This dataset requires a paid license. Please purchase access at the link below and provide your Hugging Face username. Once purchased, submit an access request on this page. Access is granted manually.

Bitnomial Crypto Derivatives Liquidity and Funding Dataset

Question this dataset helps answer: Which Bitnomial crypto derivatives contracts show meaningful volume, open interest, and perpetual funding regimes worth monitoring for market-structure research?

This dataset helps crypto derivatives researchers, dashboard builders, and systematic-market analysts screen Bitnomial product-day liquidity using daily volume, open interest, notional activity, rolling activity fields, and same-day perpetual funding-rate summaries.

Use the public sample to check:

Which Bitnomial products have nonzero volume and open interest by date.
How perpetual funding-rate summaries line up with daily notional volume and open interest.
Whether rolling 7-day activity fields are suitable for a liquidity monitor or research dashboard.

Preview vs full dataset:

Preview: 521 rows and 39 columns
Full: 10,428 rows and 39 columns

Full dataset access: https://thearticulated.gumroad.com/l/lpbgqx

from datasets import load_dataset

dataset = load_dataset("Karmane/bitnomial-crypto-derivatives-liquidity-funding-sample")
df = dataset["data"].to_pandas()
print(df.head())

The full dataset includes 10,428 rows across Bitnomial crypto derivative product-day observations, making it large enough for dashboard analysis, historical filtering, liquidity-screen research, and exploratory modeling workflows.

Responsible use note

This dataset is for research, analysis, dashboard building, educational use, and exploratory modeling. It does not provide trading advice, signals, guarantees, or predictions of future outcomes.

Modeling notes

Each row represents one Bitnomial product-day volume and open-interest observation from the public VOI chart endpoint. The observation interval is daily. Rows are not trades; they are daily product-level liquidity snapshots. For perpetual products, finalized 8-hour funding intervals ending on the same UTC date are aggregated into same-day summary fields. funding_rate_mean, funding_rate_min, funding_rate_max, mark_price_mean, price_index_mean, and mark_to_index_basis_bps_mean are calculated only from finalized funding intervals available from the public funding endpoint. Daily volume and open interest are sampled from the chart endpoint as product-day values. Missing funding fields generally mean the row is not a perpetual product or the funding endpoint did not return intervals for that symbol/date. Base symbols and product types are normalized from the public API fields without guessing contract specifications.

Fields known at observation time include the product identity, date, same-day volume/open-interest values, same-day finalized funding summaries for intervals already ended, and rolling fields based on prior product observations in this exported history. Future-outcome fields are not included. Time-based splits should sort by observation_timestamp_utc; observations from the same product should not be randomly divided without product-level or time-group controls because adjacent rows share market context. Limitations include public API coverage, Bitnomial product-launch history, inactive-product rows, repeated source dates where the API returns duplicate chart records, and the absence of private order-book or account-level data.

Commercial use cases

Build a Bitnomial crypto derivatives liquidity monitor.
Screen products by notional volume, open interest, and rolling activity.
Compare perpetual funding-rate regimes against product-day activity.
Prototype feature engineering for derivatives market-structure research.
Replace manual historical collection from separate public chart and funding endpoints.

Source notes

Data is collected from public Bitnomial exchange API endpoints and documentation pages. No login, exchange account, broker credential, or private account data is used.

Dataset Preview

record_id	source_platform	product_id	base_symbol	product_type	observation_date	observation_timestamp_utc	has_volume	has_open_interest	liquidity_activity_score	is_perpetual_product	source_voi_url	source_funding_url	source_docs_url	last_collected_at
bitnomial-1755-ADUY-future-2026-06-22	Bitnomial	1755	ADUY	Future	2026-06-22	2026-06-22 00:00:00+00:00	False	False	47.73	False	https://bitnomial.com/exchange/api/web/charts/voi	https://bitnomial.com/exchange/api/v1/funding-rates/	https://bitnomial.com/exchange/docs/api/rest/funding-rates/	2026-07-19T16:19:36.904854+00:00
bitnomial-1756-LTUH-future-2026-06-22	Bitnomial	1756	LTUH	Future	2026-06-22	2026-06-22 00:00:00+00:00	False	False	47.73	False	https://bitnomial.com/exchange/api/web/charts/voi	https://bitnomial.com/exchange/api/v1/funding-rates/	https://bitnomial.com/exchange/docs/api/rest/funding-rates/	2026-07-19T16:19:36.904854+00:00
bitnomial-1757-UCUY-future-2026-06-22	Bitnomial	1757	UCUY	Future	2026-06-22	2026-06-22 00:00:00+00:00	False	False	47.73	False	https://bitnomial.com/exchange/api/web/charts/voi	https://bitnomial.com/exchange/api/v1/funding-rates/	https://bitnomial.com/exchange/docs/api/rest/funding-rates/	2026-07-19T16:19:36.904854+00:00
bitnomial-1758-CLUK-future-2026-06-22	Bitnomial	1758	CLUK	Future	2026-06-22	2026-06-22 00:00:00+00:00	False	False	47.73	False	https://bitnomial.com/exchange/api/web/charts/voi	https://bitnomial.com/exchange/api/v1/funding-rates/	https://bitnomial.com/exchange/docs/api/rest/funding-rates/	2026-07-19T16:19:36.904854+00:00
bitnomial-1759-SOUH-future-2026-06-22	Bitnomial	1759	SOUH	Future	2026-06-22	2026-06-22 00:00:00+00:00	False	False	47.73	False	https://bitnomial.com/exchange/api/web/charts/voi	https://bitnomial.com/exchange/api/v1/funding-rates/	https://bitnomial.com/exchange/docs/api/rest/funding-rates/	2026-07-19T16:19:36.904854+00:00

Access Requirements (Paid Dataset)

This dataset is behind manual gated access.

To obtain access:

Full dataset access:
https://thearticulated.gumroad.com/l/lpbgqx
Provide your Hugging Face username at checkout.
Return to this Hugging Face page and click:
"Request Access"
Your access will be approved within 1-12 hours.

Once approved, you can use the Python snippet at the bottom of this README to load the dataset.

Dataset Structure

Total rows: 10,428

Total columns: 39

Splits

data: 10,428 rows

Data Files

data: data/data.parquet

Data Dictionary

The table below describes the columns included in this dataset.

column	pandas_dtype	dataset_type	description
record_id	str	string	Text column. Stable row identifier built from source, product ID, base symbol, product type, and observation date.
source_platform	str	string	Text column. Source exchange or venue name.
product_id	int64	integer	Whole-number numeric column. Bitnomial product identifier returned by the public chart endpoint.
base_symbol	str	string	Text column. Bitnomial base symbol for the derivative product.
product_type	str	string	Text column. Derivative product type, such as Future, Option, or Perpetual.
observation_date	str	datetime-like string	Stored as text, but values appear to represent dates or timestamps. UTC date for the product-day volume and open-interest observation.
observation_timestamp_utc	datetime64[us, UTC]	datetime	Date or timestamp column. UTC timestamp at the start of observation_date.
volume_contracts	int64	integer	Whole-number numeric column. Daily trading volume in contracts.
notional_volume_usd	int64	integer	Whole-number numeric column. Daily notional trading volume in USD as returned by Bitnomial.
open_interest_contracts	int64	integer	Whole-number numeric column. End-of-day open interest in contracts.
notional_open_interest_usd	int64	integer	Whole-number numeric column. End-of-day notional open interest in USD as returned by Bitnomial.
has_volume	bool	boolean	True/false column. Boolean flag indicating whether daily volume is above zero.
has_open_interest	bool	boolean	True/false column. Boolean flag indicating whether open interest is above zero.
liquidity_activity_score	float64	float	Decimal numeric column. Deterministic 0-100 score combining normalized notional volume and notional open interest within this build.
volume_to_open_interest_ratio	float64	float	Decimal numeric column. Daily contracts traded divided by open interest contracts when open interest is positive.
notional_volume_to_oi_ratio	float64	float	Decimal numeric column. Daily notional volume divided by notional open interest when notional open interest is positive.
rolling_7d_notional_volume_usd	float64	float	Decimal numeric column. Seven-observation rolling sum of notional volume by product.
rolling_7d_avg_open_interest_contracts	float64	float	Decimal numeric column. Seven-observation rolling average of open interest by product.
rolling_7d_active_days	float64	float	Decimal numeric column. Seven-observation rolling count of days with positive volume by product.
open_interest_1d_change_contracts	float64	float	Decimal numeric column. One-observation change in open interest by product.
open_interest_1d_change_pct	object	float-like string	Stored as text, but values appear to represent decimal numbers. Percent change in open interest by product when the prior value is positive.
funding_interval_count	int64	integer	Whole-number numeric column. Number of finalized 8-hour funding intervals joined to this product-day for perpetual products.
funding_rate_mean	float64	float	Decimal numeric column. Mean finalized funding rate for intervals ending on observation_date.
funding_rate_min	float64	float	Decimal numeric column. Minimum finalized funding rate for intervals ending on observation_date.
funding_rate_max	float64	float	Decimal numeric column. Maximum finalized funding rate for intervals ending on observation_date.
funding_rate_abs_mean	float64	float	Decimal numeric column. Mean absolute finalized funding rate for intervals ending on observation_date.
funding_rate_mean_annualized	float64	float	Decimal numeric column. Mean funding rate multiplied by 3 intervals per day and 365 days.
mark_price_mean	float64	float	Decimal numeric column. Mean mark price across finalized funding intervals ending on observation_date.
price_index_mean	float64	float	Decimal numeric column. Mean price index across finalized funding intervals ending on observation_date.
mark_to_index_basis_bps_mean	float64	float	Decimal numeric column. Mean mark-price premium or discount versus the price index in basis points.
interest_rate_mean	float64	float	Decimal numeric column. Mean interest-rate component across finalized funding intervals ending on observation_date.
estimated_current_funding_rate	float64	float	Decimal numeric column. Estimated current in-progress funding rate for the base symbol at collection time when available.
estimated_interval_start	str	datetime-like string	Stored as text, but values appear to represent dates or timestamps. Start timestamp for the current estimated funding interval when available.
estimated_interval_end	str	datetime-like string	Stored as text, but values appear to represent dates or timestamps. End timestamp for the current estimated funding interval when available.
is_perpetual_product	bool	boolean	True/false column. Boolean flag indicating product_type equals Perpetual.
source_voi_url	str	string	Text column. Public Bitnomial VOI chart endpoint used for the row.
source_funding_url	str	string	Text column. Public Bitnomial funding-rate endpoint used for perpetual funding enrichment.
source_docs_url	str	string	Text column. Bitnomial funding-rate documentation URL.
last_collected_at	str	datetime-like string	Stored as text, but values appear to represent dates or timestamps. UTC timestamp when this row was generated.

Intended Use

This dataset is intended for research, experimentation, analysis, and model prototyping.

Loading the Dataset


import os
from datasets import load_dataset

HUGGINGFACE_API_KEY_KARMANE = os.environ.get("HUGGINGFACE_API_KEY_KARMANE")

dataset = load_dataset(
    "Karmane/bitnomial-crypto-derivatives-liquidity-funding",
    token=HUGGINGFACE_API_KEY_KARMANE,
)

print(dataset)
print(dataset[list(dataset.keys())[0]][0])

# getting the DataFrame itself
# df = dataset[list(dataset.keys())[0]].to_pandas()

Karmane. (2025). Bitnomial Crypto Derivatives Liquidity and Funding Dataset. Hugging Face. https://huggingface.co/datasets/Karmane/bitnomial-crypto-derivatives-liquidity-funding

Downloads last month: 13

Total file size:

197 kB