as 'docker-compose up', with all dependencies and configuration managed by the Compose file. Supports environment-based configuration (dev, staging, prod) via .env files.

Unique: Provides a complete Docker Compose stack (backend, frontend, database, cache) that enables single-command deployment ('docker-compose up') without manual service setup. Supports environment-based configuration (dev/staging/prod) via .env files. Enables local development with the same stack as production, reducing environment drift.

vs alternatives: More convenient than manual service setup because all dependencies are defined in a single file. More reproducible than cloud-native deployments because the stack is version-controlled and can be deployed identically across environments. More accessible than Kubernetes because Docker Compose has a lower learning curve and is suitable for small to medium deployments.

Enables deployment of the analysis system as a systemd service (Linux) or cron job that runs on a local machine or VPS. The system runs continuously as a background service, polling for scheduled analysis times and executing them. Systemd provides service management (start, stop, restart, status) and automatic restart on failure. Cron provides simple time-based scheduling without a persistent service. Both approaches require minimal infrastructure (just a Linux machine) and zero cloud hosting costs.

Unique: Provides both systemd service and cron job deployment options for Linux, enabling simple self-hosted scheduling without cloud infrastructure. Systemd provides service management (start/stop/restart) and automatic restart on failure. Cron provides simple time-based scheduling. Both approaches require minimal setup and zero cloud hosting costs.

vs alternatives: More cost-effective than cloud-based scheduling because it runs on a cheap VPS or local machine. More reliable than manual script execution because systemd provides automatic restart and monitoring. More flexible than GitHub Actions because it supports long-running services and persistent state.

Aggregates news, risk alerts, earnings data, and capital flow from 4+ specialized search APIs (Anspire, Tavily, Bocha, SerpAPI) and enriches the LLM analysis context with up-to-date fundamental information. The search service queries for stock-specific news, regulatory filings, insider trading, and market sentiment, then embeds results into the LLM prompt as structured context to ground recommendations in real-world events rather than historical price patterns alone.

Unique: Implements a multi-API search strategy (Anspire, Tavily, Bocha, SerpAPI) with fallback logic similar to data fetching, ensuring news availability even if primary search API fails. Structures search results as context blocks for LLM prompts, enabling the AI to cite specific news events in recommendations. Supports market-specific search (A-shares, HK, US) with appropriate query formatting per market.

vs alternatives: More comprehensive than single-source news APIs (e.g., NewsAPI alone) because it aggregates multiple providers and includes earnings/risk data. More efficient than manual news monitoring because search is automated and results are pre-structured for LLM consumption. Supports Chinese market news (via Anspire, Bocha) unlike most Western financial APIs.

Implements a multi-agent system that decomposes complex investment questions into sub-tasks, each handled by specialized agents (technical analyst, fundamental analyst, risk manager, sentiment analyzer). Agents communicate via a shared context store and iteratively refine recommendations through multi-turn reasoning. The orchestrator routes user queries to appropriate agents, aggregates their outputs, and synthesizes a final recommendation with consensus scoring and dissent tracking.

Unique: Implements agent specialization with explicit role separation (technical analyst, fundamental analyst, risk manager, sentiment analyzer) rather than a single monolithic LLM; agents share context via a structured store and produce scored outputs that are aggregated with dissent tracking. This enables explainable AI where users can see which agents support/oppose a recommendation and why.

vs alternatives: More transparent than single-LLM analysis because users see reasoning from multiple specialized perspectives. More robust than simple prompt engineering because agent disagreement surfaces uncertainty. Enables cost optimization by routing simple queries to cheaper agents and complex queries to more capable (expensive) models.

ClickHouse/mcp-clickhouse | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki ClickHouse/mcp-clickhouse Index your code with Devin Edit Wiki Share Loading... Last indexed: 26 April 2025 ( d42bc1 ) Overview System Architecture Dependencies and Requirements Core Components MCP Server Configuration System ClickHouse Tools Database and Table Listing Query Execution Setup and Usage Installation Configuration Integration with Claude Desktop Development Guide Testing CI/CD Pipeline Code Style and Standards Menu Overview Relevant source files README.md mcp_clickhouse/mcp_server.py pyproject.toml This document provides a comprehensive introduction to the mcp-clickhouse repository, which implements a FastMCP server that provides read-only access to ClickHouse databases. This system enables applications like Claude Desktop to interact with ClickHouse databases in a controlled, secure manner without requiring direct database connection handling in those applications. For detailed setup instructions, see Setup and Usage , and for integration with Claude Desktop specifically, see Integration with Claude Desktop . Key Purpose and Features mcp-clickhouse serves as a bridge between client applications and ClickHouse databases, providing three primary capabilities: Database Listing : Retrieve a list of all available databases in the ClickHouse instance Table Information : Get det

System Architecture | ClickHouse/mcp-clickhouse | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki ClickHouse/mcp-clickhouse Index your code with Devin Edit Wiki Share Loading... Last indexed: 26 April 2025 ( d42bc1 ) Overview System Architecture Dependencies and Requirements Core Components MCP Server Configuration System ClickHouse Tools Database and Table Listing Query Execution Setup and Usage Installation Configuration Integration with Claude Desktop Development Guide Testing CI/CD Pipeline Code Style and Standards Menu System Architecture Relevant source files mcp_clickhouse/__init__.py mcp_clickhouse/main.py mcp_clickhouse/mcp_server.py This document describes the architectural design and components of the mcp-clickhouse system. It outlines the high-level structure, component relationships, data flow, and execution patterns of the system. For information on dependencies and requirements, see Dependencies and Requirements . Overview The mcp-clickhouse system is designed to provide a secure, read-only interface to ClickHouse databases through a FastMCP server. It offers tools for database exploration and query execution while maintaining strict security controls. Sources: mcp_clickhouse/mcp_server.py 1-229 mcp_clickhouse/__init__.py 1-13 mcp_clickhouse/main.py 1-10 Core Components The system consists of several key components that work together to provid

Core Components | ClickHouse/mcp-clickhouse | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki ClickHouse/mcp-clickhouse Index your code with Devin Edit Wiki Share Loading... Last indexed: 26 April 2025 ( d42bc1 ) Overview System Architecture Dependencies and Requirements Core Components MCP Server Configuration System ClickHouse Tools Database and Table Listing Query Execution Setup and Usage Installation Configuration Integration with Claude Desktop Development Guide Testing CI/CD Pipeline Code Style and Standards Menu Core Components Relevant source files mcp_clickhouse/mcp_env.py mcp_clickhouse/mcp_server.py This document provides detailed information about the main components that make up the mcp-clickhouse system. It covers the architectural structure, functional elements, and how they interact to provide a simplified interface for ClickHouse database operations. For information about how to set up and use these components, see Setup and Usage . Component Overview The mcp-clickhouse system consists of several core components that work together to provide secure, read-only access to ClickHouse databases. Sources: mcp_clickhouse/mcp_server.py 34-151 mcp_clickhouse/mcp_env.py 12-137 Key Components and Their Functions The mcp-clickhouse system contains the following key components: Component Description Implementation FastMCP Server The server that exposes t

ClickHouse/mcp-clickhouse | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki ClickHouse/mcp-clickhouse Index your code with Devin Edit Wiki Share Loading... Last indexed: 26 April 2025 ( d42bc1 ) Overview System Architecture Dependencies and Requirements Core Components MCP Server Configuration System ClickHouse Tools Database and Table Listing Query Execution Setup and Usage Installation Configuration Integration with Claude Desktop Development Guide Testing CI/CD Pipeline Code Style and Standards Menu Overview Relevant source files README.md mcp_clickhouse/mcp_server.py pyproject.toml This document provides a comprehensive introduction to the mcp-clickhouse repository, which implements a FastMCP server that provides read-only access to ClickHouse databases. This system enables applications like Claude Desktop to interact with ClickHouse databases in a controlled, secure manner without requiring direct database connection handling in those applications. For detailed setup instructions, see Setup and Usage , and for integration with Claude Desktop specifically, see Integration