Finterm.ai Bloomberg terminal for Claude Code
Hi, my name is Kam, and today my cofounder Josh and I are shipping Finterm, a CLI that gives coding agents direct access to financial data: stock prices, options data, SEC filings, and Ticker Deep Research, a filtered ticker news search. I’m a developer and have been a full-time trader for the past few years.Recently I have been using LLMs more and more in my trading and strategy. I always found it frustrating that Claude Code or GPT 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…
What it does
In the maker’s words, at launch
Hi, my name is Kam, and today my cofounder Josh and I are shipping Finterm, a CLI that gives coding agents direct access to financial data: stock prices, options data, SEC filings, and Ticker Deep Research, a filtered ticker news search. I’m a developer and have been a full-time trader for the past few years.Recently I have been using LLMs more and more in my trading and strategy. I always found it frustrating that Claude Code or GPT 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 was betting the toy was a fad and that the stock would fall. I read through the SEC filings and 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. 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. 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 known 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 and npm install -g @finterm-ai/cli to test it. We have a 3-day free trial (card required) and would love any feedback.
Does the same job
all alternatives →
Claude Agents for Financial ServicesMay 2026 · ▲242Finance agent templates for pitches, KYC, and closing books
- VTVibeTrade – Trading Harness for ClaudeMar 2026 · github.com · ▲7
Post-Opus 4.6, LLMs feel much better at using bash, code, local files, and tools. So I kept coming back to a simple question: if a model can use a computer reasonably well, why can’t I just give it my broker account, a strategy, and let it trade? My conclusion is that the blocker is not model capability in the abstract. It is the system around the model. A raw LLM breaks on a few practical things almost immediately: • no persistent operating memory across sessions • no trustworthy record of what it did and why • no hard approval boundary before money moves • no cheap always-on monitoring if…
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
Launched alongside, July 2026
the whole month →- IR
I might be the only SRE on Earth with his own bowling center. It's a more in-depth gig than you'd think. My family and I bought an abandoned 8-lane bowling center in the rural mid-west. In our small town there weren't many recreation options for families. You've heard of a food desert? This is an R&R desert. It had been abandoned for a good reason. The roof leaks, the electrical system was constantly surging, and my 70-year-old bowling equipment (still) doesn't work perfectly. The system that keeps your score is particularly interesting to me. It's the thing you watch during your game, but…
Life & fun · Jul 2026
- EElevators▲1,680
Life & fun · Jul 2026 · john.fun
- 1W18 Words▲1,160
Life & fun · Jul 2026 · 18words.com
- BA
Over the past few months, our team has been building more and more slidedecks using web frontend technologies with coding harnesses like Claude Code, but a common complaint is to make even small edits we need to edit the code either manually or via the harness. To avoid this loop, I ended up creating Bento, a single HTML file with everything you need in a slide tool including animations and shared editing. There's no install or cloud login, everything works offline. The default deck is around 560 KB and it doesn't need to fetch anything once you got it. Open it in a browser and then you can…
Dev tools · Jul 2026 · bento.page
- GG
A few days ago I found myself trying out GLM 5.2 and was really positively impressed. The capabilities and security I was getting from this LLM are similar to those I've gotten from models like Claude or GPT, and this really surprised me. But then I thought, "I wonder how it would work on a normal computer like mine," and above all, "I wonder if it would work without going into OOM on a computer like mine." So I started working with the help of agents to test this possibility. I started converting the model to int4, understanding MTP usage, and if possible implementing DSA for long context.…
AI · Jul 2026 · github.com
