Alpaca Fundamentals Data Alternative

If you're searching for an "Alpaca fundamentals data alternative," you've probably already discovered the thing this page is about: Alpaca is a genuinely good commission-free brokerage with solid execution and market-data APIs (bars, quotes, trades), but fundamentals were never its core product. That's not a knock — it's just not what the platform is built around. For anyone running a fundamentals-driven backtest and trying to avoid lookahead bias, that gap matters.

This page is a straight comparison, not a takedown. Alpaca does things we don't do — most notably, it's a real brokerage with real order routing, which we are not and don't pretend to be.

What Alpaca is actually good at

Alpaca's strength is the combination of commission-free execution and a developer-friendly market-data API layered on top of it. If you want to go from backtest to live paper trading to live money in one stack, with bars, quotes, and an actual broker connection, Alpaca is a credible, widely-used choice. That integration — data and execution under one account, one API key, one bill — is a real advantage. We don't have it. We're a data-only product.

Where the fundamentals gap shows up

The issue isn't that Alpaca's fundamentals data is bad — it's that fundamentals-style data on most retail-brokerage-adjacent platforms is typically served as "current" figures: the latest known value for a company, not the value that was actually public knowledge on a given historical date. For live trading that distinction barely matters. For backtesting, it's the whole ballgame.

When a backtest pulls "current" fundamentals and applies them to a historical date, it can accidentally use information that didn't exist yet — a restated Revenue figure, a post-amendment EPS, a number that only became public weeks or months after your backtest thinks it did. That's lookahead bias, and it can inflate paper returns in ways that don't survive contact with live trading. This is not a claim about any specific strategy's performance — just a mechanical data-integrity issue. We wrote up the mechanics in more detail here: /blog/lookahead-bias-fundamental-backtests, and the general concept of point-in-time data here: /blog/point-in-time-fundamentals-data.

What Tradevo Data actually is

Tradevo Data is narrow on purpose. It's point-in-time US equity fundamentals sourced directly from SEC EDGAR — public domain filings, 10-K and 10-K/A only, annual granularity. No quarterly yet (it's on the roadmap), no brokerage, no execution, no order book. Just the fundamentals, timestamped the way a backtest actually needs them.

Every row carries:

• first_filed — the date the value became public
• original_value — the first-reported figure, safe to use for point-in-time backtests
• latest_value — the current, possibly-restated figure
• restated flag — set when the same tag changed by more than 0.5% for the same concept, including amendments
• qa_status

Coverage is 5,189 US companies, 312,751 point-in-time rows, 7 concepts (Revenue, NetIncome, Assets, StockholdersEquity, OperatingCashFlow, EPSDiluted, DilutedShares), up to 12 fiscal years of history, with 18,723 restatements labeled so you can see exactly where "current" and "as-it-was-known" diverge.

On the free 40-company sample, we measured the gap between filing date and when a naive "current value" dataset would have assumed the number was known: it averaged 43.4 days, with a max of 61 days, on the sample's reliable-filing rows. That stat is scoped to the sample only — we have not measured and are not claiming an equivalent lookahead figure for the full 5,189-company dataset.

Access is one JSON endpoint — /v1/fundamentals?ticker&as_of[&concept] — where as_of is a server-side filter (first_filed <= as_of, same-day inclusive), so a query for a historical date only returns what was actually known by then. Bulk access is included in the same price: /v1/download for the full gzipped CSV, and /v1/snapshot?as_of for a whole-universe cross-section at any date. Parquet is roadmap, not shipped yet. 5,000 requests/day, instant API key after Stripe checkout, cancel anytime, docs at /docs.

Fair comparison

We're not claiming to be cheaper, faster, or better across the board — just narrower and more transparent about one specific problem: knowing what was actually public on a given date.

When Alpaca (or building it yourself) is the better choice

Be honest with yourself about what you're optimizing for:

• You need to actually trade, not just backtest. If your workflow goes from signal to live order, you need a broker in the loop somewhere. We're not one. Alpaca's combined data-plus-execution stack is a legitimate reason to stay there or use it alongside a PIT data source.
• You need real-time or intraday price data. Bars, quotes, trades — that's Alpaca's actual strength, and we don't compete there at all.
• You need quarterly fundamentals today. We're annual-only right now (quarterly is roadmap). If your strategy rebalances quarterly on fundamentals, that's a real gap for us, not a minor one.
• You need non-US equities. We're US-only, full stop.
• You have the engineering time and EDGAR patience to build point-in-time logic yourself from raw filings. It's doable — SEC EDGAR is public domain, and plenty of quant teams roll their own. It's just slower to build correctly than it looks, especially around amendments and restatement handling. Sharadar, Tiingo, and QuantConnect are also credible sources for fundamentals with their own approaches to point-in-time handling — worth comparing directly, including their pricing pages, before deciding.

If none of those apply — if what you actually need is "what did the market know about this company's fundamentals on this specific date, and did it later get restated" — that's the one thing we built.

Try it before you pay for it

Start with the free sample: 40 companies, 3,280 rows, full methodology, no signup, at github.com/christianpichichero-max/pit-fundamentals. Run your own lookahead-bias check against whatever you're using today.

If it holds up, the full dataset — 5,189 companies, 312,751 rows, bulk download and snapshot endpoints included — is $49/mo at https://tradevodata.com/?utm_content=alt-alpaca.

Not investment advice; verify competitor pricing yourself on their respective pricing pages.