Finterm.ai:为Claude Code打造的Bloomberg终端式金融数据工具
评分
69首次将Bloomberg终端概念引入AI agent数据接口,信息增量高
描述清晰且用例明确,但缺乏技术细节和实际性能验证
直接解决AI agent获取结构化金融数据的痛点,实用性强
恰逢AI agent与金融工具结合的热点,时效性极佳
信息来源为Hacker News自荐项目,尚未有第三方验证
基础分 76.5 × T2 信源权重 ×0.9 = 69
精选线 70未达精选线
精选理由
产品创新性强且切中AI agent在金融领域数据获取的痛点,但尚需验证数据覆盖与可靠性。
AI 点评
「这个产品精准切中了AI agent在金融领域落地的数据瓶颈——结构化、可机读、带时间戳的金融数据。对于构建交易或研究Agent的开发者来说,Finterm.ai 提供了一个很有吸引力的中间层,值得持续关注其数据广度和延迟表现。」
速览
Finterm.ai 是一个专为AI代理(如Claude Code)设计的金融数据接口,提供带时间戳的行情数据包、SEC文件差异、内幕及机构资金流向和多源深度研究结果,通过单一调用即可获取结构化金融信息,旨在提升交易决策的事实基础。
- 专为Claude Code等AI代理提供结构化、带时间戳的金融数据
- 集成行情数据、SEC文件差异、内幕交易和机构流向等多源信息
- 通过单一API调用即可完成深度研究,简化Agent开发流程
- 定位为AI时代的Bloomberg终端,降低金融数据获取门槛
正文
Finance tools shaped for agent context.
Finterm gives agents both the raw financial data and the token-efficient layer on top: filtered sources, calculated signals, diffs, and structured outputs.
Ticker data bundle
finterm bundle run ticker_data NVDA --delivery-mode inline_result Creates the NVIDIA ticker_data run and returns the structured ticker snapshot inline.
The full ticker snapshot in one bundle: earnings, guidance, price reaction, ratios, options sentiment, short pressure, technicals, statements, and pre-earnings context.
EarningsGuidancePricesRatiosOptionsStatements ticker_data.NVDA.yaml inline result Earnings actuals + surprise Guidance revenue + EPS Prices T-1 / T0 / T+5 Options P/C + liquidity Technicals RSI · MACD · SMA ticker_data bundle · live/current outputSEC filing diffs
finterm tool sec_filing_diff NVDA --base 2025:Q3 --compare 2026:Q1 Compares NVIDIA filings section by section and returns the changed language.
Item 7. MD&A — First Quarter FY2027 Summary − Revenue $57.0B · GM 73.4% · net income $31.9B · EPS $1.30 (Q3 FY2026) + Revenue $81.6B · GM 74.9% · net income $58.3B · EPS $2.39 (Q1 FY2027, +85% YoY) Item 1A. Risk Factors — China export controls − ≈$50M H20 revenue under USG licenses to China-based customers (Aug 2025) + H200 now carries a 25% U.S. import tariff; no Hopper shipments to China this quarter vs $4.6B a year ago + New: $18.6B into private cos/infra funds; up to $10B in Anthropic; equity stake in Intel pending approval MD&A · 131% churnRisk Factors · 60% churnQA-checkedTicker Deep Research
finterm bundle run company_deep_research NVDA --company-name "NVIDIA Corporation" --param q=Q1 --param fy=2027 --param prev_q=Q3 --param prev_fy=2026 --delivery-mode summary_json Runs the search, crawl, dedupe, and source-labeling pass for NVDA.
Thorough by design: fetches 600–800 links per ticker, then strips the 30–40% that is noise—AI slop, SEO spam, syndicated reprints—leaving a comprehensive read of what the market thinks about the stock.
742 crawled → 0 kept Strip AI slopDedupe syndicationLabel primary / secondary 40 searches · 742 sources crawled · 272 dropped as noise, dupes, and off-topic Crawled · 742 NVDA options flow & screener (fintel.io · template) Nvidia demand continues to outweigh supply, JPMorgan says (finance.yahoo.com) NVIDIA price & institutional ownership (fintel.io · boilerplate) iFlow: earnings-season outlook shifts (bny.com · off-entity) NVIDIA Q4/FY2026 financial results (nvidianews.nvidia.com) Nvidia halts H200 output as China export controls bite (finance.via.news) Q4 results press release — reprinted across 11 sites merged Nvidia Q4 call — WATCH LIVE (youtube.com · low-signal) Analysts keep hiking targets — is NVDA too cheap? (barchart.com) Q4 2026 earnings call transcript (marketscreener.com) 2026 market & economic outlooks (morganstanley.com · off-topic) 85% GPU share faces competition; DOJ probe (finance.yahoo.com) Kept · 470 Nvidia demand continues to outweigh supply, JPMorgan says (finance.yahoo.com) Secondary NVIDIA Q4/FY2026 financial results (nvidianews.nvidia.com) Primary Nvidia halts H200 output as China export controls bite (finance.via.news) Secondary Analysts keep hiking targets — is NVDA too cheap? (barchart.com) Secondary Q4 2026 earnings call transcript (marketscreener.com) Primary 85% GPU share faces competition; DOJ probe (finance.yahoo.com) Secondary + 464 more sources kept, deduped and labeled 0 AI slop dropped 0 duplicates merged 0 kept and labeledOptions sentiment
finterm tool options_overview NVDA Reads the live options chain into put/call flow, IV30, and expected moves.
Neutral-bullish Call-tilted flow · IV30 below realized vol Puts Calls 0% call share of volume P/C (vol) 0.72 IV30 36.9% Exp. move (1mo) ±8.7%Insider and 13F flows, netted
finterm tool insider_trades NVDA Returns NVIDIA Form 4 insider transactions with the trailing 90-day open-market net.
SourceFilerActionSharesValue Form 4 M. Stevens · Director Sell −1.00M −$221.1M Form 4 A. Puri · EVP Ops Sell −270,134 −$49.2M Form 4 C. Kress · CFO Sell −30,201 −$5.3M Form 4 S. Gawel · PAO Grant +59,509 RSU 13F Rathbones Group Hold 6.05M $1.05B Open-market Form 4 · NVDA · 90d $0.0M net insider sellingTicker sentiment
finterm tool ticker_sentiment NVDA Scores seven components against NVDA’s own trailing year and returns the 0–100 composite.
0 extreme fear up from 6 a week ago · Δ5d +8.2 coverage 5/7 Fear Greed Trend 10 Flow — Positioning 20 Price vs 125-day average 17 52-week range position 2 Up-day volume share (20d) 12 Short interest 13 Implied-volatility rank 27 Put/call volume (5-day avg) omitted · history accruing Open-interest put/call tilt omitted · history accruing Each component scored vs NVDA’s own year · 100 = greed side § 02 · Why it existsFinterm connects the data first, so your agent can reason next.
It turns the messy inputs behind a trade into sourced, timestamped outputs: ticker data, filing diffs, ownership flows, market context, and deep research with the noise stripped.
finterm bundle run ticker_data NVDA One commandGive your agent a question and let the CLI pull the relevant financial context.
ticker_data.AAPL.yaml Agent-readable outputResults come back as structured data your agent can read, cite, and reuse.
SEC 8-K · 12:04Z ✓ Earnings call · Oct 30 ✓ Sources includedFacts carry source links, timestamps, and caveats instead of loose summaries.
§ 03 · Full demoOne prompt. Three filers. The pattern hiding in plain sight.
A real run: the prompt, your agent’s sourced answer, and the exact
finterm calls underneath.
Mapped the three cloud brands to their SEC filers (Alphabet/GOOGL, Amazon/AMZN, Microsoft/MSFT) and diffed consecutive 10-Qs section by section.
Ran SEC filing diff · Alphabet (GOOGL) Bash $ finterm tool sec_filing_diff GOOGL --base 2025:Q3 --compare 2026:Q1 MD&A, Risk Factors changed · QA-checked Ran SEC filing diff · Amazon (AMZN) Bash $ finterm tool sec_filing_diff AMZN --base 2025:Q3 --compare 2026:Q1 MD&A, Risk Factors changed · QA-checked Ran SEC filing diff · Microsoft (MSFT) Bash $ finterm tool sec_filing_diff MSFT --base 2026:Q2 --compare 2026:Q3 MD&A, Risk Factors changed · QA-checkedFour themes show up across all three filings:
- An AI-capex step-change. All three roughly doubled
quarterly capex year over year, tied explicitly to AI infrastructure.
“During the three months ended March 31, 2025 and 2026, we spent $17.2 billion and $35.7 billion on capital expenditures, respectively.” Alphabet 10-Q · FY2026 Q1 · MD&A
- AI written into the risk factors. Each amended
long-standing risk language to name AI as a new vector.
“…building AI-optimized infrastructure, including our custom TPUs… Our investments ultimately may not be commercially viable or may not result in an adequate return of capital.” Alphabet 10-Q · FY2026 Q1 · Risk Factors
- Multi-billion stakes in frontier-AI labs. The build-out
now runs through the model labs themselves.
“In Q1 2026, we invested $15.0 billion in OpenAI’s Series C Preferred Stock and entered into an equity commitment letter agreement to purchase an additional $35.0 billion…” Amazon 10-Q · FY2026 Q1 · MD&A
- Off-balance-sheet data-center and power exposure. Growth
leans on uncommenced leases and credit backstops for infrastructure they
don’t own.
“…up to $33.3 billion of future backstops to support the build-out of data center and energy supply infrastructure, alongside uncommenced data-center leases of $75.6 billion.” Alphabet 10-Q · FY2026 Q1 · MD&A
Net: across Google Cloud, AWS, and Azure, the last few quarters of 10-Qs converge on a capital-intensive AI build-out that is now visible in both the financials and the risk language. Every quote links to the exact filing.
Real Finterm output · figures quoted from public filings Not investment advice § 04 · Prompts to tryPoint your agent at the hard questions.
The same shape: your prompt, then Finterm’s sourced answer, across the questions a serious analyst asks.
Is this dip in NVDA being bought or sold by insiders? Pull Form 4 activity and the latest 13F holders, and net it out.
Give me the bull and bear case on AMD’s data-center demand this quarter, from multiple sources, deduped and labeled primary or secondary.
What’s the options market pricing into TSLA earnings, and where does sentiment sit versus its own trailing year?
Diff Apple’s latest 10-K against last year’s and flag every new or softened risk factor.
§ 05 · Everything in the bundleEverything in the bundle—and what it costs bought separately.
One subscription replaces four or five. The comparison, category by category—including the rows you can buy elsewhere.
In the bundle Comparable cost The company packet. Earnings, guidance, price reaction, ratios, options, short pressure, technicals—one call. $60–150/mo SEC filing diffs. Section-aware, line-by-line diff of any two filings, churn stats, QA-checked. No real equivalent SEC search and sections. Find any filer’s filings; pull clean narrative sections as text. $49/mo Ownership flows, netted. Form 4 trades rolled into a 90-day open-market net; 13F holders, ticker-mapped. $49–149/mo Options intelligence. Implied versus realized with rank, today’s flow, positioning, expected moves—one call. $99–199/mo Sentiment as a number. A 0–100 composite of trend, flow, and positioning, each scored against the ticker’s own year. $59–99/mo Ticker Deep Research. 600–800 links per ticker filtered into one research packet: noise stripped, reprints merged, sources tiered, provenance kept. No real equivalent Prices and statements. Live prices, reported financial statements, standard technicals. $20–99/mo The dataroom. Every run cached to a local, re-queryable room — produced once, reused every session. No real equivalentPurchased separately: $300–450/month (APIs alone)
Finterm Pro: $200/month (fetched data cached, yours to keep)
Category price ranges from published provider price lists, as of July 2026.
Give your agent a finance brain.
Sign up $150/month $200/month 3-day free trial3-day free trial, card required.
Special preview price for the first 1,000 users: $50 off for a limited time during the preview period.
Once you have an account, just give finterm to your favorite
coding agent:
npm install -g @finterm-ai/cli
Copy
Or paste into your coding agent I want to set up finterm.ai: run `npm install -g @finterm-ai/cli`, then `finterm setup` to install your agent skills and `finterm prime` to orient yourself. I’ll handle the browser step of `finterm auth login` when you’re ready—then show me what Finterm can do.
CopyNot investment advice.
相关条目
精选DeepSeek V4 在 OpenRouter 上的代币份额翻倍,智能体工作负载成主要驱动力
DeepSeek V4 的快速采用表明市场对高性能、低成本推理模型的强烈偏好,尤其是智能体场景将重塑 AI 部署格局。
91
精选启用两项设置使ARC-AGI-3基准测试分数提升三倍
这项发现太关键了!它表明模型的性能不仅取决于训练,还取决于推理时的配置。对于AI工程师和研究者来说,这是一个必须注意的优化方向。特别是那些在资源受限设备上部署模型的团队,可能会因此找到低成本提升能力的方法。
91
精选GPT-5.6:前沿智能与前沿效率的融合
这条消息值得关注,因为它标志着大模型竞争从单纯拼参数转向拼效率。对于AI开发者和企业决策者来说,GPT-5.6意味着可以用更少预算获得更高性能,尤其适合那些被推理成本挡在门外的长上下文或Agent场景。OpenAI这一步,可能重新定义‘前沿’的标准。
91
精选LFM2.5-Encoder:面向CPU快速长上下文推理的新模型
这篇博文让我眼前一亮,因为它直接解决了大模型在CPU上跑长上下文的痛点。对于没有高端GPU的团队或个人开发者,LFM2.5-Encoder系列提供了可立即使用的开源方案。尤其推荐给做RAG、文档搜索或代码分析的朋友——现在用普通服务器就能处理百万级token的文档了。
91
精选GPT-Red:OpenAI通过自我对弈实现AI红队自动化,提升模型鲁棒性
GPT-Red 是红队测试从人工到自动化的里程碑,特别适合安全工程师和对齐研究者关注。它直接降低了测试成本,但也引发了对“自动攻击”本身的伦理讨论——值得所有关注AI治理的人深入思考。
91
精选OpenAI推出GPT-5.5生物漏洞赏金计划
这个计划值得关注,因为它首次将AI漏洞赏金制度拓展到生物安全领域,不仅为安全社区提供了新的研究方向,也为AI风险治理提供了实际演练场。对模型安全研究者、生物安全专家以及关心AI双重用途的从业者尤其重要。
91
精选vLLM推出原生速度Transformers建模后端,大幅提升推理性能
这项更新对于AI工程团队非常关键:无需格式转换意味着能更快地迭代模型,而2-5倍的速度提升直接降低GPU成本。如果你正在做LLM在线服务或者批量推理,这几乎是必读的技术更新。
90
精选Google DeepMind 发布手语转文本模型 SL2T,助力聋人用户
这则消息的意义不仅在于技术突破,更在于AI开始真正关注边缘群体的需求。对开发者而言,SL2T的架构思路和技术路径值得深入研究;对无障碍倡导者来说,这是推动行业标准的重要案例。