"Stock Trading" Shrimp Has Arrived? 3 Major Financial Data Terminal Giants Officially Announce "Shrimp Farming"

After more than a dozen major internet companies like Tencent and Alibaba joined the “shrimp farming” battle, leading financial data terminal providers such as Wind, Tonghuashun, and Eastmoney also officially announced their involvement.

On March 11, Wind officially launched “WindClaw,” claiming to be an “investment little lobster” that “studies, evolves, and connects data.”

Immediately following, on the early morning of March 12, Tonghuashun announced the launch of “iFinD Financial MCP,” emphasizing “providing professional financial data for OpenClaw.” At the same time, 21st Century Business Herald learned that Tonghuashun is also preparing to release its self-developed “iFinD Claw,” similar in positioning to WindClaw.

That evening, Eastmoney released “Eastmoney Skills,” highlighting the installation of “investment decision support skills” for OpenClaw.

Notably, after the explosive popularity of the DeekSeek large model in 2025, these three companies also launched their own self-developed large model products.

From the “DeekSeek moment” to the “Lobster moment,” the AI competition among major financial data terminal providers continues to heat up. Experts interviewed pointed out that future software competition may increasingly be about who can be better “driven” by AI. The race among major financial data terminal providers to “farm lobsters” is not only a response to industry trends toward intelligence but also a necessary move to consolidate competitive barriers and extend service value.

However, while these providers are immersed in the “lobster farming” craze, some of their main institutional clients—brokerages—have already started cooling down the “lobster farming” trend internally, imposing clear restrictions on the installation and use of OpenClaw.

Wind, Tonghuashun, and Eastmoney consecutively announced their “lobster farming” initiatives

Within two days, these three major financial data terminal companies each launched their own “weapons” to respond to the “lobster farming” battle. However, their product strategies differ: some focus on “data,” some on “skills” to actively connect to OpenClaw, and others directly develop “professional versions” based on the native OpenClaw.

A fintech expert explained to reporters that as a full-process AI intelligent agent, OpenClaw’s capabilities largely depend on two supports: one is massive high-quality data, which is the “nutrient” for OpenClaw’s learning and evolution; the other is diverse skills (Skills), which are the “tentacles” for practical application and problem-solving. Therefore, many companies choose to connect to OpenClaw based on their own advantages, prioritizing one of these two paths.

Specifically, Wind has taken the route of directly creating a “professional version of OpenClaw,” launching “WindClaw,” which is still in public testing.

According to Wind’s official introduction, the core features include access to Wind’s professional financial data, one-click local deployment, and continuous autonomous evolution based on user investment habits. The public beta application page shows functions such as “Monitor the Market,” “Track Stocks,” “News Monitoring,” “Stock Analysts,” “Macro Researchers,” and “Strategy Excavator.”

Image: WindWindClaw public test page

Meanwhile, Tonghuashun has prioritized data access, serving as a “professional financial data source,” by launching “iFinD Financial MCP.”

Regarding MCP, Tonghuashun explained, “Without MCP data tools, large models would only search the internet and cannot meet the needs of research personnel for financial data.” iFinD MCP emphasizes seamless integration with research-grade databases, natural language interaction, built-in professional data cleaning, and token optimization mechanisms.

According to Tonghuashun, the core modules currently available for iFinD MCP include A-share stock analysis, mutual fund analysis, macroeconomic and industry data, announcements, and news.

However, Tonghuashun has not abandoned developing its own “professional version of OpenClaw.” Reporters learned that they are planning to launch “iFinD Claw” soon, similar in positioning to WindClaw, aiming for “out-of-the-box” usability.

Image: Tonghuashun iFinD MCP connecting to OpenClaw page

Eastmoney, on the other hand, is using “Skills” as the entry point, releasing “Eastmoney Skills,” which proposes installing “investment decision support skills” for OpenClaw.

“Skills is a standardized folder that helps large language model assistants acquire professional financial data service capabilities,” Eastmoney explained. After installing a skill into OpenClaw or similar AI assistants, the AI gains the ability to call corresponding financial interfaces.

Eastmoney states that after installing Skills, OpenClaw can achieve real-time market information retrieval, automated cleaning and structuring, and can systematically screen and analyze thousands of targets based on fundamental and technical indicators, helping investors efficiently identify suitable assets.

The installation interface shows that Eastmoney Skills currently includes three skill packs: news search, financial data, and intelligent stock selection.

Image: Eastmoney Skills installation page

From “Large Models” to “Lobsters,” the AI competition is escalating again

In fact, the recent rush to connect or develop “Lobsters” is just a microcosm of the AI competition among leading players in the financial data terminal industry.

In 2025, after the “DeepSeek moment” ignited the “large model craze,” several top financial data terminal companies released their own large models, including Wind’s “Wind Alice,” Eastmoney’s “Miaoxiang,” and Tonghuashun’s “AskFinance HithinkGPT.”

A report by 21st Century Business Herald noted that the competition among financial data terminals is shifting from “selling water” to “selling shovels.” Previously, terminals relied on data resources for competitive advantage; now, data itself is less of a barrier, and what’s needed are truly effective research and trading tools.

Now, with the arrival of the “Lobster moment,” what does this mean for major financial data terminal providers?

On one hand, the transformative potential of OpenClaw as a phenomenon-level AI agent may herald a new software competition paradigm.

Song Weiwei, a fund manager at China Europe Fund, told 21st Century Business Herald that OpenClaw surpasses simple dialogue—it can help users execute tasks. Personal computers, smartphones, and cloud servers will become the carriers for AI agents, serving as their “digital employees” or “personal assistants.”

Software with good APIs can be precisely and efficiently invoked by OpenClaw, becoming an “organ” within the agent ecosystem. As AI agents like OpenClaw become more widespread, operating systems may shift from “human-centered” to “agent-centered.”

“Future software competition may be about who can be better ‘driven’ by AI,” Song Weiwei said.

On the other hand, from the product iteration logic of financial data terminals, actively integrating OpenClaw is also an important way to extend service value.

“These product launches essentially fill the ‘last mile’ from data to application, using AI agents to connect data supply with actual use,” said Zhang Ning, director of the China Fintech Research Center at Central University of Finance and Economics.

Zhang Ning analyzed that customer demands are shifting from “obtaining data” to “highly useful data,” pushing service providers to move from data supply to empowering with intelligent tools. Data vendors, leveraging their underlying data barriers and scene understanding, can quickly deeply integrate intelligent agents with their own databases to build differentiated competitive advantages.

“Under this two-way push, the deployment of such products by financial data terminal companies is not only a response to industry intelligence trends but also a necessary move to consolidate barriers and extend service value,” Zhang Ning concluded.

Internal tightening of “lobster farming” by brokerages

Despite the intensive announcements by major financial data terminal providers about “lobster farming,” some of their key institutional clients—brokerages—have already begun to cool the trend internally.

Reporters learned from multiple brokerages that many have issued internal compliance notices explicitly restricting the installation and use of OpenClaw, mainly concerning deployment on company devices and internal networks.

Most of these notices serve as reminders of related risks and require safety assessments before use; some have issued outright bans, requiring immediate suspension of installation and use; others have implemented approval processes, requiring employees to apply for permission if business needs justify.

It is noteworthy that on March 10, the National Internet Emergency Center issued a risk alert, warning that for critical industries like finance and energy, certain security vulnerabilities in OpenClaw could lead to leaks of core business data, trade secrets, and code repositories, potentially causing system paralysis and incalculable losses.

Zhang Ning warned that “lobster”-type AI applications, which tightly integrate data retrieval, analysis, operation, and command execution, not only fill the gap from data to user but also introduce more covert security risks.

Song Weiwei also pointed out that once AI gains Full Disk Access, any security vulnerability could lead to systemic data leaks. Additionally, the third-party plugin ecosystem (ClawHub) may also pose security risks.

For financial practitioners, Zhang Ning suggests being cautious about risks such as data leaks, compliance breaches, intellectual property, and reputation. He further explained that the integrated data operation process breaks traditional segmented supervision and audit trails, with internal network operations highly concealed, making abnormal transactions and external transfers difficult to detect. Cross-system operations also provide covert channels for injection attacks and plugin risks, making risk tracing more challenging and increasing the likelihood of systemic data security incidents. This is a core reason why many brokerages strictly prohibit such tools on their internal networks.

Source: 21st Century Business Herald

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