dataforge-labs/equity-perp-price-discovery

Equity and pre-IPO perpetual prices

Snapshots of perpetual-futures mark prices, index prices and basis from Aevo. The instrument universe includes equities, ETFs, commodities, foreign exchange, pre-IPO contracts and crypto assets.

Contents

Table	Record
perpetual_mark_and_index_prices	An instrument's mark price, index price and basis at an observation time

Using the data

market_type identifies the instrument category. is_rwa flags the real-world-asset subset. Crypto contracts provide a comparison group when studying equity-linked contracts outside cash-market hours.

basis_bps is (mark - index) / index * 10,000. Missing inputs produce a null basis. Group by instrument and observation time to study how the venue's mark changes relative to its reference index.

For comparisons with cash markets or other venues, align timestamps and use only information available at the observation time. Inspect the ages of matched observations; a later reference price introduces look-ahead bias.

Limitations

• Marks are venue reference prices used for margining. They are not executable bids, asks or necessarily recent trades.
• Index construction may differ by instrument. An equity index observed outside cash-market hours should not be assumed to equal the last cash close.
• Pre-IPO contract indices are venue-defined references, not public share prices.
• Instrument listings and coverage change over time. Count the available instruments in each analysis period.
• Basis alone does not establish a tradable opportunity. This table does not provide complete hedge execution, funding or borrowing costs.

Files and access

Data is stored as Parquet files under table_name/YYYY/MM/, with partitions for collection windows. Each measurement table has a fixed 7-day sample beginning at its configured collection start date. The sample windows in this repository span 2026-08-29 to 2026-09-04. Availability within each window depends on successful collection. The public sample dates remain fixed as additional history accumulates privately. Contact DataForge through the discussions tab to enquire about additional history.

Load a table

Install datasets and pandas to run this example. The train split contains all observations in the selected table; it is not a predefined modelling split.

from datasets import load_dataset

data = load_dataset("dataforge-labs/equity-perp-price-discovery",
                    "perpetual_mark_and_index_prices", split="train")
df = data.to_pandas()

Coverage

collection_runs records collection windows, poll counts and failures. It is published in full and may cover dates beyond the fixed data sample. Collection gaps are not interpolated. Use this table together with measurement timestamps and error fields to assess coverage.

License and contact

The public sample is published under ODC-BY. Attribute it to "DataForge (dataforge-labs)". For questions about the data or access to additional history, open a discussion in this repository.

Earlier file paths

Each table is stored under a directory with the same descriptive name. The file contents and date partitions are unchanged. Scripts using an earlier directory name should use the corresponding table name below, or pin downloads to revision before-folder-rename-20260915 to access the original layout. Internal collector IDs are retained in the private archive and may appear in raw coverage records.

Current table and directory	Earlier directory
perpetual_mark_and_index_prices	e23_perp_mark_index/
collection_runs	e0_run_manifest/