Continuous contracts

Info

Our continuous contract symbology is a notation that maps to an actual, tradable instrument on any given date. The continuous contract prices provided are the original, unadjusted prices. Unlike some vendor implementations that back-adjust prices to remove jumps during rollovers, our approach maintains the original properties of the data.

When dealing with far-reaching historical data on expiring products (like the E-mini S&P 500 Futures), users have to be concerned with expiration cycles and what is defined as the front month or first expiry, second expiry, etc.

When requesting (E-mini S&P 500 Futures) data, we can specify the particular product we want. Take for example, ESH5, the March 2025 expiring E-Mini S&P 500 futures contract. Since this instrument has a March 2025 expiry, it will not exist after March 2025. Since March 2025 is the front month (i.e. the nearest expiration date for the contract) only from December 2024 to March 2025, the instrument's data dating before December 2024 could be considered irrelevant.

Continuous contracts symbology provides a way to navigate this complexity. You can easily access recent contracts: front month, second expiry, and so on.

Symbology

To access a specific contract in a continuous contract product, query for that contract using this symbol structure.

[ticker].v.[expiry]

Expiry

In place of the exchange's proprietary code for specifying the month of expiration (e.g. H5 for ESH5 in CME data), we use an index to refer to which expiry in the current cycle we want.

Algorithm

To request the front month for the ES product based on volume, use ES.v.0. In this example, the v indicates the calendar roll rule. The zero corresponds to the front month. Learn more about continuous contract symbology in our continuous symbology guide.

Example

Python

PythonRust

import databento as db

# Set parameters
dataset = "GLBX.MDP3"
product = "ES"
start = "2025-03-16"
end = "2025-03-23"

# Create a historical client
client = db.Historical("$YOUR_API_KEY")

# Request OHLCV-1d data for the continuous contract
data = client.timeseries.get_range(
    dataset=dataset,
    schema="ohlcv-1d",
    symbols=f"{product}.v.0",
    stype_in="continuous",
    start=start,
    end=end,
)

# Convert to DataFrame
df = data.to_df()

print(df)

The data received is now related to the volume-based front month for each day of the requested time range. If we are interested on the second month then we can just use ES.v.1 instead.

The results below are from the week of expiration for ES. As you can see, the contract that maps to the volume-based front month, as shown by the instrument_id field, changes mid-week as the volume shifts to the new contract.

Python

PythonRust

                           rtype  publisher_id  instrument_id     open     high      low    close   volume  symbol
ts_event
2025-03-16 00:00:00+00:00     35             1           5002  5626.75  5632.00  5602.50  5608.50    30078  ES.v.0
2025-03-17 00:00:00+00:00     35             1           5002  5608.75  5707.50  5599.00  5679.50   723022  ES.v.0
2025-03-18 00:00:00+00:00     35             1           5002  5679.75  5683.25  5600.00  5621.25   412431  ES.v.0
2025-03-19 00:00:00+00:00     35             1           4916  5672.50  5770.50  5657.50  5747.75  1331926  ES.v.0
2025-03-20 00:00:00+00:00     35             1           4916  5747.50  5765.25  5682.50  5714.75  1503542  ES.v.0
2025-03-21 00:00:00+00:00     35             1           4916  5714.25  5723.75  5651.25  5720.00  1454908  ES.v.0