Brokerage Strategy Conference Teaches "Shrimp Farming" OpenClaw "Swims" Into Investment Research Circle

Recently, the topic of “long lines outside Tencent Building to watch people dress up as ‘Lobsters’” has been widely circulated online. However, this queueing craze might now spill over into the brokerage strategy conference scene.

The 21st Century Business Herald reports that OpenClaw, an open-source AI agent known for its red lobster logo and nicknamed “Little Lobster” by users, is moving from the geek community into the financial research and investment field.

Recently, many brokerage firms have been releasing “Lobster Raising Guides,” and some have even set up dedicated forums at strategy conferences to teach “lobster farming.” Fintech companies are also racing to develop, aiming to create a “Research Version OpenClaw” to seize the opportunity.

However, moving from “spectating” to “real work” involves several hurdles related to costs, security, and habits. How deep is OpenClaw truly penetrating into the research circle?

Brokerages collectively introduce “Little Lobster”

Recently, major brokerages have been holding their spring strategy meetings one after another. Besides traditional macroeconomic assessments and industry analyses, this year’s strategy sessions have also introduced the hottest “Little Lobster” into the live scene.

The so-called “Little Lobster” is an open-source AI agent called OpenClaw, claimed to be capable of “executing tasks” and “working” like a human. As “Little Lobster” gains popularity online, its application prospects in research and investment have attracted considerable attention from institutions.

For example, Guojin Securities will hold a dedicated “OpenClaw Empowering Research and Index Investment Forum” during its spring strategy meeting on March 12-13. The agenda shows that this sub-forum covers topics from “The New Paradigm of Research Empowered by Large Models” to practical applications like “OpenClaw in Active and Quantitative Research,” “Building Personal Research Assistants,” and more, offering comprehensive content. This is not Guojin Securities’ first attempt. According to sources, since late February, Guojin Securities has been touring Shanghai and Beijing with “OpenClaw Empowering Intelligent Research” forums.

In fact, this “raising lobsters” science popularization wave has recently swept sell-side research firms. According to incomplete statistics, by March 10, at least nine brokerages—including CITIC Securities, Huatai Securities, Orient Securities, Huachuang Securities, Eastmoney Securities, Dongwu Securities, OpenSource Securities, Founder Securities, and Huafu Securities—had scheduled “OpenClaw special courses” as part of roadshows to introduce how to deploy OpenClaw and its research applications to institutional and individual investors.

I also noticed that OpenSource Securities held a session titled “OpenClaw: From Beginner to Master” on March 10 evening, which had nearly 1,000 views on the streaming platform at the time of writing.

From the content shared by various institutions, there are mainly two categories: one is basic introductory content, including concepts of OpenClaw, quick deployment, information access, and other “awareness” topics; the other focuses on scenario applications, such as information retrieval, stock analysis, stock selection strategies, fully automated factor mining and backtesting, etc.

Additionally, several brokerages have released “Lobster Raising Guides” in the form of special research reports, evaluating OpenClaw’s research functions and providing practical breakdowns.

For example, OpenSource Securities prepared a 100-page PDF titled “OpenSource Financial Engineering OpenClaw Technical Documentation” for its online seminar, claiming that “no foundation is needed, and you can deploy your personal AI gateway in five minutes.” Some brokerage research reports, with detailed step-by-step explanations, have gone viral on social media, such as Founder Securities’ “Empowering Financial Research with OpenClaw: 17 Efficient Application Cases” and Northeast Securities’ “Install these 20 Skill Packs on your OpenClaw to Boost Research Efficiency by 10 Times,” among others.

“Research Lobster” Positioning War

Brokerage analysts are busy studying OpenClaw and teaching investors how to “raise lobsters.” Meanwhile, fintech companies with keen senses are focusing on the technical pain points of OpenClaw’s implementation, exploring deeper product development to upgrade it from a “geek toy” to a “professional research tool.”

Although OpenClaw claims to be usable with “zero coding experience,” as an AI agent, in real research scenarios, issues like data authority and deployment complexity remain significant hurdles. I noticed that different companies are exploring their own optimal ways to connect with OpenClaw to overcome these challenges.

One approach is to focus on data, positioning themselves as “professional databases,” encouraging investors to connect their research data sources into OpenClaw. For example, Gangtise Information, which specializes in aggregating analyst opinions, announcement summaries, and other research info, defines its research platform as an “AI research database + knowledge base,” and offers API interfaces for OpenClaw integration.

According to Gangtise’s research tech team, “Knowledge bases and databases are the two pillars of new research infrastructure.” Their way of “embracing” this “lobster” is to provide dedicated research data sources for OpenClaw.

Another approach is to package products and deploy them on the cloud, aiming for “plug-and-play” solutions with lower technical barriers, allowing users to create their own “digital researcher” through natural language. For example, their “Research Lobster” encapsulates and optimizes OpenClaw’s core capabilities, integrating high-quality research data such as roadshows, research reports, industry maps, and EDB, while also including the full set of Skills from the OpenClaw open-source community and preloaded professional research skills packages to reduce learning and configuration costs.

The team behind “Research Lobster” told reporters that it must have “research data genes,” specially optimized operational capabilities, and a large research ecosystem.

In terms of data, it requires engineering efforts like unified data organization, standardization, and precise correlation to form a clear, reliable research knowledge system, while also ensuring data security in the financial industry. On the ecosystem side, “Research Lobster” can connect with existing platform capabilities such as AI meeting management, AI transcription, AI translation, research brain, event signals, etc., to meet the full workflow needs, according to insiders.

Interestingly, I also found on social media that besides professional fintech firms, some individual bloggers are selling their self-developed AI research systems based on OpenClaw, with core functions like news queries, data analysis, target tracking, priced at a few hundred yuan.

However, some users have told me that these DIY products are more suitable for “playing around,” and for actual research work, they feel “not quite reliable.” Issues like unstable data sources, lack of transparency, untimely updates, and frequent errors significantly hinder work efficiency.

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

In summary, moving from “spectating” to “real work” involves several hurdles related to costs, security, and habits. Whether OpenClaw’s entry into the research circle is a “catfish stirring the pond” or just ripples on the water surface remains to be seen, and will depend on future market validation.