Wibull Securities Co., Ltd. has significantly expanded its financial data APIs for AI agents and large language models (LLMs). Announced on June 29, 2026, this initiative will enable investors to conduct more advanced investment analysis and trading using natural language.

Enhanced analytics capabilities by adding 23 types of endpoints

On June 29, 2026, Wibull Securities Co., Ltd. announced that it has significantly enhanced its MCP (Model Context Protocol) data infrastructure for AI agents and large language models (LLMs). The biggest highlight of this update is the addition of 23 new financial data API endpoints. This enables investors to access not only stock price checks but also a wide range of essential data domains for investment decisions, including company fundamentals, financial statement data, ETF-related information, and even market screening functions.

The specific added features are diverse. First, in corporate fundamentals information, we have enhanced functions such as capital inflow and outflow analysis, industry comparison analysis, obtaining SEC filings, and even calendar functions for financial results and dividends. This has enabled AI to grasp corporate events and trading trends in real time. In addition to key financial statements such as the income statement, balance sheet, and cash flow statement, the financial data section also covers EPS performance, consensus forecasts, and various financial indicator analyses.

This data is being gradually integrated into Webull MCP servers and is now available through AI agents and automated analysis tools. As the global demand for generative AI in the collection and analysis of investment information grows, this feature expansion can be seen as creating an environment where individual investors can easily perform advanced data analysis comparable to institutional investors with the help of AI.

Advanced investment decisions enabled by AI agents

The newly added 23 endpoints dramatically enhance the “depth of knowledge” AI agents gain when conducting investment analysis. Until now, AI utilization mainly focused on acquiring stock price data and summarizing simple news, but with the expanded Webull MCP server, AI can now quantitatively analyze a company’s profitability, growth potential, and safety. For example, it enables cross-functional analysis based on natural language, such as comparing a specific company’s financial status with SEC filings while simultaneously comparing it with competitors within the industry to present future growth forecasts.

Of particular note is the enhancement of ETF-related data, which was previously difficult to obtain. An API provides an overview of the fund, net asset value (NAV) data, asset allocation information, details of the listed stocks, as well as risk performance evaluation and distribution history. This allows investors to ask AI advanced questions such as “Tell me the proportion of specific sectors included in this ETF” or “Analyze risks based on past dividend performance,” and receive immediate answers.

The diagram below shows how AI agents access multiple data sources via APIs.

Additionally, enhanced market screening capabilities now allow for sector-specific performance analysis, high dividend rankings, and 52-week high/low rankings. This helps AI automatically identify and suggest themed investment opportunities to investors through market fund rotation and thematic investment opportunities.

Technical Background and AI-Friendly Development Environment

The Role of Model Context Protocol (MCP) and Agent Skills

Webull Brokers’ “Webull MCP Server” and “Webull Agent Skills” are groundbreaking tools first announced domestically in mid-May 2026. Among these, the “Webull MCP Server” adopts the “Model Context Protocol (MCP)” proposed by Anthropic. MCP is an open protocol that allows AI agents to securely and efficiently access external tools and data infrastructure, and officially offering it as a securities firm marks the first attempt by a domestic securities firm to support trading in both Japanese and U.S. stocks.

This technology enables developers to seamlessly connect with popular AI development tools and agents such as Cursor, Claude Desktop, and Kiro, with Webull’s trading infrastructure. On the other hand, “Webull Agent Skills” functions as a Python-based local script that can be called directly from an AI coding assistant. By combining these tools, users can use natural languages (Japanese and English) to instruct AI to retrieve real-time stock prices, check account balances and open positions, and execute orders (including corrections and cancellations) for spot stocks and margin trades, even without deep programming knowledge.

In terms of security, all operations are protected by two-factor authentication (2FA) token flows, ensuring high security even when transactions are conducted via AI. By converting the complex structure of financial APIs into a format that AI can easily understand, Wibull Securities proposes a new investment style called “AI Trading.”

AI-friendly document design focused on machine readability

A major barrier to development using generative AI was the complexity of financial API documentation. Traditional specifications were designed for humans to read, making it difficult for AI to accurately interpret and generate code. To address this challenge, Wibull Securities is redesigning its API documentation to be “AI-friendly.”

Specifically, we made it compatible with the “llms.txt” standard, which improves document readability, and made it possible to retrieve all document pages in Markdown format. This design assumes “RAG (Retrieval-Augmented Generation),” which combines information search and generation. This enables developers to directly reference Webull’s API specifications on AI tools to generate highly accurate trading code or have AI agents autonomously collect data.

The diagram below shows the flow of AI generating code by referring to llms.txt.

Providing such developer experiences is a key factor in attracting AI-native generation investors and developers. By not only publishing APIs but also providing information in a form that AI can “understand,” it is helping to promote the spread of next-generation trading environments.

Market Impact and Future Outlook

Investor Reactions and Positive Impact on Stock and Market Capitalization

Webull’s introduction of the MCP server and API expansion was received very positively in the stock market. When the MCP server officially launched in the U.S. market on June 11, 2026, the stock price of parent company Webull Corporation (NASDAQ: BULL) recorded a significant increase of 9.97% to 11.27% on the day of the announcement. In just this single day’s movement, the company’s market capitalization increased by approximately $326 million, reaching a scale of $3.6 billion. This reflects investors’ high appreciation for the company’s AI strategy and the potential for user base expansion and monetization.

Compared to past data, the response to the introduction of MCP this time is remarkable. For example, when the AI decision partner “Vega” was launched in November 2025, its stock price rose by only about 0.73%. However, the MCP server has made significant progress from simply “providing information” to “direct trade execution by AI,” and the market views it as a stronger catalyst for growth. Webull’s solo rise amid weak peers suggests that this AI-driven move is recognized as a unique strength of the company.

According to market analysis, this stock price increase aligns with Webull’s narrative of “AI Trading,” and the significance of opening advanced trading tools to general retail investors without programming knowledge is significant. In this way, the implementation of AI technology is a prime example of how directly increasing a company’s market value.

Future Developments and Expansion of the “AI Trading” Ecosystem

Starting with this data API expansion, Wibull Securities intends to further strengthen its AI integration capabilities. Currently, MCP servers are available to clients in the United States and Japan, but they are planned to be rolled out sequentially to other markets across 16 regions worldwide. By providing this advanced AI trading environment to the company’s more than 27 million registered users worldwide, it has the potential to fundamentally change the trading styles of individual investors worldwide.

Future highlights include the deepening of AI News Summary features and the autonomy of watchlist management. Use cases are envisioned where AI agents learn from users’ interests, dynamically update watchlists, or immediately provide analyses that compare with financial data when important news occurs. Furthermore, in research areas such as Python-based algorithmic trading and automated trading, the spread of “AI-powered Trading,” where AI generates bug-free code with a full understanding of specifications, is expected to accelerate.

On the other hand, Wibull Securities emphasizes the disclaimer that “final investment decisions based on AI-collected and analyzed information are solely the responsibility of the customer.” As AI technology becomes more advanced, users are required to understand the characteristics of tools and have the literacy to use them appropriately. Wibull Securities is determined to solidify its position as a next-generation trading infrastructure centered on three pillars: API, AI, and developer features.

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