Claude Code is turning into a Bloomberg terminal: one command opens access to 17,000 stocks and cryptocurrencies.
An analyst under the pseudonym CyrilXBT demonstrated how a single command can connect the AI assistant Claude Code to live market data — quotes for more than 17,000 stocks and cryptocurrencies, earnings reports, balance sheets, and cash flow statements. In his assessment, such functionality could replace the capabilities of a professional Bloomberg terminal.
This involves connecting an external financial data service via the MCP protocol. The setup takes just three steps and, according to the author, about 60 seconds.
How it works
The first step is running a command inside Claude Code to add the financial-datasets service via HTTP transport. It specifies the MCP server address and registers it in the tool. The second step is authorization: the user types /mcp and completes the OAuth procedure in the browser. The third step is natural language queries. As examples, CyrilXBT cites questions about Apple's current price-to-earnings ratio and market capitalization, Tesla's quarterly reports over the last four periods, and the change in Bitcoin's price over the past year.
After setup, Claude Code accesses data on 17,000+ stocks, earnings reports, balance sheets, income statements, and cryptocurrency quotes directly. Previously, this required a Bloomberg terminal, a complex financial data API, or hours of manual searching across various sources.
CyrilXBT separately highlights the cost of the alternative. In his estimate, analysts pay about $24,000 per year for a Bloomberg terminal, and the emergence of such a combination is unlikely to please these professionals.
Who benefits from this
A sustainable advantage will go to those who learn to combine Claude Code's reasoning with access to live financial data. This refers to quants, analysts, and portfolio managers. Such an advantage compounds every day — it is a research edge that grows with continued use of the combination in daily work.
The analyst emphasizes that none of the steps is difficult on its own. The value arises precisely from connecting an ordinary tool to a stream of up-to-date market data in a single interface. The idea itself relies on an open standard for connecting external sources. Claude Code serves here as a shell through which the user accesses a third-party data provider without manual information gathering.
A thousand agents overnight from a smartphone
CyrilXBT also published a breakdown of the workflow of Boris Cherny, one of the creators of Claude Code. According to the author's description, Cherny uses a smartphone as the primary interface instead of a laptop and maintains between five and ten active sessions simultaneously. Each of these can spawn auxiliary agents — sometimes several hundred at once, sometimes several thousand overnight on larger tasks. Dozens of loops run continuously in the background, with some of them preserved server-side even when the laptop is closed.
CyrilXBT highlights four elements without which such a scheme is dangerous. First — launching and directing tasks from a smartphone: a full-fledged interface for sending instructions is needed, not just receiving status notifications. Second — isolated parallel execution. Each agent or cluster of auxiliary agents operates in its own context and workspace; otherwise, running many processes simultaneously produces chaos rather than scale. The third element, most often overlooked, is verification without human involvement. The result of each agent is checked against something real: another agent, a test suite, or an explicit rule, rather than the agent's own report of successful completion. Fourth — strict stop conditions and a spending cap. A process running for hours unattended needs an absolute limit on time, money, and volume that does not depend on whether a person notices a failure. This is what turns overnight agent work from a risk into a manageable task.
My view: Connecting Claude Code to live financial data via MCP is not just a convenient integration, but a signal of a paradigm shift in analytics. When an AI assistant gains access to the same data as professional terminals, the barrier to entry for retail investors and small funds drops sharply. The question now is not whether AI can replace Bloomberg, but how quickly traditional financial tools will adapt to a new reality where the speed and depth of analysis are determined not by the subscription budget, but by the ability to formulate queries correctly.