Adjustment factors now available

We're excited to announce that our adjustment factors dataset is now available over our reference data API. This dataset combines all corporate action events into a single adjustment ratio for any given day, covering not just forward and reverse splits but every type of capital change. For users primarily focused on adjusted prices, it provides a simpler alternative to our corporate actions dataset.

Adjustment factors are applied to historical data to account for the effects of various corporate actions on a security's price and maintain price continuity over long holding periods. It's essential to back-adjust all end-of-day (EOD) prices to calculate derived per-share data accurately, including earnings per share, price-to-earnings ratio, dividends per share, assets per share, and cash flow per share.

Each record in this dataset specifies the country, security, corporate actions event, adjustment factor, and more. Refer to our documentation for a complete list of fields.

Adjustment factors data is available over our HTTP reference data API and official Python client library. The reference API is based on the same conventions and timestamp formats as our historical and live APIs.

To access this dataset, contact our sales team using the form on our reference data pricing table. Pricing starts at $225 per month.

The Python example below demonstrates how to request the adjustment factor for every corporate action event associated with the Microsoft Corporation (MSFT) symbol for the year 2009.

import databento as db

client = db.Reference("YOUR_API_KEY")

df_raw = client.adjustment_factors.get_range(
    symbols=["MSFT"],
    stype_in="raw_symbol",
    start="2009",
    end="2010",
    countries=["US"],
)

More in-depth tutorials on how to apply adjustment factors and handle multiple stock selections can be found in our docs.

We'll expand Databento's reference data catalog by the end of Q4 2024 with the addition of our security master dataset.

Companies often change their corporate names or securities' ticker symbols, which can create confusion when joining historical data with real-time data. The security master dataset will provide a comprehensive range of external symbology mappings since 2005, including Bloomberg FIGI, ISINs, CFIs, FISNs, and CIKs, to enable the tracking of securities across different identifiers and naming conventions.