Built by an engineer who got tired of bad data.
Valuein is a one-person company. I'm Rainer Arencibia — founder, engineer, and the only person on the on-call rotation. I built Valuein because every existing financial data vendor either costs $24K/year, or quietly leaks future information into your backtests, or both. I needed a clean SEC fundamentals dataset for my own research and couldn't find one I trusted, so I built it.
105M+
Standardized Financial Facts
16,000+
Companies Covered
1994
History Starts
8
Parquet Tables Per Tier
Why this exists
The financial data market is broken in three quiet ways. It is too expensive for individual analysts and small funds — Bloomberg starts at $24K/year per seat, WRDS academic licenses are six figures, and every “cheap” alternative tops out somewhere short of usable.
It carries survivorship bias — datasets that drop bankrupt and delisted companies make every backtest look 200 bps better than reality. Strategies that only work because Enron isn't in your training data aren't strategies.
And it is not point-in-time — most vendors silently overwrite amended filings, so when you query “Apple's 2018 revenue” you get the restated value, not what the market actually saw on January 1, 2019. That single bug invalidates most published quant research.
Valuein fixes all three. The same SEC EDGAR primary source everyone else uses, parsed and standardized into a queryable Parquet warehouse, with accepted_at on every fact and every delisted ticker preserved. Free for S&P500 history, $49/mo for the full universe.
How a one-person company runs at this scale
Everything is automated and edge-native. The data pipeline runs on schedule, ingests the latest SEC submissions, normalizes XBRL into ~200 canonical concepts, runs validation checks, and pushes Parquet files to per-tier object storage. The MCP server, Bulk Data API, and Python SDK all read from the same warehouse — no duplication, no drift.
The frontend, the API, the rate limiter — all run at the edge. There's no kubernetes cluster to babysit, no separate “ops” team. When a customer reports a bug, the same person who built the pipeline reads the message and ships the fix.
That model has limits. I won't pretend to offer white-glove account management at the Pro tier. What I can offer is honest documentation, a real status page, public methodology, and a founder who answers his own emails. If that's the trade you want to make, the free tier is one click away.
Two channels. One dataset.
Whether you're a Python quant or building an AI-powered workflow, Valuein meets you where you work.
- Python SDK →
pip install valuein-sdk (or uv pip install valuein-sdk) — DuckDB-backed DataFrames in 60 seconds. SQL-native, no black-box abstractions, no API key required to start.
- MCP Server →
One Bearer token, every MCP-compatible client. Claude, Cursor, Codex, ChatGPT — all query SEC fundamentals as a first-class tool.
Try it before you buy it.
Sample tier is one click — no signup, no credit card. S&P500 companies, 5 years of history, free forever.