Finterm.ai: Giving Claude Code actual financial data

I used to spend my entire workflow manually copy-pasting SEC filing snippets into GPT windows, trying to bridge the gap between an LLM's reasoning and the actual numbers I needed for a trade. It was a nightmare of context switching—one window for the filing, one for the news, and another for the options data. I was essentially acting as the human API, which is the most inefficient way to run a research desk.

My cofounder and I realized that if we wanted to move toward autonomous trading agents, the bottleneck wasn't the model's intelligence; it was the data access. Most agents fail because they rely on messy web searches that return SEO spam or "AI slop" instead of granular, factual data. We built Finterm as a CLI specifically designed for coding agents like Claude Code.

The logic here is about token efficiency and signal-to-noise ratio. Instead of having an agent crawl hundreds of redundant webpages, the CLI handles the heavy lifting. For example, if you're researching a ticker, it fetches a massive volume of links, strips out the noise, and returns a clean research packet. It even includes an SEC filing diff tool. Since most quarterly filings are 90% boilerplate, the tool highlights only the actual changes, so the agent doesn't waste its context window reading the same legal disclaimers over and over.

We designed it to behave like a set of "skills." When you need a ticker's P/E ratio, revenue, or options sentiment, the agent makes a single batched call rather than dozens of individual API requests. It’s much more stable for a team trying to integrate AI into a structured financial workflow.

If you're already using Claude Code for technical tasks and want to bring it into the financial research space without the manual data-entry headache, you can test it via npm:

npm install -g @finterm-ai/cli

The data is delayed by about 15 minutes, which keeps the overhead low and fits a research-heavy approach rather than high-frequency scalping.