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Build your first application

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Examples and tutorials

Core concepts

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Quickstart

Set up Databento

You need an API key to request data from Databento. Sign up and you will automatically receive an API key. Each API key is a 32-character string starting with db-, that can be found from the API keys page on your portal.

Info
Every new account receives $125 in free data credits. These credits allow you to start receiving actual historical data immediately and verify your integration at no cost.

Choose a service

Databento provides two data services: historical and live. These two services are nearly identical, but we separate them due to licensing fees and the differences between response-request and real-time streaming APIs. You can choose to integrate just one or both services.

	Historical	Live	Reference
Coverage	Data from at least 24 hours ago.	Intraday historical data from within the last 24 hours and real-time data.	Historical with intraday updates.
Pricing	Usage-based or flat-rate. No monthly license fees.	Usage-based or flat-rate. Monthly license fees apply.	Dataset license fees apply.
Access	Client libraries (C++, Python, and Rust) and API (HTTP).	Client libraries (C++, Python, and Rust) and API (Raw).	Client libraries (Python) and API (HTTP).

Build your first application

Historical

Live

Reference

Select how you will be integrating Databento below to see installation instructions. By default, Python has been selected for you.

If you don't see an official client library for your preferred language, you can still integrate our historical service through its HTTP API.

HISTORICAL DATA

Client Libraries

Python

C++

Rust

APIs

HTTP

pip install -U databento

209

Install our Python client library with pip. Python 3.9+ is required.

$ pip install databento

A simple Databento historical application looks like this.

This replays 10 minutes of trades of the entire CME Globex market event-by-event.

Copy this to a file main.py. Then, run the file with python main.py.

PythonC++RustHTTP

      
    
import databento as db

client = db.Historical("$YOUR_API_KEY")
data = client.timeseries.get_range(
    dataset="GLBX.MDP3",
    symbols="ALL_SYMBOLS",
    start="2022-06-02T14:20:00",
    end="2022-06-02T14:30:00",
)

data.replay(print)

You can modify this application to specify particular instruments, and schemas.

Let's get ESM2 and NQZ2 data in 1-second OHLCV bars.

PythonC++RustHTTP

      
    
import databento as db

client = db.Historical("$YOUR_API_KEY")
data = client.timeseries.get_range(
    dataset="GLBX.MDP3",
    symbols=["ESM2", "NQZ2"],
    schema="ohlcv-1s",
    start="2022-06-06T14:30:00",
    end="2022-06-06T14:40:00",
)

data.replay(print)

You have successfully written your first historical data application with Databento! Here are shortcuts to some of the next steps you can take:

To download a large amount of data to disk, see how to do a batch download of data files.
To get another dataset, just swap the dataset. You can get a list of datasets and their names from our metadata.
You can use our symbology API to find other instrument IDs.
You can also find dataset names and instrument IDs interactively from our search.

To learn more, read the full documentation for our historical service under API reference - Historical.