---
license: cc0-1.0
language:
  - en
pretty_name: Point-in-Time US Equity Fundamentals (Sample)
tags:
  - finance
  - stocks
  - fundamentals
  - backtesting
  - sec-edgar
size_categories:
  - 1K<n<10K
task_categories:
  - tabular-regression
configs:
  - config_name: default
    data_files: pit_fundamentals_history.csv
---

# Point-in-Time US Equity Fundamentals (Sample)

A free, verifiable sample of **point-in-time** US equity fundamentals from SEC EDGAR — 40 large-cap companies, 3,280 annual rows. Every value is stamped with the date it *first became public*, so a backtest only ever sees what was knowable at the time. **No lookahead bias.**

This is the open sample of [Tradevo Data](https://tradevodata.com); the full API covers 5,202 companies.

## Why point-in-time matters

Most fundamentals datasets store one row per fiscal period keyed by the *period-end* date, and silently overwrite numbers when companies restate. Joining on period-end hands your backtest data months before it was public; using restated values lets it "see" corrections that didn't exist yet. Both quietly inflate returns. This dataset fixes both, by keeping the original first-reported value and its filing date.

## The clearest example in the data

Amazon's FY2020 diluted share count reads **510,000,000** as originally filed, and
**10,198,000,000** in EDGAR today — the 2022 20-for-1 split, applied backwards through
history. Both numbers are correct; they are denominated in different shares.

This is the case that survives the usual defence. Everyone knows to wait for the filing date
before using a 10-K, and that discipline does nothing here: the filing date is right, and the
value is still denominated in shares that did not exist on it. Pair today's figure with a 2020
price and market cap is wrong by exactly 20×.

**189 of the 3,280 rows** differ between `original_value` and `latest_value`. Three distinct
causes trip that flag and they need different handling — 20 are retroactive split adjustments
(AAPL 4:1, AMZN 20:1, NVDA 4:1 then 10:1, TSLA 5:1 then 3:1, WMT 3:1, NFLX 7:1 then 10:1, AVGO
10:1, NKE 2:1), a handful are accounting-standard adoption rather than error (Salesforce's
FY2017–18 net income moves are ASC 606 applied retrospectively), and the rest are genuine
revisions and 10-K/A amendments. `METHODOLOGY.md` separates them.

## Columns

| column | meaning |
|---|---|
| `ticker` | US stock symbol |
| `cik` | SEC Central Index Key |
| `concept` | one of: Revenue, NetIncome, Assets, StockholdersEquity, OperatingCashFlow, EPSDiluted, DilutedShares |
| `xbrl_tag` | the exact XBRL tag the value came from |
| `fiscal_year` | issuer fiscal year |
| `period_end` | fiscal period end date |
| `first_filed` | **the date this value first became public** (the point-in-time key) |
| `lag_days` | days between `period_end` and `first_filed` |
| `filed_reliable` | whether the filing date is high-confidence |
| `original_value` | value **as first reported** — use this for point-in-time backtests |
| `latest_value` | most recent revised value (may post-date `first_filed`) |
| `restated` | true if later amended by more than 0.5% |
| `qa_status` | per-row quality verdict (`clean` or `FLAG:…`) |

## Usage

```python
from datasets import load_dataset
ds = load_dataset("Tradevodata/point-in-time-us-equity-fundamentals-sample")
```

For point-in-time correctness, filter to `first_filed <= your_as_of_date` and use `original_value`.

## Provenance & license

Source: U.S. SEC EDGAR (public domain). Data is CC0-1.0. See `METHODOLOGY.md` for exactly how it was built. Not investment advice; verify against primary filings before relying on it.

Full dataset + JSON API with `as_of` queries: **[tradevodata.com](https://tradevodata.com)** · methodology + code: **[github.com/christianpichichero-max/pit-fundamentals](https://github.com/christianpichichero-max/pit-fundamentals)**