Bcs MCP
BCS-MCP is an autonomous MCP service for integrating the BCS trading API with LLM. It collects market and personal data, stores them in PostgreSQL/pgvector, and provides tools through MCP, supporting semantic search, local script calculation, and trading signal generation.
2.5 points
5.9K

What is BCS-MCP?

BCS-MCP is an intelligent middleware that acts as a bridge, connecting your BCS trading account with AI assistants (such as Claude, ChatGPT, etc.). It automatically collects real-time prices of financial products like stocks and bonds, as well as your position information, and stores them in a local database. You can directly ask the AI assistant questions about market conditions, portfolio performance, etc. in natural language, and the AI assistant will obtain accurate data through BCS-MCP and analyze it for you.

How to use BCS-MCP?

It's very simple to use: 1) Start the service with a single click using Docker; 2) Configure the MCP connection in your AI assistant tool (such as Claude Desktop); 3) Then you can inquire about market information just like having a chat. For example, you can directly ask: "Which stocks do I currently hold?", "How much has AAPL risen today?", "Help me calculate the overall return of my portfolio".

Applicable Scenarios

Suitable for investors, traders, financial analysts, and other people who need to quickly obtain market information and make investment decisions. Particularly suitable for: - People who want to query complex market data in natural language - People who need AI-assisted analysis of their investment portfolios - People who hope to automatically monitor the market conditions of specific stocks - Users who want intelligent analysis of historical trading records

Main Features

Real-time Market Data
Automatically obtain real-time prices, bid-ask quotes, transaction details, and K-line chart data of financial products such as stocks and ETFs. The data update frequency can reach the second level.
Portfolio Management
Synchronize your position information, trading records, and account balance, and calculate the profit and loss and rate of return in real-time, so that you can always keep track of your investment performance.
AI Natural Language Query
There's no need to learn complex query commands. You can ask questions in everyday language, such as "How much money did I earn from my investments today?", "Which stocks have the largest fluctuations recently?".
Intelligent Semantic Search
It can not only search by name but also understand concepts. For example, searching for "tech giant stocks" will find relevant companies such as Apple and Microsoft.
Technical Indicator Calculation
It has built-in common technical analysis tools, such as Moving Average (MA), Relative Strength Index (RSI), etc. The AI can help you calculate and analyze these indicators.
Trading Signal Generation
Generate buy and sell signals based on preset rules or AI analysis to assist you in making more informed trading decisions.
Data Security Isolation
Market data and personal account data are stored separately to ensure privacy and security. Only authorized information will be shared with the AI.
Local Processing Priority
Perform data calculation and analysis in the local database as much as possible to reduce the transmission of a large amount of raw data to the AI and save usage costs.
Advantages
๐Ÿค– Natural language interaction: Query complex financial data like having a conversation with an expert
โšก Real-time data synchronization: Reflect market changes in a timely manner for more timely decision-making
๐Ÿ”’ Privacy protection: Sensitive trading data is stored locally with strong controllability
๐Ÿ’พ Complete historical records: All queries and analyses are recorded for easy review
๐Ÿ”„ Automated operation: Automatically update data 24/7 without manual operation
๐Ÿงฉ Modular design: Different functions are clearly separated for easy maintenance and upgrading
๐Ÿณ One-click deployment: Docker containerization makes installation and configuration extremely simple
Limitations
๐Ÿ“ถ Dependence on network connection: Requires a stable network to access the BCS trading API
๐Ÿ”‘ Requirement for API permissions: Must have an API access token for the BCS trading platform
๐Ÿ’ป Technical threshold: Initial configuration requires certain technical knowledge
๐Ÿ“Š Data scope limitation: Can only obtain market data supported by the BCS platform
๐Ÿค– AI understanding limitation: The analysis of complex financial problems depends on the capabilities of the AI model
โš™๏ธ Self-hosting requirement: Requires self-maintenance of the server and database

How to Use

Preparation
Ensure that you have a BCS trading account and have enabled API access permissions, and obtain a Refresh Token. Prepare a computer or server with Docker installed.
Configure the Environment
Download the project files, copy the environment configuration file template, and fill in your BCS API token and other configuration items.
Start the Service
Use Docker Compose to start all services with a single click, including the database, data synchronization program, and MCP server.
Connect to the AI Assistant
Configure the MCP connection in your AI assistant tool. Taking Claude Desktop as an example, edit the configuration file and add the BCS-MCP server address.
Start Using
Restart the AI assistant. Now you can directly query market data and investment information in natural language!

Usage Examples

Case 1: Portfolio Health Check
Mr. Zhang asks the AI assistant about his investment performance every morning while having coffee, instead of logging in to the complex trading platform.
Case 2: Market Opportunity Discovery
Ms. Li wants to find undervalued stocks but doesn't have time to study all the financial reports, so she asks the AI assistant to help her with the initial screening.
Case 3: Trading Decision Support
Mr. Wang is considering adjusting his positions and needs to understand the market sentiment and the technical aspects of the relevant stocks.
Case 4: Educational Learning Scenario
Liu, a finance major student, uses BCS-MCP as a learning tool to verify classroom theories with real data.

Frequently Asked Questions

Is BCS-MCP safe? Will it leak my trading password?
Do I need to keep my computer running all the time to use this service?
Is this service free?
Which AI assistants are supported?
How timely is the data update?
If I want to stop using it, how can I completely delete it?
Can I customize technical indicators or add new analysis functions?
How to troubleshoot connection problems?

Related Resources

BCS Trading Platform API Documentation
Official API documentation to understand the data format and interface limitations
Model Context Protocol Official Website
Official description of the MCP protocol to understand the technical principles
GitHub Project Repository
Source code, issue feedback, and contribution guidelines
Docker Official Documentation
Docker installation and usage tutorials
Claude Desktop Configuration Guide
How to configure Claude Desktop to use the MCP server
Best Practices for Financial Data API
Usage and security suggestions for financial data interfaces

Installation

Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.

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