I always found it frustrating that LLMs did not have direct access to actual financial information and had to rely on web search, so it couldn’t get me more granular numbers for specific options pricing.
When making a trade I want to understand as much as possible about the stock. Instead of relying on analysts or interpretations of the data, I like to go directly to the truth. So whenever I have a trade thesis, I break research into a few parts: company research, analyst sentiment, and market sentiment.
Last september I had a short thesis on Popmart Labubu's parent company. I thought the toy was a fad and that the stock would fall. I read through the SEC filings and had an LLM analyze them too: how big a driver is Labubu, what's the business model, what does the debt look like, and what looks strange enough to dig into. I compared the company to its peers on EPS and industry metrics. I asked GPT to do deep research that included around 50 queries and hundreds of pages to map every argument about the stock. Finally I looked at the options data: call/put ratios, implied volatility, recent volume, to see how the real money was betting. I made 16% over the next month. But the flow was painful, fetching SEC data, copy pasting filing sections in to GPT, aggregating everything by hand, and juggle a dozen chat windows. In the past few months, Josh and I spent more time trying to get agents to trade autonomously. The more we dug in, the more we realized that the problem you need to solve first is giving agents access to factual information in a token-efficient way.
Our first design decision was making Finterm a CLI. We found that agents performed better with a CLI, since it didn’t waste as many tokens as interfacing with MCP or making API calls. We designed the CLI to be self-documenting and behave similarly to skills so it would be agent-friendly.
Second, we batch multiple calls together. Whenever I research a ticker, I want the same few pieces of information every time—P/E ratio, revenue, current stock price, options sentiment. We let your agent make a single call, which saves tokens and gives a more complete view of a ticker.
When doing web searches about a ticker, you often get noisy articles (how much you would have made if you had invested $X in Amazon in 2002), SEO spam, and duplicated articles covering the same topic from the same source. So our Ticker Deep Research returns a research packet: it fetches 600–800 links per ticker, strips out the 30–40% that is noise, and gives back the state of the internet on that ticker—deduped, with sources labeled primary or secondary and AI-slop sites flagged. Instead of crawling hundreds of webpages itself, your agent gets a thorough snapshot of what the market thinks about the stock.
We take the same approach for SEC filings. Even with raw filings accessible now, most quarterly and annual filings are 90–95% boilerplate and repetition. We offer raw filings, but also an SEC filing diff tool where your agent sees only the diffs: the important changes to the company.
Stock and options data is delayed by up to 15 minutes, which keeps costs reasonable and fits the research-first use case we’re building for.
We realize this is a niche product for a technical audience that likes to trade stocks using Claude Code, but it’s close to a lot of the frustrations I feel myself, so I wanted to share it and see if anyone else is interested.
You can sign up at finterm.ai and npm install -g @finterm-ai/cli to test it. We have a 3-day free trial (card required) and would love any feedback.