From pip install to
10 years of fundamentals
in 60 seconds.
Bitemporal SEC EDGAR data. Python SDK. REST API. Parquet. Built so you spend time on alpha, not on data cleaning.
# Install once
$ pip install valuein
import valuein as v
v.authenticate('vi_live_your_key')
# 10 years of AAPL in one line
df = v.fundamentals(
ticker='AAPL',
form_type='10-K',
period='10y'
).to_dataframe()
# Returns a pandas DataFrame
df.shape # (40, 42)Built for production quant workflows
Bitemporal Data Integrity
Every fact carries a knowledge_at timestamp. Query what was known at any historical date — critical for walk-forward backtesting.
Point-in-Time Accuracy
No look-ahead bias. Your 2015 backtest uses only data that was available in 2015, including amendment tracking.
Zero Survivorship Bias
Delisted, bankrupt, and acquired companies are fully included in the universe. No distorted historical performance.
Native Parquet Format
Columnar storage compatible with DuckDB, Polars, and Spark. Query 105M rows locally with zero egress cost on sample data.
Sub-100ms API Latency
Cloudflare edge infrastructure. Median response time under 100ms globally. P99 under 500ms.
Schema Stability Guarantee
Field names and types are versioned. Breaking changes are never shipped without a major version bump and 90-day deprecation notice.
Real data. Right now.
AAPL 10-K annual fundamentals — standardized from SEC EDGAR (USD millions)
| ticker | period_end | revenue ($M) | net_income ($M) | eps_diluted | form_type |
|---|---|---|---|---|---|
| AAPL | 2023-09-30 | 383,285 | 96,995 | 6.13 | 10-K |
| AAPL | 2022-09-24 | 394,328 | 99,803 | 6.11 | 10-K |
| AAPL | 2021-09-25 | 365,817 | 94,680 | 5.61 | 10-K |
| AAPL | 2020-09-26 | 274,515 | 57,411 | 3.28 | 10-K |
| AAPL | 2019-09-28 | 260,174 | 55,256 | 2.97 | 10-K |
Live data from the Sample tier. Revenue and net income in USD millions. Available via API and Python SDK.
Ready to build?
Free tier includes 10 tickers, 5-year history. No credit card required.