As I continue to build out my toolbox for analyzing and trading commodity markets, one of the early challenges I ran into was accessing quality historical futures data without committing to a massive subscription plan upfront.
That’s where Databento comes in.
Unlike many traditional data vendors, Databento has a refreshingly modern approach:
You only pay for the actual data you take, and in the format you want it.
For someone like me, who’s still exploring which data frequency and depth I really need, this is ideal. I don’t have to sign up to an annual contract or pay for datasets I don’t use. I can start small, experiment across markets, and scale up only when I’m confident in what I need.
What stands out about Databento so far
- Flexible pricing – You’re billed based on usage and bandwidth. Need daily OHLC for just a few commodities? That’s a tiny amount of data. Need tick-level depth for backtesting? You can do that too, but you decide how far you go.
- Supported markets – They cover energy, ags, metals, currencies, indices, and more — including CME Group markets, so it feels tailor-made for a commodity trader or analyst.
- Developer-friendly – Everything is built around APIs and modern workflows. Whether you prefer Python, R, or just downloading CSV files, the flexibility is there.
- No lock-in – I love that I can start collecting data in my preferred format and not get stuck in a proprietary ecosystem.
How I’m using it right now
I’ve begun by downloading daily OHLCV (Open, High, Low, Close, Volume) data for a few benchmark futures contracts: WTI Crude Oil, Corn, and Gold. It’s been straightforward to fetch what I need, and that allows me to immediately plug the data into my current analysis tools — no conversions, no fuss.
It’s also nice to know that if I later decide to explore more frequent data (like 1-minute bars or full tick data for intraday backtesting), I can do that without signing my life away.
Final thoughts
This “pay-for-what-you-use” model is a breath of fresh air in an industry where many data vendors expect you to invest well before even knowing if the product meets your needs.
So far, Databento seems like a smart and cost-effective way to build up a serious futures dataset — and I’m looking forward to expanding my use of it as I grow the analytical side of my commodity trading practice.
If you’ve been frustrated by the lack of accessible, reasonably priced futures data, I’d definitely put Databento on your radar.