Fundamentals Data for Finance LLM AI Agents: A Practical Grounding Layer

Tradevo Data has released a point-in-time US equity fundamentals dataset sourced from SEC EDGAR, designed as a grounding layer for finance LLM agents to avoid lookahead bias. The dataset covers 5,168 US companies with 632,466 annual point-in-time rows and 1,089,635 quarterly rows, and each record carries first_filed, original_value, latest_value, a restatement flag, and a QA status, with 37,804 restatements labeled. It is exposed through a single JSON endpoint that applies server-side point-in-time filtering via first_filed <= as_of.

Finance AI agents have a basic reliability problem: language models can produce plausible answers, including plausible numbers. Plausible is not enough when a user asks what a company reported, when the information became public, or whether the figure was later restated. One practical approach is a grounding layer built from dated financial facts. For developers building retrieval-augmented generation systems, filing copilots, screening agents, research assistants, or automated company briefs, point-in-time fundamentals can provide a structured bridge between SEC filings and model-generated explanations. The model can reason over the data, but it should not invent the data. Tradevo Data provides point-in-time US equity fundamentals sourced from SEC EDGAR. Its annual dataset covers 5,168 US companies, 632,466 point-in-time rows, 16 annual concepts, and up to 12 fiscal years. Its quarterly dataset contains 1,089,635 rows across 5,382 companies and seven quarterly concepts. Coverage varies by filer. A conventional fundamentals database may emphasize the latest known value. That can be useful for current analysis, but it may be inappropriate for a historical question. Suppose an agent is asked: What revenue and net income were publicly available for a company on June 15 of a past year? If retrieval returns a figure restated months later, the answer contains information that was unavailable on June 15. The number may now be technically correct while still being historically invalid for that date. That distinction matters for: This is lookahead bias at the data layer. A longer explanation is available at https://tradevodata.com/blog/lookahead-bias-fundamental-backtests?utm content=blog-grounding-a-finance-llm-in-as-reported-fundamentals&utm source=devto&utm medium=syndication&utm campaign=syndicate https://tradevodata.com/blog/lookahead-bias-fundamental-backtests?utm content=blog-grounding-a-finance-llm-in-as-reported-fundamentals&utm source=devto&utm medium=syndication&utm campaign=syndicate . A useful finance grounding record should do more than return a value. It should establish provenance. Each Tradevo Data row includes: first filed : the date the value became public original value : the first-reported, point-in-time-safe value latest value : the current revision restated : a flag for a greater-than-0.5% change under the same XBRL tag, including amendments qa status : a quality-assurance status The dataset labels 37,804 restatements. For an AI agent, those fields support a stronger response pattern: An XBRL tag is not a complete semantic guarantee: filers can use extensions, presentation varies, and accounting context still matters. But a dated record containing the original value, revised value, restatement flag, and QA status is more auditable than an unsupported number in model output. Tradevo Data exposes one JSON fundamentals endpoint with server-side point-in-time filtering: GET https://tradevodata.com/v1/fundamentals?ticker=AAPL&as of=2020-06-15&concept=Revenue&period=annual&utm content=blog-grounding-a-finance-llm-in-as-reported-fundamentals&utm source=devto&utm medium=syndication&utm campaign=syndicate The server applies: first filed <= as of The date is inclusive. Annual data is the default, while period=quarterly requests quarterly rows. A basic agent tool contract might look like this: { "tool": "get fundamentals", "arguments": { "ticker": "AAPL", "as of": "2020-06-15", "concept": "Revenue", "period": "annual" } } The model should then compose an answer from returned records rather than relying on memorized parameters. A system instruction can require it to include the fiscal period, first filed , original value , and concept in every numerical citation. For example: Use only retrieved values. If no qualifying row exists by the requested as-of date, say that the dataset returned no supported value. Do not infer or interpolate a financial figure. That final rule is important. Retrieval does not prevent hallucination unless the agent is explicitly required to abstain when evidence is missing. A filing agent can separate structured facts from unstructured context: Structured fundamentals are not a replacement for the filing. They can serve as a compact numerical layer alongside it. For bulk workflows, the $29-per-month Pro plan includes /v1/download and /v1/snapshot?as of , each supporting period=annual|quarterly . These endpoints can support a local retrieval store or reproducible historical snapshot. Parquet format is not included. More background on the data model is available at https://tradevodata.com/blog/point-in-time-fundamentals-data?utm content=blog-grounding-a-finance-llm-in-as-reported-fundamentals&utm source=devto&utm medium=syndication&utm campaign=syndicate https://tradevodata.com/blog/point-in-time-fundamentals-data?utm content=blog-grounding-a-finance-llm-in-as-reported-fundamentals&utm source=devto&utm medium=syndication&utm campaign=syndicate . The annual dataset contains these 16 concepts: Quarterly coverage includes seven concepts. Q4 is reported where tagged or derived and labelled where supported. Derived Q4 EPS and share figures are not provided. The service does not provide TTM calculations, non-US coverage, delisted-company coverage, or Parquet output. It is not presented as a full filing-text corpus, market-data feed, estimates database, or accounting ontology. These limits matter. An agent should not present absence as zero, assume every filer reports every concept, or silently substitute one accounting concept for another. | Option | Best fit | Potential strengths | Important considerations | |---|---|---|---| | Tradevo Data | Developers wanting a budget-tier API and bulk access for core US fundamentals | Server-side as of , original and latest values, filing dates, restatement labels, and QA status | Limited concept set; no delisted, TTM, non-US, or Parquet coverage | | Raw SEC EDGAR/XBRL | Teams needing maximum control and direct primary-source processing | Public-domain source material and direct control over interpretation | Requires ingestion, taxonomy mapping, duplicate handling, amendment processing, QA, and point-in-time logic | | Sharadar | Researchers evaluating a broader established data product | A credible alternative with its own coverage and data model | Confirm current scope and licensing; see their pricing page: https://data.nasdaq.com/databases/SF1 https://data.nasdaq.com/databases/SF1 | | Tiingo | Developers evaluating fundamentals alongside other financial APIs | A credible provider with a broader product context | Verify point-in-time semantics and coverage; see their pricing page: https://www.tiingo.com/pricing/overview https://www.tiingo.com/pricing/overview | | QuantConnect | Teams building inside an integrated research and execution platform | Data access within a broader algorithmic environment | Platform fit may matter more than a standalone fundamentals API; see their pricing page: https://www.quantconnect.com/pricing/ https://www.quantconnect.com/pricing/ | No provider should be chosen from a feature label alone. Test amendment handling, filing-date semantics, survivorship assumptions, missing values, concept normalization, redistribution rights, and historical coverage against your actual agent workflow. Another provider can be the better choice when you need a broader security universe, delisted companies, non-US issuers, more accounting concepts, analyst estimates, TTM figures, market data, or a managed research platform. A specialist vendor may also win when your institution needs enterprise support, contractual service levels, or licensing terms beyond a developer-oriented product. If your workflow already runs inside QuantConnect, its integrated environment may reduce engineering work. If you need a broader commercial dataset, Sharadar or Tiingo may fit better after you verify current coverage and point-in-time behavior. Tradevo Data is positioned as an honest budget tier of research-grade point-in-time data, not as the only affordable option and not as a universal financial-data layer. Build directly from SEC EDGAR when provenance control is more important than implementation speed, your required concepts fall outside the supported set, or your team has specialized accounting and data-engineering expertise. Be prepared to handle: EDGAR is public domain, so self-building can be rational. The practical burden is maintaining consistent interpretation and QA over time. A public proof pack is available at https://github.com/christianpichichero-max/pit-fundamentals https://github.com/christianpichichero-max/pit-fundamentals . It contains five companies, their latest three fiscal years, 225 rows, and the full methodology with no signup. On reliable-filing rows in that five-company proof pack only, measured lookahead averaged 35.2 days and reached a maximum of 48 days. Those measurements describe the proof pack, not the full dataset. For API evaluation, https://tradevodata.com/?utm content=blog-grounding-a-finance-llm-in-as-reported-fundamentals&utm source=devto&utm medium=syndication&utm campaign=syndicate https://tradevodata.com/?utm content=blog-grounding-a-finance-llm-in-as-reported-fundamentals&utm source=devto&utm medium=syndication&utm campaign=syndicate offers a card-backed seven-day free trial covering 10 companies, a rolling three-year history, and 100 requests per day without bulk. Unless canceled, it renews at $29 per month. Pro provides complete available history, 5,000 requests per day, and bulk endpoints. Documentation is at https://tradevodata.com/docs?utm content=blog-grounding-a-finance-llm-in-as-reported-fundamentals&utm source=devto&utm medium=syndication&utm campaign=syndicate https://tradevodata.com/docs?utm content=blog-grounding-a-finance-llm-in-as-reported-fundamentals&utm source=devto&utm medium=syndication&utm campaign=syndicate . Start with the public sample, test the dates and concepts against source filings, and make abstention part of the agent design. The goal is not to make a model sound certain. It is to make supported financial numbers traceable. Not investment advice.