OPBT SDK · 03A

Complete data catalog

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03A

Complete data catalog

Data estate and decision boundaries#

OPR has two deliberately separate read planes: native market observations and 34 versioned opt-in datasets. This chapter is the inventory and usage contract; current entity coverage remains machine-discovered rather than frozen into prose.

Plane	Surfaces	Availability	Boundary
Completed market bars	history · history_many · klines	Factor · Backtest · Paper · Live	Sparse completed UTC bars; never padded.
Settled funding	funding_history · funding_history_many	Factor · Backtest · Paper · Live	Verified settlements; not estimated accrual.
Top of book	bbo · orderbook	Backtest · Paper · Live	BAR_PROXY in history; current OnePort BBO in Paper/Live.
ADL observations	adl_history	Factor/Backtest historical archive only	Coverage-limited private-account observations; no public row sample.
Versioned datasets	data.history · history_many · latest · status	Factor · Backtest · Paper · Live	Frozen declaration plus dataset-specific visibility index.

Discover definitions and live coverage#

Start with the catalog. A definition answers “what does this field mean?”; an entity row answers “what is currently admitted and observed?”. Never infer an address, native symbol or source route from a display name.

MCP · data:read

data.dataset.catalog({
    "dataset_id": "crypto.derivatives.open_interest_5m",
    "entity": "BTC",
    "limit": 50,
    "offset": 0,
})

data.dataset.coverage_check({
    "dataset_id": "crypto.derivatives.open_interest_5m",
    "entities": ["binance:UPERP:BTCUSDT"],
    "start": "2025-01-01T00:00:00Z",
    "end": "2026-01-01T00:00:00Z",
    "lookback_days": 90,
})

# Resolve display symbols to stable cmc:<id> identities before freezing code.
data.dataset.catalog({
    "dataset_id": "crypto.market.cmc.asset_metrics_1d",
    "entity": "BTC",
    "limit": 50,
})

REST · Authorization: Bearer …

GET /api/data/datasets?entity=BTC&limit=50&offset=0
GET /api/data/datasets/crypto.derivatives.open_interest_5m?entity=BTC
POST /api/data/datasets/coverage-check
{
  "dataset_id": "crypto.derivatives.open_interest_5m",
  "entities": ["binance:UPERP:BTCUSDT"],
  "start": "2025-01-01T00:00:00Z",
  "end": "2026-01-01T00:00:00Z",
  "lookback_days": 90
}

Native market data: exact calls and samples#

Native bars and funding use instrument identities from data.catalog. Full method limits are in the Data API reference. The snapshot below used the production completed-bar feed for binance:UPERP:BTCUSDT.

factor.py / strategy.py

bars = ctx.history(
    "binance:UPERP:BTCUSDT",
    fields=["open", "high", "low", "close", "volume", "turnover"],
    window=3,
    freq="1h",
)
funding = ctx.funding_history("binance:UPERP:BTCUSDT", window=3)

# Factor code uses the same read-only names on factor_ctx.
# Strategy-only BBO: quote = ctx.bbo("binance:UPERP:BTCUSDT")

Production bar DataFrame

# pandas.DataFrame · index=ts (UTC)
# dtypes: open=float64, high=float64, low=float64, close=float64, volume=float64, turnover=float64
ts                    open     high     low      close    volume             turnover
2026-09-03T01:00:00Z  77025.1  77516    76927.3  77321    6257.057000000002  483606462.1462
2026-09-03T02:00:00Z  77321    77887.9  77200.1  77783.4  7578.569999999998  588583715.4183999
2026-09-03T03:00:00Z  77783.3  77850    77596.3  77665.3  4169.969           324087836.20470005

Production funding DataFrame

# pandas.DataFrame · index=settle_time (UTC)
# dtypes: rate=float64, interval_hours=float64
settle_time                  rate        interval_hours
2026-09-02T08:00:00.001000Z  0.00007998  8.000000277777778
2026-09-02T16:00:00.005000Z  0.00003819  8.00000111111111
2026-09-03T00:00:00Z         0.00007287  7.999998611111111

One frozen declaration, four read workloads#

Operation	Return	Invariant
history(alias, entity, fields, window)	One UTC-indexed DataFrame	May only narrow the frozen entity and field declaration.
history_many(alias, entities, fields, window)	dict[str, DataFrame]	Independent bounded window per declared entity.
latest(alias, entity, fields)	Zero- or one-row DataFrame	Uses the identical decision boundary as history.
status(alias)	dict	Frozen identity, fields and time semantics; no source rows.

Factor Evaluation uses factor_ctx.data. Backtest, Paper and Live Strategies use ctx.data. Dataset declarations are read-only inputs and never create orders, position rules, retries, fallbacks or account-risk decisions.

Crypto market structure & derivatives#

CoinMarketCap daily crypto asset metrics#

Selected-asset price, rank, market cap, supply and volume keyed by stable CMC id; symbols and names remain dated observations.

ID · VERSIONcrypto.market.cmc.asset_metrics_1d@1

FREQUENCY · SHAPE1d · SERIES

TIME INDEXpublication_floor_or_observed_at

HISTORY · PITCORRECTED_SNAPSHOT · ASSUMED_PUBLICATION_LAG

Entity format · stable CoinMarketCap id, for example cmc:1 or cmc:1027
Observed sample entity · cmc:1

CAPTURED · 2026-09-08T09:33:33Z

factor.py / strategy.py

__data__ = {
    "lookback_days": 365,
    "datasets": {
        "cmc_assets": {
            "id": "crypto.market.cmc.asset_metrics_1d",
            "schema_version": 1,
            "entities": ["cmc:1"],
            "fields": ["metric_date","provider_symbol","cmc_rank","market_cap_usd","circulating_supply","quality_flags"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "cmc_assets", "cmc:1", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "cmc_assets", "cmc:1", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: metric_date=object, provider_symbol=str, cmc_rank=int64, market_cap_usd=float64, circulating_supply=float64, quality_flags=object
time                      metric_date  provider_symbol  cmc_rank  market_cap_usd      circulating_supply  quality_flags
2026-08-30T00:35:00.000Z  2026-08-29   BTC              1         1570941868391.3594  20077009            []
2026-08-31T00:35:00.000Z  2026-08-30   BTC              1         1559372102010.7974  20077518            []
2026-09-01T00:35:00.000Z  2026-08-31   BTC              1         1577098928677.762   20077992.999999996  []

Complete field contract · 12 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
metric_date^SAMPLE	Date	NO	—	UTC source date represented by the CMC snapshot.
provider_symbol^SAMPLE	String	NO	—	CoinMarketCap symbol captured for this stable CMC asset id on metric_date.
name	String	NO	—	CoinMarketCap asset name captured on metric_date.
slug	String	NO	—	CoinMarketCap asset slug captured on metric_date.
cmc_rank^SAMPLE	UInt32	NO	rank	Provider market-cap rank within the source-qualified daily top-1000 range.
price_usd	Float64	YES	USD/token	Provider USD reference price.
market_cap_usd^SAMPLE	Float64	YES	USD	Provider reported circulating market capitalization.
circulating_supply^SAMPLE	Float64	YES	tokens	Provider reported circulating token supply.
total_supply	Float64	YES	tokens	Provider reported total token supply.
max_supply	Float64	YES	tokens	Provider reported maximum token supply.
volume_24h_usd	Float64	YES	USD/24h	Provider reported trailing 24-hour USD volume.
quality_flags^SAMPLE	Array[String]	NO	—	Source quality evidence; an empty array means no issue was reported.

Contract notes

Backfilled rows use the provider's documented next-day publication floor of 00:35 UTC.
Incremental rows become visible at the later of that floor and the source observed_at time.
Only source-qualified official ranks in the daily top-1000 range are exposed; missing provider ranks are not renumbered.
CMC id is the stable identity; symbols, names and slugs are observations, not execution aliases.
Nullable values and quality flags are preserved without filling, interpolation or recomputation.
Historical rows are corrected snapshots without reconstructed revision vintages.

CoinMarketCap daily crypto market cross-section#

One bounded daily top-1000 snapshot with arrays aligned by stable CMC id for T-1 through T-N cross-sectional research.

ID · VERSIONcrypto.market.cmc.cross_section_1d@1

FREQUENCY · SHAPE1d · SERIES

TIME INDEXpublication_floor_or_observed_at

HISTORY · PITCORRECTED_SNAPSHOT · ASSUMED_PUBLICATION_LAG

Entity format · the singleton top1000 cross-section
Observed sample entity · top1000

CAPTURED · 2026-09-08T09:33:33Z

factor.py / strategy.py

__data__ = {
    "lookback_days": 365,
    "datasets": {
        "cmc_cross": {
            "id": "crypto.market.cmc.cross_section_1d",
            "schema_version": 1,
            "entities": ["top1000"],
            "fields": ["metric_date","component_count"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "cmc_cross", "top1000", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "cmc_cross", "top1000", window=3,
)

T-1…T-N market-cap alignment

__data__ = {"lookback_days": 365, "datasets": {"cmc_cross": {
    "id": "crypto.market.cmc.cross_section_1d", "schema_version": 1,
    "entities": ["top1000"],
    "fields": ["metric_date", "component_count", "cmc_ids",
               "provider_symbols", "cmc_ranks", "market_cap_usd"],
}}}

# At decision time T this contains only snapshots whose available_at < T.
snapshots = ctx.data.history("cmc_cross", "top1000", window=31)
old, new = snapshots.iloc[0], snapshots.iloc[-1]
old_cap = dict(zip(map(int, old["cmc_ids"]), old["market_cap_usd"]))
new_cap = dict(zip(map(int, new["cmc_ids"]), new["market_cap_usd"]))
valid = lambda value: value is not None and value == value and value > 0
delta = {cmc_id: new_cap[cmc_id] / old_cap[cmc_id] - 1.0
         for cmc_id in old_cap.keys() & new_cap.keys()
         if valid(old_cap[cmc_id]) and valid(new_cap[cmc_id])}

# Use cmc_id across dates. Never align by array position or symbol.

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: metric_date=object, component_count=int64
time                      metric_date  component_count
2026-08-30T00:35:00.000Z  2026-08-29   1000
2026-08-31T00:35:00.000Z  2026-08-30   1000
2026-09-01T00:35:00.000Z  2026-08-31   1000

Complete field contract · 14 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
metric_date^SAMPLE	Date	NO	—	UTC source date represented by the CMC snapshot.
component_count^SAMPLE	UInt16	NO	assets	Number of aligned assets in this daily snapshot.
cmc_ids	Array[UInt32]	NO	—	Stable CMC ids aligned element-for-element with every other array field.
provider_symbols	Array[String]	NO	—	Point-in-time provider symbols aligned by CMC id.
names	Array[String]	NO	—	Point-in-time provider asset names aligned by CMC id.
slugs	Array[String]	NO	—	Point-in-time provider slugs aligned by CMC id.
cmc_ranks	Array[UInt32]	NO	rank	Official provider ranks; gaps remain gaps and are not renumbered.
price_usd	Array[Nullable[Float64]]	YES	USD/token	Aligned provider USD reference prices; individual elements may be null.
market_cap_usd	Array[Nullable[Float64]]	YES	USD	Aligned provider circulating market capitalizations; elements may be null.
circulating_supply	Array[Nullable[Float64]]	YES	tokens	Aligned provider circulating supplies; individual elements may be null.
total_supply	Array[Nullable[Float64]]	YES	tokens	Aligned provider total supplies; individual elements may be null.
max_supply	Array[Nullable[Float64]]	YES	tokens	Aligned provider maximum supplies; individual elements may be null.
volume_24h_usd	Array[Nullable[Float64]]	YES	USD/24h	Aligned provider trailing 24-hour USD volumes; elements may be null.
quality_flags	Array[Array[String]]	NO	—	Aligned per-asset source quality evidence arrays.

Contract notes

Every row contains aligned arrays ordered by official rank and then stable CMC id.
Backfilled rows use the next-day 00:35 UTC publication floor; incremental rows use the later of that floor and the last observed member row.
The source-qualified daily top-1000 can contain fewer members and rank gaps; missing members are never converted to zero.
Use this dataset for multi-day cross-sectional deltas and asset_metrics_1d for selected per-asset histories.
Symbols, names and slugs are observations; CMC ids are the stable join keys.
Historical rows are corrected snapshots without reconstructed revision vintages.

Korean spot premium against a USD reference#

Asynchronous KRW spot, USD reference and FX observations combined into a relative-value fact—not an executable arbitrage return.

ID · VERSIONcrypto.market.korea_spot_premium_15m@1

FREQUENCY · SHAPE15m · SERIES

TIME INDEXperiod_end

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · lowercase source route key:uppercase asset, for example upbit_coinbase_eodhd:BTC
Observed sample entity · upbit_coinbase_eodhd:BTC

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "korea_premium": {
            "id": "crypto.market.korea_spot_premium_15m",
            "schema_version": 1,
            "entities": ["upbit_coinbase_eodhd:BTC"],
            "fields": ["premium_rate","korean_price_krw","reference_price_usd","usd_krw","sample_type"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "korea_premium", "upbit_coinbase_eodhd:BTC", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "korea_premium", "upbit_coinbase_eodhd:BTC", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: premium_rate=float64, korean_price_krw=float64, reference_price_usd=float64, usd_krw=float64, sample_type=str
time                  premium_rate          korean_price_krw  reference_price_usd  usd_krw  sample_type
2026-09-03T04:15:00Z  0.013036192933288726  106850000         77674.52             1357.91  live_ticker
2026-09-03T04:30:00Z  0.01387716810551809   106753000         77529.93             1358.08  live_ticker
2026-09-03T04:45:00Z  0.014111282178119522  106762000         77565.4              1357.26  live_ticker

Complete field contract · 8 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
premium_rate^SAMPLE	Float64	NO	ratio	Korean spot premium: korean_price_krw / (reference_price_usd * usd_krw) - 1.
korean_price_krw^SAMPLE	Float64	NO	KRW/base asset	Korean-exchange spot price in KRW for one base-asset unit.
reference_price_usd^SAMPLE	Float64	NO	USD/base asset	Reference-exchange spot price in USD for one base-asset unit.
usd_krw^SAMPLE	Float64	NO	KRW/USD	KRW value of one USD used in the premium calculation.
korean_price_at	DateTime64(3)	NO	UTC	Producer timestamp of the Korean spot-price observation.
reference_price_at	DateTime64(3)	NO	UTC	Producer timestamp of the USD reference-price observation.
fx_at	DateTime64(3)	NO	UTC	Producer timestamp of the USD/KRW observation.
sample_type^SAMPLE	String	NO	—	Producer observation-method and provenance label.

Contract notes

A row is indexed at period_end and is visible only when period_end < T.
Producer is_final is neither required nor used by the public contract.
premium_rate equals korean_price_krw / (reference_price_usd * usd_krw) - 1.
The three prices are asynchronous observations, not an executable arbitrage return.
Missing periods are not filled or interpolated; historical values are corrected snapshots.

Perpetual open interest in base-asset quantity#

Admitted perpetual open interest normalized to base-asset quantity; contract-count venues are deliberately excluded.

ID · VERSIONcrypto.derivatives.open_interest_5m@1

FREQUENCY · SHAPE5m · SERIES

TIME INDEXobservation_time

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · admitted lowercase venue:UPERP:native-symbol
Observed sample entity · binance:UPERP:BTCUSDT

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "open_interest": {
            "id": "crypto.derivatives.open_interest_5m",
            "schema_version": 1,
            "entities": ["binance:UPERP:BTCUSDT"],
            "fields": ["oi_base_qty"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "open_interest", "binance:UPERP:BTCUSDT", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "open_interest", "binance:UPERP:BTCUSDT", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: oi_base_qty=float64
time                  oi_base_qty
2026-09-03T04:35:30Z  107668.061
2026-09-03T04:40:30Z  107663.287
2026-09-03T04:45:30Z  107680.403

Complete field contract · 1 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
oi_base_qty^SAMPLE	Float64	NO	base asset	Venue open interest normalized to the instrument base-asset quantity.

Contract notes

oi_base_qty is the venue position_size expressed in base-asset units.
Gate and KuCoin are not admitted because their raw values are contract counts.
Deribit UPERP uses <ASSET>_USDC-PERPETUAL; <ASSET>-PERPETUAL is CPERP.
At decision time T, observations with observation_time < T are visible.
Missing observations are not filled or converted to zero.

Completed-minute perpetual liquidation flow#

Sparse completed event minutes split into forced short-closing buys and forced long-closing sells.

ID · VERSIONcrypto.derivatives.liquidation_flow_1m@1

FREQUENCY · SHAPE1m · SERIES

TIME INDEXminute_close

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · binance|bybit|okx|kraken_futures:UPERP:native-symbol
Observed sample entity · binance:UPERP:BTCUSDT

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "liquidations": {
            "id": "crypto.derivatives.liquidation_flow_1m",
            "schema_version": 1,
            "entities": ["binance:UPERP:BTCUSDT"],
            "fields": ["short_liquidation_count","short_liquidation_quote_notional","long_liquidation_count","long_liquidation_quote_notional"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "liquidations", "binance:UPERP:BTCUSDT", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "liquidations", "binance:UPERP:BTCUSDT", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: short_liquidation_count=int64, short_liquidation_quote_notional=float64, long_liquidation_count=int64, long_liquidation_quote_notional=float64
time                  short_liquidation_count  short_liquidation_quote_notional  long_liquidation_count  long_liquidation_quote_notional
2026-09-03T04:22:00Z  0                        0                                 4                       4030.68
2026-09-03T04:23:00Z  1                        47147.54                          0                       0
2026-09-03T04:28:00Z  0                        0                                 1                       853.06

Complete field contract · 6 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
short_liquidation_count^SAMPLE	UInt64	NO	events/minute	Forced-buy events in the completed minute; each closes a short position.
short_liquidation_base_qty	Float64	NO	base asset/minute	Base-asset quantity forced bought to close short positions.
short_liquidation_quote_notional^SAMPLE	Float64	NO	venue quote asset/minute	Quote notional forced bought to close short positions.
long_liquidation_count^SAMPLE	UInt64	NO	events/minute	Forced-sell events in the completed minute; each closes a long position.
long_liquidation_base_qty	Float64	NO	base asset/minute	Base-asset quantity forced sold to close long positions.
long_liquidation_quote_notional^SAMPLE	Float64	NO	venue quote asset/minute	Quote notional forced sold to close long positions.

Contract notes

Only Binance, Bybit, OKX and Kraken Futures UPERP facts are admitted.
Stored Buy means a forced buy closing a short; stored Sell closes a long.
Rows aggregate completed event minutes and are visible only when minute_close < T.
Natural no-event minutes stay absent; OPR does not fabricate zero rows.
Historical values are corrected snapshots; ingestion time is not presented as event time.

Binance USD-M perpetual market sentiment#

Top-trader, global-account and taker ratios with an explicit metric mask for partial source coverage.

ID · VERSIONcrypto.derivatives.binance.futures_sentiment_5m@1

FREQUENCY · SHAPE5m · SERIES

TIME INDEXperiod_end

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · binance:UPERP:native-symbol
Observed sample entity · binance:UPERP:BTCUSDT

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "binance_sentiment": {
            "id": "crypto.derivatives.binance.futures_sentiment_5m",
            "schema_version": 1,
            "entities": ["binance:UPERP:BTCUSDT"],
            "fields": ["top_trader_account_long_short_ratio","global_account_long_short_ratio","taker_buy_sell_ratio","metric_mask","is_complete"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "binance_sentiment", "binance:UPERP:BTCUSDT", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "binance_sentiment", "binance:UPERP:BTCUSDT", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: top_trader_account_long_short_ratio=float64, global_account_long_short_ratio=float64, taker_buy_sell_ratio=float64, metric_mask=int64, is_complete=bool
time                  top_trader_account_long_short_ratio  global_account_long_short_ratio  taker_buy_sell_ratio  metric_mask  is_complete
2026-09-03T04:25:00Z  1.263                                1.2002                           2.0101                15           True
2026-09-03T04:30:00Z  1.2619                               1.1993                           0.808                 15           True
2026-09-03T04:35:00Z  1.2619                               1.1983                           2.3312                15           True

Complete field contract · 14 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
top_trader_account_long_ratio	Float64	YES	ratio	Share of sampled top-trader accounts reported long by Binance.
top_trader_account_short_ratio	Float64	YES	ratio	Share of sampled top-trader accounts reported short by Binance.
top_trader_account_long_short_ratio^SAMPLE	Float64	YES	long/short	Binance top-trader account long-to-short ratio.
top_trader_position_long_ratio	Float64	YES	ratio	Long share of sampled top-trader position exposure.
top_trader_position_short_ratio	Float64	YES	ratio	Short share of sampled top-trader position exposure.
top_trader_position_long_short_ratio	Float64	YES	long/short	Binance top-trader position long-to-short ratio.
global_account_long_ratio	Float64	YES	ratio	Long-account share across the Binance global account sample.
global_account_short_ratio	Float64	YES	ratio	Short-account share across the Binance global account sample.
global_account_long_short_ratio^SAMPLE	Float64	YES	long/short	Binance global-account long-to-short ratio.
taker_buy_volume	Float64	YES	base asset/5m	Binance USD-M perpetual taker-buy volume for the completed period.
taker_sell_volume	Float64	YES	base asset/5m	Binance USD-M perpetual taker-sell volume for the completed period.
taker_buy_sell_ratio^SAMPLE	Float64	YES	buy/sell	Binance taker-buy volume divided by taker-sell volume.
metric_mask^SAMPLE	UInt8	NO	bit mask	Producer presence mask for the four metric families in this row.
is_complete^SAMPLE	Bool	NO	—	Whether all four metric families are present; partial rows remain usable.

Contract notes

Only Binance USD-M PERPETUAL rows are admitted.
A row is indexed at its completed period_end and is visible only when period_end < T.
Partial historical rows remain visible; metric_mask and is_complete disclose coverage.
Missing metrics and periods remain absent or null; OPR never fills them with zero.
Historical values are corrected snapshots.

Deribit DVOL completed hourly index bars#

Completed hourly DVOL index bars in index points; not realized volatility and not an executable instrument price.

ID · VERSIONcrypto.derivatives.deribit.dvol_1h@1

FREQUENCY · SHAPE1h · SERIES

TIME INDEXhour_end

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · uppercase Deribit DVOL asset, for example BTC or ETH
Observed sample entity · BTC

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "dvol": {
            "id": "crypto.derivatives.deribit.dvol_1h",
            "schema_version": 1,
            "entities": ["BTC"],
            "fields": ["open","high","low","close","is_final"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "dvol", "BTC", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "dvol", "BTC", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: open=float64, high=float64, low=float64, close=float64, is_final=bool
time                  open   high   low    close  is_final
2026-09-03T02:00:00Z  37.15  37.16  37.03  37.08  True
2026-09-03T03:00:00Z  37.08  37.09  36.71  36.8   True
2026-09-03T04:00:00Z  36.8   36.8   36.66  36.75  True

Complete field contract · 7 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
hour_start	DateTime	NO	UTC	Start of the completed DVOL hour.
hour_end	DateTime	NO	UTC	End of the completed DVOL hour.
open^SAMPLE	Float64	NO	volatility index points	DVOL at hour open.
high^SAMPLE	Float64	NO	volatility index points	Highest DVOL in the hour.
low^SAMPLE	Float64	NO	volatility index points	Lowest DVOL in the hour.
close^SAMPLE	Float64	NO	volatility index points	DVOL at hour close.
is_final^SAMPLE	Bool	NO	—	Whether the producer finalized the hour; exposed rows are final.

Contract notes

Only producer-finalized hours are exposed.
At decision time T, observations with hour_end < T are visible.
DVOL values are index points, not decimal realized volatility or an executable instrument price.
Historical rows are corrected snapshots.

Deribit option market regime and positioning#

Deribit option quality, term structure, put/call positioning and premium turnover under one frozen methodology.

ID · VERSIONcrypto.derivatives.deribit.option_regime_5m@1

FREQUENCY · SHAPE5m · SERIES

TIME INDEXsnapshot_bucket_end

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · uppercase Deribit option underlying, for example BTC or ETH
Observed sample entity · BTC

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "option_regime": {
            "id": "crypto.derivatives.deribit.option_regime_5m",
            "schema_version": 1,
            "entities": ["BTC"],
            "fields": ["index_price","surface_node_coverage_ratio","atm_term_slope_7d_30d_pct","put_call_open_interest_ratio","total_volume_24h_usd","source_provider"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "option_regime", "BTC", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "option_regime", "BTC", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: index_price=float64, surface_node_coverage_ratio=float64, atm_term_slope_7d_30d_pct=float64, put_call_open_interest_ratio=float64, total_volume_24h_usd=float64, source_provider=str
time                  index_price  surface_node_coverage_ratio  atm_term_slope_7d_30d_pct  put_call_open_interest_ratio  total_volume_24h_usd  source_provider
2026-09-03T10:00:00Z  77601.49     1                            4.783075165847823          0.5509616927626599            16255770.619999995    DERIBIT_PUBLIC_API_INVERSE_OPTIONS
2026-09-03T10:05:00Z  77610.13     1                            4.78207590276315           0.5509699268500942            16271471.68           DERIBIT_PUBLIC_API_INVERSE_OPTIONS
2026-09-03T10:10:00Z  77605.02     1                            4.753924861292578          0.5509359406997202            16249928.08           DERIBIT_PUBLIC_API_INVERSE_OPTIONS

Complete field contract · 32 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
index_price^SAMPLE	Float64	NO	USD/base asset	Deribit underlying index price used to construct this snapshot.
source_option_count	UInt32	NO	option instruments	Option instruments present in the source snapshot before validation.
valid_option_count	UInt32	NO	option instruments	Source option instruments admitted to the aggregate calculations.
invalid_option_count	UInt32	NO	option instruments	Source option instruments rejected by producer validation.
source_expiry_count	UInt16	NO	expiries	Distinct source expiries observed.
surface_expiry_count	UInt16	NO	expiries	Expiries with enough valid nodes for surface construction.
two_sided_quote_count	UInt32	NO	option instruments	Valid option instruments with a two-sided quote.
mark_iv_coverage_ratio	Float64	NO	ratio	Share of source options with mark IV.
required_field_coverage_ratio	Float64	NO	ratio	Share of source options containing every producer-required field.
two_sided_quote_ratio	Float64	NO	ratio	Share of valid options with a two-sided quote.
surface_node_count	UInt8	NO	nodes	Available canonical delta/tenor volatility surface nodes.
surface_node_coverage_ratio^SAMPLE	Float64	NO	ratio	Available nodes divided by the 30-node canonical surface grid.
atm_term_slope_7d_30d_pct^SAMPLE	Float64	YES	volatility percentage points	30-day ATM IV minus 7-day ATM IV.
atm_term_slope_30d_90d_pct	Float64	YES	volatility percentage points	90-day ATM IV minus 30-day ATM IV.
atm_term_slope_90d_180d_pct	Float64	YES	volatility percentage points	180-day ATM IV minus 90-day ATM IV.
front_iv_ratio_7d_30d	Float64	YES	ratio	7-day ATM IV divided by 30-day ATM IV.
call_open_interest_base	Float64	NO	base asset	Call open interest in underlying coin.
put_open_interest_base	Float64	NO	base asset	Put open interest in underlying coin.
total_open_interest_base	Float64	NO	base asset	Total option open interest in underlying coin.
put_call_open_interest_ratio^SAMPLE	Float64	YES	put/call	Put open interest divided by call open interest.
call_volume_24h_base	Float64	NO	base asset/24h	Rolling call volume in underlying coin.
put_volume_24h_base	Float64	NO	base asset/24h	Rolling put volume in underlying coin.
total_volume_24h_base	Float64	NO	base asset/24h	Rolling total option volume in underlying coin.
put_call_volume_24h_base_ratio	Float64	YES	put/call	Put base volume divided by call base volume.
call_volume_24h_usd	Float64	NO	USD premium turnover/24h	Rolling call option premium turnover in USD.
put_volume_24h_usd	Float64	NO	USD premium turnover/24h	Rolling put option premium turnover in USD.
total_volume_24h_usd^SAMPLE	Float64	NO	USD premium turnover/24h	Rolling total option premium turnover in USD.
put_call_volume_24h_usd_ratio	Float64	YES	put/call	Put USD premium turnover divided by call USD premium turnover.
open_interest_weighted_iv_pct	Float64	YES	volatility percentage points	Mark IV weighted by option open interest.
volume_weighted_iv_pct	Float64	YES	volatility percentage points	Mark IV weighted by rolling option volume.
source_provider^SAMPLE	String	NO	—	Producer source label; historical replay and live API rows intentionally share one series.
methodology_version	String	NO	—	Frozen producer surface methodology version.

Contract notes

Each row is indexed at snapshotBucket plus five minutes; only snapshot_bucket_end < T is visible.
sourceTimestamp is the earliest contributing quote time and collectedAt is ingestion time; neither is the public index.
Base open interest and volume are denominated in the underlying coin.
USD volume fields are option premium turnover, not underlying notional.
Historical replay and live API rows share one corrected series under the frozen methodology version.
The dataset is a read-only Factor/Strategy input and never implies an option execution route.

Deribit interpolated option volatility surface#

Interpolated option surface arrays normalized into scalar time series for each underlying and canonical tenor.

ID · VERSIONcrypto.derivatives.deribit.option_surface_5m@1

FREQUENCY · SHAPE5m · SERIES

TIME INDEXsnapshot_bucket_end

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · uppercase underlying/canonical tenor, for example BTC/30D; tenors are 7D, 14D, 30D, 60D, 90D and 180D
Observed sample entity · BTC/30D

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "option_surface": {
            "id": "crypto.derivatives.deribit.option_surface_5m",
            "schema_version": 1,
            "entities": ["BTC/30D"],
            "fields": ["tenor_days","forward_price","atm_iv_pct","rr_25_pct","bf_25_pct","source_provider"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "option_surface", "BTC/30D", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "option_surface", "BTC/30D", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: tenor_days=int64, forward_price=float64, atm_iv_pct=float64, rr_25_pct=float64, bf_25_pct=float64, source_provider=str
time                  tenor_days  forward_price      atm_iv_pct          rr_25_pct             bf_25_pct          source_provider
2026-09-03T10:00:00Z  30          77811.36981574833  34.158303831024924  -0.30469094498031524  1.207483956363916  DERIBIT_PUBLIC_API_INVERSE_OPTIONS
2026-09-03T10:05:00Z  30          77823.99100667167  34.14950873465129   -0.25955344299311633  1.198486618656517  DERIBIT_PUBLIC_API_INVERSE_OPTIONS
2026-09-03T10:10:00Z  30          77816.30531558671  34.13168774039993   -0.30021182597673857  1.208436219425984  DERIBIT_PUBLIC_API_INVERSE_OPTIONS

Complete field contract · 17 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
index_price	Float64	NO	USD/base asset	Deribit underlying index price used to construct this snapshot.
tenor_days^SAMPLE	UInt16	NO	calendar days	Canonical interpolated tenor for this entity.
forward_price^SAMPLE	Float64	YES	USD/base asset	Interpolated option-implied forward price.
put_10_delta_iv_pct	Float64	YES	volatility percentage points	Interpolated 10-delta put IV.
put_25_delta_iv_pct	Float64	YES	volatility percentage points	Interpolated 25-delta put IV.
atm_iv_pct^SAMPLE	Float64	YES	volatility percentage points	Interpolated at-the-money IV.
call_25_delta_iv_pct	Float64	YES	volatility percentage points	Interpolated 25-delta call IV.
call_10_delta_iv_pct	Float64	YES	volatility percentage points	Interpolated 10-delta call IV.
rr_25_pct^SAMPLE	Float64	YES	volatility percentage points	25-delta call IV minus put IV.
bf_25_pct^SAMPLE	Float64	YES	volatility percentage points	25-delta butterfly relative to ATM IV.
rr_10_pct	Float64	YES	volatility percentage points	10-delta call IV minus put IV.
bf_10_pct	Float64	YES	volatility percentage points	10-delta butterfly relative to ATM IV.
put_skew_25_pct	Float64	YES	volatility percentage points	25-delta put IV minus ATM IV.
call_skew_25_pct	Float64	YES	volatility percentage points	25-delta call IV minus ATM IV.
surface_node_coverage_ratio	Float64	NO	ratio	Available nodes divided by the 30-node canonical surface grid.
source_provider^SAMPLE	String	NO	—	Producer source label; historical replay and live API rows intentionally share one series.
methodology_version	String	NO	—	Frozen producer surface methodology version.

Contract notes

Array-valued producer rows are normalized to one scalar series per underlying and canonical tenor.
Each row is indexed at snapshotBucket plus five minutes; only snapshot_bucket_end < T is visible.
IV, risk-reversal, butterfly and skew values are volatility percentage points, not decimal volatility.
Null surface nodes remain null and missing five-minute buckets remain absent.
Historical replay and live API rows share one corrected series under the frozen methodology version.
The dataset is a read-only Factor/Strategy input and never implies an option execution route.

Cross-asset, macro & attention#

US equity end-of-day prices and corporate actions#

US equity daily OHLC, adjusted prices, volume and explicit corporate-action fields at the next UTC-day boundary.

ID · VERSIONmarket.equity.us.eod_1d@1

FREQUENCY · SHAPE1d · SERIES

TIME INDEXnext_utc_day

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · uppercase US equity source symbol, for example AAPL or SPY
Observed sample entity · SPY

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "us_equity": {
            "id": "market.equity.us.eod_1d",
            "schema_version": 1,
            "entities": ["SPY"],
            "fields": ["trade_date","open","high","low","close","adjusted_close","volume","is_adjustment_day"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "us_equity", "SPY", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "us_equity", "SPY", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: trade_date=object, open=float64, high=float64, low=float64, close=float64, adjusted_close=float64, volume=int64, is_adjustment_day=bool
time                  trade_date  open    high     low      close   adjusted_close  volume    is_adjustment_day
2026-09-01T00:00:00Z  2026-08-31  767.33  767.995  764.715  767.05  767.05          38810779  False
2026-09-02T00:00:00Z  2026-09-01  762.01  764.67   759.48   761.78  761.78          41126315  False
2026-09-03T00:00:00Z  2026-09-02  762.45  766.43   761.73   765.16  765.16          28223374  False

Complete field contract · 19 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
trade_date^SAMPLE	Date	NO	—	US trading date represented by the observation.
open^SAMPLE	Float64	YES	listing currency/share	Unadjusted session open.
high^SAMPLE	Float64	YES	listing currency/share	Unadjusted session high.
low^SAMPLE	Float64	YES	listing currency/share	Unadjusted session low.
close^SAMPLE	Float64	YES	listing currency/share	Unadjusted session close.
adjusted_open	Float64	YES	listing currency/share	Producer-adjusted session open under the current corrected snapshot.
adjusted_high	Float64	YES	listing currency/share	Producer-adjusted session high under the current corrected snapshot.
adjusted_low	Float64	YES	listing currency/share	Producer-adjusted session low under the current corrected snapshot.
adjusted_close^SAMPLE	Float64	YES	listing currency/share	Producer-adjusted session close under the current corrected snapshot.
volume^SAMPLE	UInt64	YES	shares/day	Reported session trading volume.
adjustment_factor	Float64	YES	ratio	Producer factor relating the current adjusted and unadjusted series.
is_adjustment_day^SAMPLE	Bool	NO	—	Whether a corporate-action adjustment occurs on trade_date.
adjustment_type	String	NO	—	Producer classification for the day's adjustment.
split_ratio	Float64	YES	new shares/old share	Numeric split ratio when available.
split_ratio_text	String	NO	—	Source split-ratio representation when supplied.
dividend_amount	Float64	YES	dividend currency/share	Producer-adjusted cash dividend amount.
dividend_unadjusted_amount	Float64	YES	dividend currency/share	Unadjusted cash dividend amount reported by the source.
dividend_currency	String	NO	—	Currency of the reported dividend amount.
corporate_action_count	UInt16	NO	events/day	Corporate-action events represented on trade_date.

Contract notes

Each observation describes trade_date and is indexed at 00:00 UTC on the following calendar day.
At decision time T, only observations whose next_utc_day index is strictly before T are visible.
Historical rows are corrected snapshots; exact first-publication and revision timestamps are not claimed.
Missing sessions and nullable source values remain missing; OPR does not fill or interpolate them.
The dataset is a read-only Strategy/Factor input and does not create an execution mapping.

Crypto ETF completed daily fund flows#

Completed fund and provider-total daily flows; nullable fund values remain null rather than becoming zero.

ID · VERSIONcrypto.etf.flow_1d@1

FREQUENCY · SHAPE1d · SERIES

TIME INDEXnext_utc_day

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · uppercase <market>/<asset>/<ticker>, for example US/BTC/__TOTAL__
Observed sample entity · US/BTC/__TOTAL__

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "etf_flow": {
            "id": "crypto.etf.flow_1d",
            "schema_version": 1,
            "entities": ["US/BTC/__TOTAL__"],
            "fields": ["trade_date","is_total","flow_usd","price_usd"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "etf_flow", "US/BTC/__TOTAL__", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "etf_flow", "US/BTC/__TOTAL__", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: trade_date=object, is_total=bool, flow_usd=float64, price_usd=float64
time                  trade_date  is_total  flow_usd    price_usd
2026-09-01T00:00:00Z  2026-08-31  True      216700000   77634.6
2026-09-02T00:00:00Z  2026-09-01  True      -236500000  78549.6
2026-09-03T00:00:00Z  2026-09-02  True      101100000   77400.2

Complete field contract · 4 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
trade_date^SAMPLE	Date	NO	—	US trading date represented by the row.
is_total^SAMPLE	Bool	NO	—	Whether the entity is the provider's aggregate across funds for the asset.
flow_usd^SAMPLE	Float64	YES	USD/day	Completed daily net subscription flow; positive is a net inflow.
price_usd^SAMPLE	Float64	YES	USD/asset	Provider reference asset price; currently supplied only for aggregate entities.

Contract notes

Each observation is indexed at 00:00 UTC on the day after trade_date.
The next-day boundary excludes provisional same-day source rows in every workload.
__TOTAL__ is the provider aggregate; fund entities retain nullable source flows.
Missing and null fund values are not filled, interpolated or converted to zero.
Historical rows are corrected snapshots; intraday revisions are not claimed as replayable.

Crypto ETF completed daily aggregate net assets#

Completed US BTC/ETH ETF aggregate net assets and their daily first difference, kept separate from fund-flow facts.

ID · VERSIONcrypto.etf.net_assets_1d@1

FREQUENCY · SHAPE1d · SERIES

TIME INDEXnext_utc_day

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · uppercase <market>/<asset>/__TOTAL__, for example US/BTC/__TOTAL__
Observed sample entity · US/BTC/__TOTAL__

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "etf_assets": {
            "id": "crypto.etf.net_assets_1d",
            "schema_version": 1,
            "entities": ["US/BTC/__TOTAL__"],
            "fields": ["trade_date","net_assets_usd","net_assets_change_usd","provider_reference_price_usd"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "etf_assets", "US/BTC/__TOTAL__", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "etf_assets", "US/BTC/__TOTAL__", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: trade_date=object, net_assets_usd=float64, net_assets_change_usd=float64, provider_reference_price_usd=float64
time                  trade_date  net_assets_usd  net_assets_change_usd  provider_reference_price_usd
2026-09-01T00:00:00Z  2026-08-31  147196800000    216700000              78549.6
2026-09-02T00:00:00Z  2026-09-01  146960300000    -236500000             77400.1
2026-09-03T00:00:00Z  2026-09-02  147061400000    101100000              77300

Complete field contract · 4 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
trade_date^SAMPLE	Date	NO	—	US trading date represented by the row.
net_assets_usd^SAMPLE	Float64	NO	USD	Provider aggregate net assets across the asset's US ETF products.
net_assets_change_usd^SAMPLE	Float64	YES	USD/day	Daily first difference of aggregate net assets; this is not ETF net flow.
provider_reference_price_usd^SAMPLE	Float64	YES	USD/asset	Provider reference underlying price accompanying the net-assets row; not an execution price.

Contract notes

Each observation is indexed at 00:00 UTC on the day after trade_date.
Entities are provider aggregates across US ETF products, not individual funds.
net_assets_change_usd is the daily first difference of net assets and is not fund flow.
The provider reference price is contextual and is not an executable market price.
Historical rows are corrected snapshots; intraday revisions are not replayable.

CFTC Traders in Financial Futures weekly positions#

Weekly TFF positions indexed at the official publication time, including exceptional release calendars.

ID · VERSIONmacro.cftc.tff_futures_only_1w@1

FREQUENCY · SHAPE1w · SERIES

TIME INDEXofficial_release_at

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · uppercase CFTC contract code, for example 133741
Observed sample entity · 133741

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "cftc_tff": {
            "id": "macro.cftc.tff_futures_only_1w",
            "schema_version": 1,
            "entities": ["133741"],
            "fields": ["report_date","asset","open_interest","asset_manager_long","asset_manager_short","leveraged_money_long","leveraged_money_short"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "cftc_tff", "133741", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "cftc_tff", "133741", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: report_date=object, asset=str, open_interest=int64, asset_manager_long=int64, asset_manager_short=int64, leveraged_money_long=int64, leveraged_money_short=int64
time                  report_date  asset  open_interest  asset_manager_long  asset_manager_short  leveraged_money_long  leveraged_money_short
2026-08-14T19:30:00Z  2026-08-11   BTC    21185          4741                2507                 4997                  12049
2026-08-21T19:30:00Z  2026-08-18   BTC    21760          4531                1799                 4488                  11927
2026-08-28T19:30:00Z  2026-08-25   BTC    22216          4732                1787                 3181                  11270

Complete field contract · 32 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
report_date^SAMPLE	Date	NO	—	CFTC as-of date represented by the weekly report.
asset^SAMPLE	String	YES	—	Optional OPR crypto-asset mapping; null for other contracts.
market_and_exchange_names	String	NO	—	CFTC market and exchange label.
contract_market_name	String	NO	—	CFTC contract market name.
commodity_name	String	NO	—	CFTC commodity name.
commodity_group	String	NO	—	CFTC commodity group.
commodity_subgroup	String	NO	—	CFTC commodity subgroup.
contract_units	String	NO	—	Source description of contract position units.
open_interest^SAMPLE	Int64	YES	contracts	Total reportable market open interest.
dealer_long	Int64	YES	contracts	Dealer/intermediary long positions.
dealer_short	Int64	YES	contracts	Dealer/intermediary short positions.
dealer_spread	Int64	YES	contracts	Dealer/intermediary spread positions.
asset_manager_long^SAMPLE	Int64	YES	contracts	Asset manager/institutional long positions.
asset_manager_short^SAMPLE	Int64	YES	contracts	Asset manager/institutional short positions.
asset_manager_spread	Int64	YES	contracts	Asset manager/institutional spread positions.
leveraged_money_long^SAMPLE	Int64	YES	contracts	Leveraged money long positions.
leveraged_money_short^SAMPLE	Int64	YES	contracts	Leveraged money short positions.
leveraged_money_spread	Int64	YES	contracts	Leveraged money spread positions.
other_reportable_long	Int64	YES	contracts	Other reportable long positions.
other_reportable_short	Int64	YES	contracts	Other reportable short positions.
other_reportable_spread	Int64	YES	contracts	Other reportable spread positions.
total_reportable_long	Int64	YES	contracts	Total reportable long positions.
total_reportable_short	Int64	YES	contracts	Total reportable short positions.
nonreportable_long	Int64	YES	contracts	Non-reportable long positions.
nonreportable_short	Int64	YES	contracts	Non-reportable short positions.
change_open_interest	Int64	YES	contracts/week	Weekly change in open interest.
change_dealer_long	Int64	YES	contracts/week	Weekly change in dealer/intermediary long positions.
change_dealer_short	Int64	YES	contracts/week	Weekly change in dealer/intermediary short positions.
change_asset_manager_long	Int64	YES	contracts/week	Weekly change in asset manager/institutional long positions.
change_asset_manager_short	Int64	YES	contracts/week	Weekly change in asset manager/institutional short positions.
change_leveraged_money_long	Int64	YES	contracts/week	Weekly change in leveraged money long positions.
change_leveraged_money_short	Int64	YES	contracts/week	Weekly change in leveraged money short positions.

Contract notes

The row describes report_date positions but is indexed at the CFTC publication time.
Normal publication is the third federal business day after report_date at 15:30 America/New_York.
Official 2019, 2023 and 2025 disruption/catch-up calendars override the normal rule.
asset is optional catalog metadata; contract_code remains the stable identity.
Historical values are corrected snapshots because source revision history is unavailable.

Wikipedia daily crypto-asset attention#

Daily user pageviews aggregated across registered historical article aliases, visible on the following UTC day.

ID · VERSIONattention.wikipedia.pageviews_1d@1

FREQUENCY · SHAPE1d · SERIES

TIME INDEXnext_utc_day

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · uppercase mapped asset, for example BTC or ETH
Observed sample entity · BTC

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "wiki_attention": {
            "id": "attention.wikipedia.pageviews_1d",
            "schema_version": 1,
            "entities": ["BTC"],
            "fields": ["date","views","article_count"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "wiki_attention", "BTC", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "wiki_attention", "BTC", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: date=object, views=int64, article_count=int64
time                  date        views  article_count
2026-08-31T00:00:00Z  2026-08-30  5126   1
2026-09-01T00:00:00Z  2026-08-31  2130   1
2026-09-02T00:00:00Z  2026-09-01  3759   1

Complete field contract · 3 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
date^SAMPLE	Date	NO	—	UTC date represented by the pageview observation.
views^SAMPLE	UInt64	NO	user pageviews/day	Daily user pageviews summed across all registered historical article aliases.
article_count^SAMPLE	UInt16	NO	articles/day	Distinct registered article aliases contributing on the date.

Contract notes

Each UTC date is indexed at 00:00 UTC on the following day.
views sums all registered historical article aliases for the asset and date.
article_count exposes alias overlap without requiring strategies to handle page renames.
The current UTC day is never exposed and missing days are not filled.
Historical rows are corrected snapshots.

Global sovereign benchmark daily yields#

Final daily sovereign benchmark yield OHLC, exposed after a conservative next-day publication boundary.

ID · VERSIONmacro.sovereign.yield_1d@1

FREQUENCY · SHAPE1d · SERIES

TIME INDEXday_end_plus_12h

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · uppercase <ISO2>_GOVT_<tenor>, for example US_GOVT_10Y
Observed sample entity · US_GOVT_10Y

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "sovereign_yield": {
            "id": "macro.sovereign.yield_1d",
            "schema_version": 1,
            "entities": ["US_GOVT_10Y"],
            "fields": ["trade_date","yield_open_pct","yield_high_pct","yield_low_pct","yield_close_pct","quality_flags"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "sovereign_yield", "US_GOVT_10Y", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "sovereign_yield", "US_GOVT_10Y", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: trade_date=object, yield_open_pct=float64, yield_high_pct=float64, yield_low_pct=float64, yield_close_pct=float64, quality_flags=object
time                  trade_date  yield_open_pct  yield_high_pct  yield_low_pct  yield_close_pct  quality_flags
2026-08-29T12:00:00Z  2026-08-28  4.674           4.733           4.655          4.717            []
2026-09-01T12:00:00Z  2026-08-31  4.717           4.77            4.71           4.756            []
2026-09-02T12:00:00Z  2026-09-01  4.756           4.814           4.748          4.797            []

Complete field contract · 11 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
trade_date^SAMPLE	Date	NO	—	UTC source date represented by the yield row.
country_code	String	NO	—	Normalized ISO 3166-1 alpha-2 country code.
currency	String	NO	—	Currency associated with the sovereign benchmark.
tenor	String	NO	—	Canonical source tenor label, such as 10Y.
tenor_months	UInt16	NO	months	Canonical tenor expressed in months.
yield_open_pct^SAMPLE	Float64	NO	percentage points	Source daily opening yield; 4.25 means 4.25%.
yield_high_pct^SAMPLE	Float64	NO	percentage points	Source daily highest yield; 4.25 means 4.25%.
yield_low_pct^SAMPLE	Float64	NO	percentage points	Source daily lowest yield; 4.25 means 4.25%.
yield_close_pct^SAMPLE	Float64	NO	percentage points	Source daily closing yield; 4.25 means 4.25%.
is_final	Bool	NO	—	Whether the producer finalized the day; exposed rows are final.
quality_flags^SAMPLE	Array[String]	NO	—	Non-destructive producer warnings; flagged observations remain visible.

Contract notes

Only final GBOND sovereign benchmark rows are exposed.
Each trade_date is conservatively indexed at 12:00 UTC on the following calendar day.
Yields are percentage points: 4.25 means 4.25%, and negative yields remain valid.
Quality flags are non-destructive warnings; missing dates and nulls are not filled.
Historical rows are corrected snapshots; ingestion timestamps are not treated as publication times.
The dataset is a read-only Factor/Strategy input and never implies a bond execution route.

US company fundamentals#

US company sector and industry observations#

Provider sector/industry, SIC and Fama labels from retained reference snapshots. Visible only after observation time, not historical classification effective dates.

ID · VERSIONmarket.equity.us.classification_observations@1

FREQUENCY · SHAPEirregular · SERIES

TIME INDEXsource_observed_at

HISTORY · PITCORRECTED_SNAPSHOT · OBSERVATION_TIME_ONLY

Entity format · native source ticker, for example AAPL
Observed sample entity · AAPL

CAPTURED · 2026-09-04T10:14:19.351170Z

factor.py / strategy.py

__data__ = {
    "lookback_days": 730,
    "datasets": {
        "classification": {
            "id": "market.equity.us.classification_observations",
            "schema_version": 1,
            "entities": ["AAPL"],
            "fields": ["sector","industry","siccode","sicsector","sicindustry","famaindustry"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "classification", "AAPL", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "classification", "AAPL", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: sector=str, industry=str, siccode=str, sicsector=str, sicindustry=str, famaindustry=str
time                         sector      industry              siccode  sicsector      sicindustry           famaindustry
2026-09-04T03:36:11.031000Z  Technology  Consumer Electronics  3571     Manufacturing  Electronic Computers  Computers

Complete field contract · 6 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
sector^SAMPLE	String	YES	—	Provider sector label; not asserted to be licensed GICS.
industry^SAMPLE	String	YES	—	Provider industry label, separate from SIC and Fama classifications.
siccode^SAMPLE	String	YES	—	SIC industry code as text; preserve leading zeroes.
sicsector^SAMPLE	String	YES	—	Source SIC sector label.
sicindustry^SAMPLE	String	YES	—	Source SIC industry label.
famaindustry^SAMPLE	String	YES	—	Source Fama industry label; no inferred group number or taxonomy version.

Contract notes

Native source ticker is the data identity; share classes are never silently aliased or routed for execution.
Retain source ticker case, spaces and index prefix (e.g. ^IXIC) exactly; storage uses a reversible encoding.
At decision T only index < T is visible, within the declared rolling lookback_days in all four modes.
Sparse dates and nullable values are preserved; window counts observations, not reporting quarters.
Historical rows are corrected snapshots; source ingestion/version fields do not prove first publication.
Only retained reference snapshots are exposed after their actual source observation time; no historical sector-change or effective-date history is claimed.
lastupdated, firstpricedate and filing dates never backdate a classification. Before the first stored observation the result is empty.
Upstream replacement may move a retained snapshot's observation time; this is corrected snapshot data, not a permanent vintage archive.
Empty source labels become null. Provider sector/industry, SIC and Fama are distinct classifications, not aliases or trading signals.
Catalog entity tags are current reference metadata for discovery, not as-of Strategy/Factor inputs; use this dataset for bounded runtime reads.

US equity as-reported financial statements#

As-reported financial statements indexed after the filing day; ARQ, ARY and ART stay distinct. Restated MR dimensions are not admitted.

ID · VERSIONmarket.equity.us.fundamentals_ar@1

FREQUENCY · SHAPE1d · SERIES

TIME INDEXfiling_day_plus_36h

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · <native-ticker>/<ARQ|ARY|ART>
Observed sample entity · AAPL/ARQ

CAPTURED · 2026-09-04T09:00:57.476716Z

factor.py / strategy.py

__data__ = {
    "lookback_days": 730,
    "datasets": {
        "financials": {
            "id": "market.equity.us.fundamentals_ar",
            "schema_version": 1,
            "entities": ["AAPL/ARQ"],
            "fields": ["filing_date","report_period","revenueusd","epsusd","roe"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "financials", "AAPL/ARQ", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "financials", "AAPL/ARQ", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: filing_date=object, report_period=object, revenueusd=float64, epsusd=float64, roe=object
time                  filing_date  report_period  revenueusd    epsusd  roe
2026-01-31T12:00:00Z  2026-01-30   2025-12-27     143756000000  2.85    null
2026-05-02T12:00:00Z  2026-05-01   2026-03-28     111184000000  2.02    null
2026-08-01T12:00:00Z  2026-07-31   2026-06-27     109417000000  2.03    null

Complete field contract · 109 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
filing_date^SAMPLE	Date	NO	—	SEC filing date, not fiscal period end.
report_period^SAMPLE	Date	NO	—	Financial reporting period end.
fiscal_period	String	NO	—	Source fiscal label, for example 2025-Q4.
calendar_date	Date	YES	—	Fiscal calendar label; never the visibility date.
accoci	Float64	YES	reporting currency	Accumulated Other Comprehensive Income.
assets	Float64	YES	reporting currency	Total Assets.
assetsavg	Float64	YES	reporting currency	Average Assets.
assetsc	Float64	YES	reporting currency	Current Assets.
assetsnc	Float64	YES	reporting currency	Assets Non-Current.
assetturnover	Float64	YES	ratio	Asset Turnover.
bvps	Float64	YES	reporting currency/share	Book Value per Share.
capex	Float64	YES	reporting currency	Capital Expenditure.
cashneq	Float64	YES	reporting currency	Cash and Equivalents.
cashnequsd	Float64	YES	USD	Cash and Equivalents (USD).
consolinc	Float64	YES	reporting currency	Consolidated Income.
cor	Float64	YES	reporting currency	Cost of Revenue.
currentratio	Float64	YES	ratio	Current Ratio.
de	Float64	YES	ratio	Debt to Equity Ratio.
debt	Float64	YES	reporting currency	Total Debt.
debtc	Float64	YES	reporting currency	Debt Current.
debtnc	Float64	YES	reporting currency	Debt Non-Current.
debtusd	Float64	YES	USD	Total Debt (USD).
deferredrev	Float64	YES	reporting currency	Deferred Revenue.
depamor	Float64	YES	reporting currency	Depreciation Amortization & Accretion.
deposits	Float64	YES	reporting currency	Deposit Liabilities.
divyield	Float64	YES	ratio	Dividend Yield.
dps	Float64	YES	USD/share	Dividends per Basic Common Share.
ebit	Float64	YES	reporting currency	Earning Before Interest & Taxes (EBIT).
ebitda	Float64	YES	reporting currency	Earnings Before Interest Taxes & Depreciation Amortization (EBITDA).
ebitdamargin	Float64	YES	ratio	EBITDA Margin.
ebitdausd	Float64	YES	USD	Earnings Before Interest Taxes & Depreciation Amortization (USD).
ebitusd	Float64	YES	USD	Earning Before Interest & Taxes (USD).
ebt	Float64	YES	reporting currency	Earnings before Tax.
eps	Float64	YES	reporting currency/share	Earnings per Basic Share.
epsdil	Float64	YES	reporting currency/share	Earnings per Diluted Share.
epsusd^SAMPLE	Float64	YES	USD/share	Earnings per Basic Share (USD).
equity	Float64	YES	reporting currency	Shareholders Equity Attributable to Parent.
equityavg	Float64	YES	reporting currency	Average Equity.
equityusd	Float64	YES	USD	Shareholders Equity (USD).
enterprise_value_usd	Float64	YES	USD	Enterprise Value.
evebit	Float64	YES	ratio	Enterprise Value over EBIT.
evebitda	Float64	YES	ratio	Enterprise Value over EBITDA.
fcf	Float64	YES	reporting currency	Free Cash Flow.
fcfps	Float64	YES	reporting currency/share	Free Cash Flow per Share.
fxusd	Float64	YES	ratio	Foreign Currency to USD Exchange Rate.
gp	Float64	YES	reporting currency	Gross Profit.
grossmargin	Float64	YES	ratio	Gross Margin.
intangibles	Float64	YES	reporting currency	Goodwill and Intangible Assets.
intexp	Float64	YES	reporting currency	Interest Expense.
invcap	Float64	YES	reporting currency	Invested Capital.
invcapavg	Float64	YES	reporting currency	Invested Capital Average.
inventory	Float64	YES	reporting currency	Inventory.
investments	Float64	YES	reporting currency	Investments.
investmentsc	Float64	YES	reporting currency	Investments Current.
investmentsnc	Float64	YES	reporting currency	Investments Non-Current.
liabilities	Float64	YES	reporting currency	Total Liabilities.
liabilitiesc	Float64	YES	reporting currency	Current Liabilities.
liabilitiesnc	Float64	YES	reporting currency	Liabilities Non-Current.
market_cap_usd	Float64	YES	USD	Market Capitalization.
ncf	Float64	YES	reporting currency	Net Cash Flow / Change in Cash & Cash Equivalents.
ncfbus	Float64	YES	reporting currency	Net Cash Flow - Business Acquisitions and Disposals.
ncfcommon	Float64	YES	reporting currency	Issuance (Purchase) of Equity Shares.
ncfdebt	Float64	YES	reporting currency	Issuance (Repayment) of Debt Securities.
ncfdiv	Float64	YES	reporting currency	Payment of Dividends & Other Cash Distributions.
ncff	Float64	YES	reporting currency	Net Cash Flow from Financing.
ncfi	Float64	YES	reporting currency	Net Cash Flow from Investing.
ncfinv	Float64	YES	reporting currency	Net Cash Flow - Investment Acquisitions and Disposals.
ncfo	Float64	YES	reporting currency	Net Cash Flow from Operations.
ncfx	Float64	YES	reporting currency	Effect of Exchange Rate Changes on Cash.
netinc	Float64	YES	reporting currency	Net Income.
netinccmn	Float64	YES	reporting currency	Net Income Common Stock.
netinccmnusd	Float64	YES	USD	Net Income Common Stock (USD).
netincdis	Float64	YES	reporting currency	Net Loss Income from Discontinued Operations.
netincnci	Float64	YES	reporting currency	Net Income to Non-Controlling Interests.
netmargin	Float64	YES	ratio	Profit Margin.
opex	Float64	YES	reporting currency	Operating Expenses.
opinc	Float64	YES	reporting currency	Operating Income.
payables	Float64	YES	reporting currency	Trade and Non-Trade Payables.
payoutratio	Float64	YES	ratio	Payout Ratio.
pb	Float64	YES	ratio	Price to Book Value.
pe	Float64	YES	ratio	Price Earnings (Damodaran Method).
pe1	Float64	YES	ratio	Price to Earnings Ratio.
ppnenet	Float64	YES	reporting currency	Property Plant & Equipment Net.
prefdivis	Float64	YES	reporting currency	Preferred Dividends Income Statement Impact.
price	Float64	YES	USD/share	Share Price (Adjusted Close).
ps	Float64	YES	ratio	Price Sales (Damodaran Method).
ps1	Float64	YES	ratio	Price to Sales Ratio.
receivables	Float64	YES	reporting currency	Trade and Non-Trade Receivables.
retearn	Float64	YES	reporting currency	Accumulated Retained Earnings (Deficit).
revenue	Float64	YES	reporting currency	Revenues.
revenueusd^SAMPLE	Float64	YES	USD	Revenues (USD).
rnd	Float64	YES	reporting currency	Research and Development Expense.
roa	Float64	YES	ratio	Return on Average Assets.
roe^SAMPLE	Float64	YES	ratio	Return on Average Equity.
roic	Float64	YES	ratio	Return on Invested Capital.
ros	Float64	YES	ratio	Return on Sales.
sbcomp	Float64	YES	reporting currency	Share Based Compensation.
sgna	Float64	YES	reporting currency	Selling General and Administrative Expense.
sharefactor	Float64	YES	ratio	Share Factor.
sharesbas	Float64	YES	shares	Shares (Basic).
shareswa	Float64	YES	shares	Weighted Average Shares.
shareswadil	Float64	YES	shares	Weighted Average Shares Diluted.
sps	Float64	YES	USD/share	Sales per Share.
tangibles	Float64	YES	reporting currency	Tangible Asset Value.
taxassets	Float64	YES	reporting currency	Tax Assets.
taxexp	Float64	YES	reporting currency	Income Tax Expense.
taxliabilities	Float64	YES	reporting currency	Tax Liabilities.
tbvps	Float64	YES	reporting currency/share	Tangible Assets Book Value per Share.
workingcapital	Float64	YES	reporting currency	Working Capital.

Contract notes

Native source ticker is the data identity; share classes are never silently aliased or routed for execution.
Retain source ticker case, spaces and index prefix (e.g. ^IXIC) exactly; storage uses a reversible encoding.
At decision T only index < T is visible, within the declared rolling lookback_days in all four modes.
Sparse dates and nullable values are preserved; window counts observations, not reporting quarters.
Historical rows are corrected snapshots; source ingestion/version fields do not prove first publication.
ARQ/ARY/ART retain as-reported quarter/year/TTM observations; MR restatements are not admitted.
Index is filing date + 36 hours (next day 12:00 UTC), a conservative batch allowance, not an exact publication timestamp.
Multiple filings in one quarter are real observations; compare report_period, not blindly every fourth row.
Reporting-currency fields are not USD unless their unit explicitly says USD; fxusd and USD variants are separate facts.

US equity daily valuation#

Daily valuation ratios, market cap and enterprise value; money is normalized to USD, not millions of USD.

ID · VERSIONmarket.equity.us.valuation_1d@1

FREQUENCY · SHAPE1d · SERIES

TIME INDEXday_end_plus_12h

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · native source ticker, for example AAPL
Observed sample entity · AAPL

CAPTURED · 2026-09-04T09:00:57.476716Z

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "valuation": {
            "id": "market.equity.us.valuation_1d",
            "schema_version": 1,
            "entities": ["AAPL"],
            "fields": ["price_date","market_cap_usd","enterprise_value_usd","pe","pb","ps"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "valuation", "AAPL", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "valuation", "AAPL", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: price_date=object, market_cap_usd=float64, enterprise_value_usd=float64, pe=float64, pb=float64, ps=float64
time                  price_date  market_cap_usd  enterprise_value_usd  pe    pb    ps
2026-09-01T12:00:00Z  2026-08-31  4624165900000   4668965900000         35.9  43    9.9
2026-09-02T12:00:00Z  2026-09-01  4745005700000   4789805700000         36.8  44.1  10.2
2026-09-03T12:00:00Z  2026-09-02  4742524700000   4787324700000         36.8  44.1  10.2

Complete field contract · 8 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
price_date^SAMPLE	Date	NO	—	Valuation price date.
enterprise_value_usd^SAMPLE	Float64	YES	USD	Enterprise Value - Daily.
evebit	Float64	YES	ratio	Enterprise Value over EBIT - Daily.
evebitda	Float64	YES	ratio	Enterprise Value over EBITDA - Daily.
market_cap_usd^SAMPLE	Float64	YES	USD	Market Capitalization - Daily.
pb^SAMPLE	Float64	YES	ratio	Price to Book Value - Daily.
pe^SAMPLE	Float64	YES	ratio	Price Earnings (Damodaran Method) - Daily.
ps^SAMPLE	Float64	YES	ratio	Price Sales (Damodaran Method) - Daily.

Contract notes

Native source ticker is the data identity; share classes are never silently aliased or routed for execution.
Retain source ticker case, spaces and index prefix (e.g. ^IXIC) exactly; storage uses a reversible encoding.
At decision T only index < T is visible, within the declared rolling lookback_days in all four modes.
Sparse dates and nullable values are preserved; window counts observations, not reporting quarters.
Historical rows are corrected snapshots; source ingestion/version fields do not prove first publication.
Index is price date + 36 hours; daily valuation uses source as-reported financials.
Market cap and enterprise value are normalized from source USD millions to USD.

US equity material filing events#

Sparse filing-day event codes; not predicted earnings dates, event sentiment or a complete corporate-action ledger.

ID · VERSIONmarket.equity.us.filing_events_1d@1

FREQUENCY · SHAPE1d · SERIES

TIME INDEXfiling_day_plus_36h

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · native source ticker, for example AAPL
Observed sample entity · AAPL

CAPTURED · 2026-09-04T09:00:57.476716Z

factor.py / strategy.py

__data__ = {
    "lookback_days": 730,
    "datasets": {
        "filings": {
            "id": "market.equity.us.filing_events_1d",
            "schema_version": 1,
            "entities": ["AAPL"],
            "fields": ["filing_date","event_codes"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "filings", "AAPL", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "filings", "AAPL", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: filing_date=object, event_codes=str
time                  filing_date  event_codes
2026-05-01T12:00:00Z  2026-04-30   22|91
2026-07-31T12:00:00Z  2026-07-30   22|91
2026-09-02T12:00:00Z  2026-09-01   52

Complete field contract · 2 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
filing_date^SAMPLE	Date	NO	—	Form 8-K filing date.
event_codes^SAMPLE	String	NO	—	Source pipe-separated 8-K event codes; not sentiment or earnings surprise.

Contract notes

Native source ticker is the data identity; share classes are never silently aliased or routed for execution.
Retain source ticker case, spaces and index prefix (e.g. ^IXIC) exactly; storage uses a reversible encoding.
At decision T only index < T is visible, within the declared rolling lookback_days in all four modes.
Sparse dates and nullable values are preserved; window counts observations, not reporting quarters.
Historical rows are corrected snapshots; source ingestion/version fields do not prove first publication.
Index is filing date + 36 hours. Codes describe filings, not the transaction/event occurrence date.

US equity insider filing activity#

Filing-day non-derivative purchases and sales. Notional uses reported price × absolute shares, with priced-row counts and nullable totals.

ID · VERSIONmarket.equity.us.insider_activity_1d@1

FREQUENCY · SHAPE1d · SERIES

TIME INDEXfiling_day_plus_36h

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · native source ticker, for example AAPL
Observed sample entity · AAPL

CAPTURED · 2026-09-04T09:00:57.476716Z

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "insiders": {
            "id": "market.equity.us.insider_activity_1d",
            "schema_version": 1,
            "entities": ["AAPL"],
            "fields": ["filing_date","purchase_count","sale_count","purchase_notional_usd","sale_notional_usd"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "insiders", "AAPL", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "insiders", "AAPL", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: filing_date=object, purchase_count=int64, sale_count=int64, purchase_notional_usd=object, sale_notional_usd=float64
time                  filing_date  purchase_count  sale_count  purchase_notional_usd  sale_notional_usd
2026-08-21T12:00:00Z  2026-08-20   0               1           null                   442478.11
2026-08-28T12:00:00Z  2026-08-27   0               1           null                   447457.05
2026-09-02T12:00:00Z  2026-09-01   0               0           null                   null

Complete field contract · 11 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
filing_date^SAMPLE	Date	NO	—	SEC Form 3/4/5 filing date.
filing_row_count	UInt64	NO	rows	Deduplicated source filing rows; not the number of distinct filings.
purchase_count^SAMPLE	UInt64	NO	rows	Non-derivative P-coded purchase rows.
sale_count^SAMPLE	UInt64	NO	rows	Non-derivative S-coded sale rows.
other_row_count	UInt64	NO	rows	All other rows, including awards, tax withholding, options and exercises.
purchase_priced_count	UInt64	NO	rows	Purchase rows with both reported quantity and price.
sale_priced_count	UInt64	NO	rows	Sale rows with both reported quantity and price.
purchase_notional_usd^SAMPLE	Float64	YES	USD	Sum of abs(quantity) * reported USD price for non-derivative P rows; null if none priced.
sale_notional_usd^SAMPLE	Float64	YES	USD	Sum of abs(quantity) * reported USD price for non-derivative S rows; null if none priced.
first_transaction_date	Date	YES	—	Earliest transaction date in this day's disclosed rows.
last_transaction_date	Date	YES	—	Latest transaction date in this day's disclosed rows.

Contract notes

Native source ticker is the data identity; share classes are never silently aliased or routed for execution.
Retain source ticker case, spaces and index prefix (e.g. ^IXIC) exactly; storage uses a reversible encoding.
At decision T only index < T is visible, within the declared rolling lookback_days in all four modes.
Sparse dates and nullable values are preserved; window counts observations, not reporting quarters.
Historical rows are corrected snapshots; source ingestion/version fields do not prove first publication.
Index is filing date + 36 hours, never transaction date; sparse filing days are not padded.
Only N-prefixed acquired/disposed codes with P/S transactions enter purchase/sale aggregates.
Notional is explicitly calculated from reported quantity and price, not the source transactionvalue field with inconsistent unit metadata.
Priced counts disclose partial totals; these are reported transactions, not an OPR trading signal.

US equity institutional quarterly holdings#

Ticker-level holdings visible after an assumed 60-calendar-day lag. This is not a filing-timestamp-exact PIT dataset.

ID · VERSIONmarket.equity.us.institutional_holdings_1q@1

FREQUENCY · SHAPE1q · SERIES

TIME INDEXquarter_end_plus_60d_12h

HISTORY · PITCORRECTED_SNAPSHOT · ASSUMED_PUBLICATION_LAG

Entity format · native source ticker, for example AAPL
Observed sample entity · AAPL

CAPTURED · 2026-09-04T09:00:57.476716Z

factor.py / strategy.py

__data__ = {
    "lookback_days": 730,
    "datasets": {
        "holdings": {
            "id": "market.equity.us.institutional_holdings_1q",
            "schema_version": 1,
            "entities": ["AAPL"],
            "fields": ["report_period","shrholders","shrunits","shrvalue","totalvalue"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "holdings", "AAPL", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "holdings", "AAPL", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: report_period=object, shrholders=float64, shrunits=float64, shrvalue=float64, totalvalue=float64
time                  report_period  shrholders  shrunits    shrvalue       totalvalue
2026-03-01T12:00:00Z  2025-12-31     6119        9637861000  2609624400000  2732093000000
2026-05-30T12:00:00Z  2026-03-31     6121        9484956000  2396209600000  2496552400000
2026-08-29T12:00:00Z  2026-06-30     6124        9676660000  2795596100000  2928822500000

Complete field contract · 27 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
report_period^SAMPLE	Date	NO	—	13F calendar-quarter end, not the filing date.
cllholders	Float64	YES	count	Number of Call holders (Institutional).
cllunits	Float64	YES	units	Number of Call Units held (institutional).
cllvalue	Float64	YES	USD	Value of Call units held (institutional).
dbtholders	Float64	YES	count	Number of Debt holders (institutional).
dbtunits	Float64	YES	units	Number of Debt Units held (institutional).
dbtvalue	Float64	YES	USD	Value of Debt units held (institutional).
fndholders	Float64	YES	count	Number of Fund holders (institutional).
fndunits	Float64	YES	units	Number of Fund units held (institutional).
fndvalue	Float64	YES	USD	Value of Fund units held (institutional).
percentoftotal	Float64	YES	percentage points	Percentage of Total Institutional Holdings for the Quarter.
prfholders	Float64	YES	count	Number of Preferred Stock holders (institutional).
prfunits	Float64	YES	units	Number of Preferred Stock units held (institutional).
prfvalue	Float64	YES	USD	Value of Preferred Stock units held (institutional).
putholders	Float64	YES	count	Number of Put holders (institutional).
putunits	Float64	YES	units	Number of Put Units held (institutional).
putvalue	Float64	YES	USD	Value of Put units held (institutional).
shrholders^SAMPLE	Float64	YES	count	Number of Shareholders (Institutional).
shrunits^SAMPLE	Float64	YES	units	Number of Share Units held (institutional).
shrvalue^SAMPLE	Float64	YES	USD	Value of Share units held (institutional).
totalvalue^SAMPLE	Float64	YES	USD	Total Value of all Security types held (institutional).
undholders	Float64	YES	count	Number of Unidentified Security type holders (institutional).
undunits	Float64	YES	units	Number of Unidentified Security type units held (institutional).
undvalue	Float64	YES	USD	Value of Unidentified Security type units held (institutional).
wntholders	Float64	YES	count	Number of Warrant holders (institutional).
wntunits	Float64	YES	units	Number of Warrant Units held (institutional).
wntvalue	Float64	YES	USD	Value of Warrant units held (institutional).

Contract notes

Native source ticker is the data identity; share classes are never silently aliased or routed for execution.
Retain source ticker case, spaces and index prefix (e.g. ^IXIC) exactly; storage uses a reversible encoding.
At decision T only index < T is visible, within the declared rolling lookback_days in all four modes.
Sparse dates and nullable values are preserved; window counts observations, not reporting quarters.
Historical rows are corrected snapshots; source ingestion/version fields do not prove first publication.
Source has no historical filing timestamps. Index = quarter end + 60 calendar days at 12:00 UTC is an explicit research assumption, not strict PIT.
The assumption allows a buffer after the usual 45-day 13F deadline, but cannot prove availability of late filings, amendments or corrections.
Source USD millions and thousand units are normalized to USD and units; percentoftotal remains percentage points.
Only per-ticker aggregates are exposed; each security-type prefix retains its source meaning, and their counts are not interchangeable.

US equity observed price metrics#

Stored dated beta, return and dividend-yield snapshots only. A latest-only source is never expanded into invented historical observations.

ID · VERSIONmarket.equity.us.price_metrics_1d@1

FREQUENCY · SHAPE1d · SERIES

TIME INDEXday_end_plus_12h

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · native source ticker, for example AAPL
Observed sample entity · AACIU

CAPTURED · 2026-09-04T09:00:57.476716Z

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "price_metrics": {
            "id": "market.equity.us.price_metrics_1d",
            "schema_version": 1,
            "entities": ["AACIU"],
            "fields": ["price_date","beta1y","return1y","dividendyieldtrailing"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "price_metrics", "AACIU", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "price_metrics", "AACIU", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: price_date=object, beta1y=float64, return1y=object, dividendyieldtrailing=float64
time                  price_date  beta1y  return1y  dividendyieldtrailing
2026-09-03T12:00:00Z  2026-09-02  0.06    null      0

Complete field contract · 20 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
price_date^SAMPLE	Date	NO	—	Date of the stored price-metric snapshot.
beta1y^SAMPLE	Float64	YES	ratio	Beta - 1 Year Daily.
beta5y	Float64	YES	ratio	Beta - 5 Year Monthly.
dividendyieldforward	Float64	YES	percentage points	Dividend Yield - Forward.
dividendyieldtrailing^SAMPLE	Float64	YES	percentage points	Dividend Yield - Trailing.
high52w	Float64	YES	USD/share	High Price - 52 Week.
high5y	Float64	YES	USD/share	High Price - 5 Year.
low52w	Float64	YES	USD/share	Low Price - 52 Week.
low5y	Float64	YES	USD/share	Low Price - 5 Year.
ma200d	Float64	YES	USD/share	Price Moving Average - 200 Day.
ma200w	Float64	YES	USD/share	Price Moving Average - 200 Week.
ma50d	Float64	YES	USD/share	Price Moving Average - 50 Day.
ma50w	Float64	YES	USD/share	Price Moving Average - 50 Week.
price	Float64	YES	USD/share	Price.
return1y^SAMPLE	Float64	YES	percentage points	Total Return - 1 Year.
return5y	Float64	YES	percentage points	Total Return - 5 Year.
returnytd	Float64	YES	percentage points	Total Return - Year to Date.
volume	Float64	YES	count	Volume.
volumeavg1m	Float64	YES	count	Volume Average - 1 Month.
volumeavg3m	Float64	YES	count	Volume Average - 3 Month.

Contract notes

Native source ticker is the data identity; share classes are never silently aliased or routed for execution.
Retain source ticker case, spaces and index prefix (e.g. ^IXIC) exactly; storage uses a reversible encoding.
At decision T only index < T is visible, within the declared rolling lookback_days in all four modes.
Sparse dates and nullable values are preserved; window counts observations, not reporting quarters.
Historical rows are corrected snapshots; source ingestion/version fields do not prove first publication.
Only stored dated snapshots are read; a current snapshot is never backfilled into earlier dates.
Index is price date + 36 hours. Source returns and dividend yields remain percentage points.

S&P 500 dated membership observations#

Independent dated addition, removal and membership observations; no inferred or forward-filled index membership.

ID · VERSIONmarket.equity.us.sp500_observations_1d@1

FREQUENCY · SHAPE1d · SERIES

TIME INDEXday_end_plus_12h

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · native source ticker, for example AAPL
Observed sample entity · AAPL

CAPTURED · 2026-09-04T09:00:57.476716Z

factor.py / strategy.py

__data__ = {
    "lookback_days": 730,
    "datasets": {
        "sp500": {
            "id": "market.equity.us.sp500_observations_1d",
            "schema_version": 1,
            "entities": ["AAPL"],
            "fields": ["observation_date","added","removed","historical_member","current_member"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "sp500", "AAPL", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "sp500", "AAPL", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: observation_date=object, added=bool, removed=bool, historical_member=bool, current_member=bool
time                  observation_date  added  removed  historical_member  current_member
2026-01-01T12:00:00Z  2025-12-31        False  False    True               False
2026-04-01T12:00:00Z  2026-03-31        False  False    True               False
2026-07-01T12:00:00Z  2026-06-30        False  False    True               False

Complete field contract · 5 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
observation_date^SAMPLE	Date	NO	—	Source effective or membership-snapshot date.
added^SAMPLE	Bool	NO	—	An added record exists on this date.
removed^SAMPLE	Bool	NO	—	A removed record exists on this date.
historical_member^SAMPLE	Bool	NO	—	A historical constituent snapshot includes this ticker on this date.
current_member^SAMPLE	Bool	NO	—	A current constituent snapshot includes this ticker on this date only.

Contract notes

Native source ticker is the data identity; share classes are never silently aliased or routed for execution.
Retain source ticker case, spaces and index prefix (e.g. ^IXIC) exactly; storage uses a reversible encoding.
At decision T only index < T is visible, within the declared rolling lookback_days in all four modes.
Sparse dates and nullable values are preserved; window counts observations, not reporting quarters.
Historical rows are corrected snapshots; source ingestion/version fields do not prove first publication.
Index is source date + 36 hours; current membership is never projected backwards.
Independent dated flags preserve coexisting source records. Universe reconstruction and forward filling belong to Strategy/Factor.
This is effective-date/snapshot history, not historical announcement-time evidence.

On-chain balances & stablecoin supply#

Bitcoin daily on-chain holder and valuation metrics#

Bitcoin STH/LTH SOPR, realized price and supply plus NUPL and Puell Multiple under a frozen 155-day cohort contract.

ID · VERSIONcrypto.onchain.bitcoin.metrics_1d@1

FREQUENCY · SHAPE1d · SERIES

TIME INDEXday_end_plus_12h

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · the singleton BTC entity
Observed sample entity · BTC

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "btc_chain": {
            "id": "crypto.onchain.bitcoin.metrics_1d",
            "schema_version": 1,
            "entities": ["BTC"],
            "fields": ["metric_date","sth_sopr","lth_sopr","nupl","puell_multiple","sth_realized_price_usd","lth_realized_price_usd","sth_supply_btc","lth_supply_btc"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "btc_chain", "BTC", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "btc_chain", "BTC", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: metric_date=object, sth_sopr=float64, lth_sopr=float64, nupl=float64, puell_multiple=float64, sth_realized_price_usd=float64, lth_realized_price_usd=float64, sth_supply_btc=float64, lth_supply_btc=float64
time                  metric_date  sth_sopr    lth_sopr    nupl        puell_multiple      sth_realized_price_usd  lth_realized_price_usd  sth_supply_btc    lth_supply_btc
2026-08-31T12:00:00Z  2026-08-30   1.00429313  1.18279938  0.32236466  1.069154021767318   70058.64393449483       49445.00300776408       4120223.22231606  16738601.61373322
2026-09-01T12:00:00Z  2026-08-31   1.0148015   0.97215813  0.32387415  1.0092386962762343  70190.41255775676       49428.01918428837       4197831.3232899   16730959.80614234
2026-09-02T12:00:00Z  2026-09-01   1.00414813  0.97732866  0.31414441  0.9299154609677172  70297.67767032461       49411.508769431086      4258411.78953358  16726477.30994246

Complete field contract · 9 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
metric_date^SAMPLE	Date	NO	—	UTC source date represented by the indicator row.
sth_sopr^SAMPLE	Float64	YES	ratio	Spent Output Profit Ratio for the source-defined short-term holder cohort.
lth_sopr^SAMPLE	Float64	YES	ratio	Spent Output Profit Ratio for the source-defined long-term holder cohort.
nupl^SAMPLE	Float64	YES	ratio	Bitcoin net unrealized profit/loss ratio.
puell_multiple^SAMPLE	Float64	YES	ratio	Bitcoin miner-revenue Puell Multiple.
sth_realized_price_usd^SAMPLE	Float64	YES	USD/BTC	Realized price for the source-defined short-term holder cohort.
lth_realized_price_usd^SAMPLE	Float64	YES	USD/BTC	Realized price for the source-defined long-term holder cohort.
sth_supply_btc^SAMPLE	Float64	YES	BTC	Bitcoin supply attributed to the source-defined short-term holder cohort.
lth_supply_btc^SAMPLE	Float64	YES	BTC	Bitcoin supply attributed to the source-defined long-term holder cohort.

Contract notes

Each metric_date is conservatively indexed at 12:00 UTC on the following day.
STH and LTH fields freeze the source's 155-day cohort cutoff in schema version 1.
Metrics are nullable independently because source series can publish on different days.
Provider reference prices are omitted because they differ by metric and are not execution prices.
Historical rows are corrected snapshots; ingestion time is not historical publication time.

Third-party observed exchange reserve balances#

Provider-owned ALL-chain exchange reserve observations in native asset units—not account balances, official PoR or solvency evidence.

ID · VERSIONcrypto.exchange.reserve_balance_1d@1

FREQUENCY · SHAPE1d · SERIES

TIME INDEXday_end_plus_12h

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · lowercase <provider>/<exchange>/uppercase-asset, for example defillama/binance/BTC
Observed sample entity · defillama/binance/BTC

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "exchange_reserves": {
            "id": "crypto.exchange.reserve_balance_1d",
            "schema_version": 1,
            "entities": ["defillama/binance/BTC"],
            "fields": ["stat_date","balance_asset_qty","source_provider","chain_scope"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "exchange_reserves", "defillama/binance/BTC", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "exchange_reserves", "defillama/binance/BTC", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: stat_date=object, balance_asset_qty=float64, source_provider=str, chain_scope=str
time                  stat_date   balance_asset_qty  source_provider  chain_scope
2026-09-01T12:00:00Z  2026-08-31  635722.41347       defillama        ALL
2026-09-02T12:00:00Z  2026-09-01  636867.1915        defillama        ALL
2026-09-03T12:00:00Z  2026-09-02  639900.38377       defillama        ALL

Complete field contract · 4 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
stat_date^SAMPLE	Date	NO	—	UTC source date represented by the cross-chain reserve snapshot.
balance_asset_qty^SAMPLE	Float64	NO	asset units	Third-party observed exchange reserve balance in units of the entity asset.
source_provider^SAMPLE	String	NO	—	Provider whose methodology and coverage define this entity's series.
chain_scope^SAMPLE	String	NO	—	Source aggregation scope; schema version 1 exposes only ALL-chain totals.

Contract notes

Each stat_date is conservatively indexed at 12:00 UTC on the following day.
Provider is part of identity; OPR never splices histories across source methodologies.
balance_asset_qty remains in native asset units and is never converted to USD by OPR.
Schema version 1 exposes source-owned ALL-chain totals, not chain-level attribution.
These are third-party observed reserves, not official proof of reserves, customer liabilities or solvency evidence.
Historical rows are corrected snapshots; missing dates are not filled and revisions are selected atomically.
The dataset is read-only and never implies an execution venue, signal or order action.

Global stablecoin daily supply, price and market capitalization#

Global per-asset stablecoin reference price, circulating supply and provider market cap as independent source fields.

ID · VERSIONcrypto.stablecoin.supply_1d@1

FREQUENCY · SHAPE1d · SERIES

TIME INDEXday_end_plus_12h

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · uppercase stablecoin asset, for example USDT or USDC
Observed sample entity · USDT

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "stable_supply": {
            "id": "crypto.stablecoin.supply_1d",
            "schema_version": 1,
            "entities": ["USDT"],
            "fields": ["metric_date","price_usd","circulating_supply","market_cap_usd"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "stable_supply", "USDT", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "stable_supply", "USDT", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: metric_date=object, price_usd=float64, circulating_supply=float64, market_cap_usd=float64
time                  metric_date  price_usd     circulating_supply  market_cap_usd
2026-08-31T12:00:00Z  2026-08-30   1.0000345163  183424569787.6266   183409547755.58
2026-09-01T12:00:00Z  2026-08-31   0.9999895456  183361202281.8293   183329310818
2026-09-02T12:00:00Z  2026-09-01   0.9997370096  183375156013.7062   183272740934.06

Complete field contract · 4 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
metric_date^SAMPLE	Date	NO	—	UTC source date represented by the stablecoin row.
price_usd^SAMPLE	Float64	NO	USD/token	Provider stablecoin reference price.
circulating_supply^SAMPLE	Float64	NO	tokens	Provider circulating token supply; global rather than chain-specific.
market_cap_usd^SAMPLE	Float64	NO	USD	Provider stablecoin market capitalization; retained as an independent source field.

Contract notes

Each metric_date is conservatively indexed at 12:00 UTC on the following day.
Supply is global asset-level data and does not claim chain-level attribution.
Price, circulating supply and market capitalization are independent provider fields.
Missing dates are not filled and market capitalization is never recomputed by OPR.
Historical rows are corrected snapshots; ingestion time is not historical publication time.

Stablecoin available-component market-cap basket#

A fixed four-asset market-cap basket that discloses the exact available components contributing on every date.

ID · VERSIONcrypto.stablecoin.market_cap_basket_1d@1

FREQUENCY · SHAPE1d · SERIES

TIME INDEXday_end_plus_12h

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · the singleton USDT_USDC_DAI_FDUSD_V1 basket
Observed sample entity · USDT_USDC_DAI_FDUSD_V1

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "stable_basket": {
            "id": "crypto.stablecoin.market_cap_basket_1d",
            "schema_version": 1,
            "entities": ["USDT_USDC_DAI_FDUSD_V1"],
            "fields": ["metric_date","market_cap_usd","component_count","available_assets"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "stable_basket", "USDT_USDC_DAI_FDUSD_V1", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "stable_basket", "USDT_USDC_DAI_FDUSD_V1", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: metric_date=object, market_cap_usd=float64, component_count=int64, available_assets=object
time                  metric_date  market_cap_usd   component_count  available_assets
2026-08-31T12:00:00Z  2026-08-30   262209245430.65  4                ["DAI","FDUSD","USDC","USDT"]
2026-09-01T12:00:00Z  2026-08-31   261674077562.27  4                ["DAI","FDUSD","USDC","USDT"]
2026-09-02T12:00:00Z  2026-09-01   261778232905.68  4                ["DAI","FDUSD","USDC","USDT"]

Complete field contract · 4 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
metric_date^SAMPLE	Date	NO	—	UTC source date represented by the basket row.
market_cap_usd^SAMPLE	Float64	NO	USD	Sum of source market capitalizations for the available basket components.
component_count^SAMPLE	UInt8	NO	assets	Number of basket assets with a source row on metric_date.
available_assets^SAMPLE	Array[String]	NO	—	Sorted basket assets contributing to market_cap_usd on metric_date.

Contract notes

Each metric_date is conservatively indexed at 12:00 UTC on the following day.
The v1 universe is USDT, USDC, DAI and FDUSD, not the full stablecoin market.
Only assets with a source row on metric_date contribute to market_cap_usd.
available_assets and component_count disclose historical composition changes.
Historical rows are corrected snapshots and missing components are never imputed.

Public-chain & network activity#

Public-chain daily transaction activity#

Source-complete daily transaction and fee facts with CAIP-2 identities and chain-specific semantics.

ID · VERSIONcrypto.chain.activity_1d@1

FREQUENCY · SHAPE1d · SERIES

TIME INDEXday_end_plus_12h

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · CAIP-2 <namespace>:<reference>
Observed sample entity · eip155:1

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "chain_activity": {
            "id": "crypto.chain.activity_1d",
            "schema_version": 1,
            "entities": ["eip155:1"],
            "fields": ["activity_date","tx_count","gas_used","fee_native","native_asset","source_provider"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "chain_activity", "eip155:1", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "chain_activity", "eip155:1", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: activity_date=object, tx_count=int64, gas_used=object, fee_native=float64, native_asset=str, source_provider=str
time                  activity_date  tx_count  gas_used  fee_native          native_asset  source_provider
2026-08-31T12:00:00Z  2026-08-30     1794571   null      127.06130526073696  ETH           coinmetrics
2026-09-01T12:00:00Z  2026-08-31     1838510   null      171.16181146385563  ETH           coinmetrics
2026-09-02T12:00:00Z  2026-09-01     2013158   null      145.30400797369722  ETH           coinmetrics

Complete field contract · 7 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
activity_date^SAMPLE	Date	NO	—	UTC date represented by the finalized daily observation.
is_final	Bool	NO	—	Whether the source marked the UTC day complete; exposed rows are final.
tx_count^SAMPLE	UInt64	YES	transactions/day	Source-defined transaction count. Solana includes vote transactions.
gas_used^SAMPLE	UInt64	YES	gas/day	Source-reported daily gas used when the chain supplies this metric.
fee_native^SAMPLE	Float64	YES	native asset/day	Daily transaction fees normalized from source base units into the chain native asset.
native_asset^SAMPLE	String	NO	—	Native fee asset for this chain entity.
source_provider^SAMPLE	String	NO	—	Canonical source provider selected for this entity.

Contract notes

Only source-complete UTC days are exposed.
The public index is UTC day end plus a conservative 12-hour source-publication allowance.
Transaction-count semantics remain source and chain specific; Solana includes vote transactions.
No active-address metric is claimed because the source does not provide one.
Historical rows are corrected snapshots; ingestion timestamps are not treated as historical publication times.

Public-chain hourly fee and gas activity#

Deduplicated source-minute fee and gas observations aggregated hourly with a conservative publication lag.

ID · VERSIONcrypto.chain.fee_1h@1

FREQUENCY · SHAPE1h · SERIES

TIME INDEXhour_end_plus_48h

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · CAIP-2 <namespace>:<reference>
Observed sample entity · eip155:1

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "chain_fee": {
            "id": "crypto.chain.fee_1h",
            "schema_version": 1,
            "entities": ["eip155:1"],
            "fields": ["observation_hour_end","fee_native","gas_used","gas_price_wei","minute_observations","native_asset"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "chain_fee", "eip155:1", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "chain_fee", "eip155:1", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: observation_hour_end=datetime64[us], fee_native=float64, gas_used=int64, gas_price_wei=float64, minute_observations=int64, native_asset=str
time                  observation_hour_end  fee_native          gas_used    gas_price_wei      minute_observations  native_asset
2026-09-03T02:00:00Z  2026-09-01T02:00:00   3.557250839860122   9061197867  392580638.0208608  60                   ETH
2026-09-03T03:00:00Z  2026-09-01T03:00:00   12.632729528887833  9162309987  1378771242.930206  60                   ETH
2026-09-03T04:00:00Z  2026-09-01T04:00:00   4.043531366947858   9246222307  437317126.1398978  60                   ETH

Complete field contract · 9 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
observation_hour_end^SAMPLE	DateTime	NO	UTC	End of the source hour represented by the observation, before the conservative visibility lag.
is_final	Bool	NO	—	Whether the hourly aggregate is built from the source HISTORY plane; exposed rows are final.
fee_native^SAMPLE	Float64	YES	native asset/hour	Observed hourly fees normalized from satoshi, wei or lamports into the chain native asset.
gas_used^SAMPLE	UInt64	YES	gas/hour	Observed hourly gas used when supplied by the chain source.
gas_price_wei^SAMPLE	Float64	YES	wei/gas	Gas-used-weighted hourly fee per gas for EVM chains; null for non-EVM chains.
fee_sat_per_vb_p50	Float64	YES	sat/vB	Median observed BTC fee rate in the hour; null for non-BTC chains.
minute_observations^SAMPLE	UInt16	NO	observed minutes/hour	Distinct source minute keys represented in the hour; sparse BTC block minutes are not filled.
native_asset^SAMPLE	String	NO	—	Native fee asset for this chain entity.
source_provider	String	NO	—	Canonical source provider selected for this entity.

Contract notes

Source HISTORY minute rows are deduplicated by business key and version before hourly aggregation.
The public index is observation hour end plus a conservative 48-hour batch-publication allowance.
BTC remains sparse at block-observation minutes; missing minutes and nullable metrics are never filled.
Gas fields are chain-specific and nullable; raw cross-chain gas levels are not normalized into a score.
Historical rows are corrected snapshots; ingestion timestamps are not treated as historical publication times.

MemeCoin global daily launch and graduation activity#

Final daily launch and normalized graduation totals across every registered source.

ID · VERSIONcrypto.memecoin.activity.global_1d@1

FREQUENCY · SHAPE1d · SERIES

TIME INDEXperiod_available_at

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · global
Observed sample entity · global

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "meme_global": {
            "id": "crypto.memecoin.activity.global_1d",
            "schema_version": 1,
            "entities": ["global"],
            "fields": ["is_final","launched_count","graduated_count","quality_status"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "meme_global", "global", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "meme_global", "global", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: is_final=bool, launched_count=int64, graduated_count=int64, quality_status=str
time                  is_final  launched_count  graduated_count  quality_status
2026-09-01T00:05:00Z  True      59521           3036             OK
2026-09-02T00:05:00Z  True      52535           3233             OK
2026-09-03T00:05:00Z  True      53747           3097             OK

Complete field contract · 4 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
is_final^SAMPLE	Bool	NO	—	Whether the producer finalized the UTC-day aggregate; exposed rows are final.
launched_count^SAMPLE	UInt64	NO	token launches/day	Canonical token-launch count aggregated across registered MemeCoin sources.
graduated_count^SAMPLE	UInt64	NO	token graduations/day	Canonical successful-graduation count under each source's registered semantics.
quality_status^SAMPLE	String	NO	—	Producer summary quality status for the daily aggregate.

Contract notes

Only final UTC-day aggregates are exposed.
The index is the producer's period_available_at; each row describes the preceding UTC date.
At decision time T, observations with period_available_at < T are visible.
graduated_count normalizes each registered source's canonical success semantics.
Protocol/source operations and provisional current-day rows stay behind the adapter boundary.

MemeCoin chain daily launch and graduation activity#

The same launch and graduation contract partitioned by catalogued CAIP-2 chain identity.

ID · VERSIONcrypto.memecoin.activity.chain_1d@1

FREQUENCY · SHAPE1d · SERIES

TIME INDEXperiod_available_at

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · CAIP-2 <namespace>:<reference>
Observed sample entity · solana:mainnet-beta

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "meme_chain": {
            "id": "crypto.memecoin.activity.chain_1d",
            "schema_version": 1,
            "entities": ["solana:mainnet-beta"],
            "fields": ["is_final","launched_count","graduated_count","quality_status"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "meme_chain", "solana:mainnet-beta", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "meme_chain", "solana:mainnet-beta", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: is_final=bool, launched_count=int64, graduated_count=int64, quality_status=str
time                  is_final  launched_count  graduated_count  quality_status
2026-09-01T00:05:00Z  True      40691           2957             OK
2026-09-02T00:05:00Z  True      39480           3162             OK
2026-09-03T00:05:00Z  True      36615           3028             OK

Complete field contract · 4 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
is_final^SAMPLE	Bool	NO	—	Whether the producer finalized the UTC-day aggregate; exposed rows are final.
launched_count^SAMPLE	UInt64	NO	token launches/day	Canonical token-launch count aggregated across registered MemeCoin sources.
graduated_count^SAMPLE	UInt64	NO	token graduations/day	Canonical successful-graduation count under each source's registered semantics.
quality_status^SAMPLE	String	NO	—	Producer summary quality status for the daily aggregate.

Contract notes

Only final UTC-day aggregates are exposed.
The index is the producer's period_available_at; each row describes the preceding UTC date.
At decision time T, observations with period_available_at < T are visible.
Chain identities use CAIP-2 and do not hard-code the currently registered chains.
graduated_count normalizes each registered source's canonical success semantics.

DeFi lending & liquidity#

Aave v3 reserve hourly state#

Final Aave v3 reserve rates, balances, capacity flags, valuation and quality fields by market and asset.

ID · VERSIONcrypto.defi.aave.reserve_1h@1

FREQUENCY · SHAPE1h · SERIES

TIME INDEXhour_end

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · eip155:<chain_id>/aave-v3:<market_address>/erc20:<asset_address>
Observed sample entity · eip155:42161/aave-v3:0x794a61358d6845594f94dc1db02a252b5b4814ad/erc20:0x82af49447d8a07e3bd95bd0d56f35241523fbab1

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "aave_reserve": {
            "id": "crypto.defi.aave.reserve_1h",
            "schema_version": 1,
            "entities": ["eip155:42161/aave-v3:0x794a61358d6845594f94dc1db02a252b5b4814ad/erc20:0x82af49447d8a07e3bd95bd0d56f35241523fbab1"],
            "fields": ["liquidity_apy_close","variable_borrow_apy_close","total_supply_usd","total_borrow_usd","utilization_rate","quality_status"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "aave_reserve", "eip155:42161/aave-v3:0x794a61358d6845594f94dc1db02a252b5b4814ad/erc20:0x82af49447d8a07e3bd95bd0d56f35241523fbab1", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "aave_reserve", "eip155:42161/aave-v3:0x794a61358d6845594f94dc1db02a252b5b4814ad/erc20:0x82af49447d8a07e3bd95bd0d56f35241523fbab1", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: liquidity_apy_close=float64, variable_borrow_apy_close=float64, total_supply_usd=float64, total_borrow_usd=float64, utilization_rate=float64, quality_status=str
time                  liquidity_apy_close   variable_borrow_apy_close  total_supply_usd    total_borrow_usd    utilization_rate    quality_status
2026-09-03T02:00:00Z  0.009517651379017394  0.019754567533622664       234396906.31834465  133565002.16863582  0.8180463704003216  OK
2026-09-03T03:00:00Z  0.009520038527915275  0.01975715451929556        236285840.32173797  134657539.29192308  0.8181524657143308  OK
2026-09-03T04:00:00Z  0.009529483723931273  0.019768777606150412       235933106.12745503  134510963.99268055  0.8186291044204275  OK

Complete field contract · 43 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
start_block	UInt64	NO	block	First Ethereum block covered by the hour.
end_block	UInt64	NO	block	Last Ethereum block covered by the hour.
covered_seconds	UInt32	NO	second	Observed duration represented by the row.
is_final	Bool	NO	—	Whether the producer finalized the hourly row; exposed rows are always final.
liquidity_apr_open	Float64	NO	ratio/year	Supply APR at the start of the hour.
liquidity_apr_close	Float64	NO	ratio/year	Supply APR at the end of the hour.
liquidity_apy_open	Float64	NO	ratio/year	Compounded supply APY at the start of the hour.
liquidity_apy_close^SAMPLE	Float64	NO	ratio/year	Compounded supply APY at the end of the hour.
variable_borrow_apr_open	Float64	NO	ratio/year	Variable borrow APR at the start of the hour.
variable_borrow_apr_close	Float64	NO	ratio/year	Variable borrow APR at the end of the hour.
variable_borrow_apy_open	Float64	NO	ratio/year	Compounded variable borrow APY at the start of the hour.
variable_borrow_apy_close^SAMPLE	Float64	NO	ratio/year	Compounded variable borrow APY at the end of the hour.
stable_borrow_apr_open	Float64	NO	ratio/year	Stable borrow APR at the start of the hour.
stable_borrow_apr_close	Float64	NO	ratio/year	Stable borrow APR at the end of the hour.
stable_borrow_apy_open	Float64	NO	ratio/year	Compounded stable borrow APY at the start of the hour.
stable_borrow_apy_close	Float64	NO	ratio/year	Compounded stable borrow APY at the end of the hour.
supply_return_1h	Float64	NO	ratio/hour	Realized protocol supply return over the hour.
borrow_cost_1h	Float64	NO	ratio/hour	Realized variable borrow cost over the hour.
total_supply	Float64	NO	asset	Total supplied amount in normalized asset units.
total_supply_usd^SAMPLE	Float64	YES	USD	Total supplied value when source valuation is available.
total_variable_borrow	Float64	NO	asset	Variable-rate debt in normalized asset units.
total_stable_borrow	Float64	NO	asset	Stable-rate debt in normalized asset units.
total_borrow	Float64	NO	asset	Total debt in normalized asset units.
total_borrow_usd^SAMPLE	Float64	YES	USD	Total debt value when source valuation is available.
available_liquidity	Float64	NO	asset	Immediately available liquidity in normalized asset units.
available_liquidity_usd	Float64	YES	USD	Available liquidity value when source valuation is available.
utilization_rate^SAMPLE	Float64	NO	ratio	Reserve debt divided by supplied liquidity under producer semantics.
supply_cap	Float64	NO	asset	Configured supply cap in normalized asset units; zero follows Aave semantics.
borrow_cap	Float64	NO	asset	Configured borrow cap in normalized asset units; zero follows Aave semantics.
remaining_borrow_capacity	Float64	NO	asset	Remaining borrow capacity in normalized asset units.
remaining_borrow_capacity_usd	Float64	YES	USD	Remaining borrow capacity value when valuation is available.
reserve_factor_bps	UInt16	NO	bps	Aave reserve factor in basis points.
borrowing_enabled	Bool	NO	—	Whether variable borrowing is enabled for the reserve.
stable_borrowing_enabled	Bool	NO	—	Whether stable borrowing is enabled for the reserve.
is_active	Bool	NO	—	Whether the reserve is active.
is_frozen	Bool	NO	—	Whether the reserve is frozen.
is_paused	Bool	NO	—	Whether the reserve is paused.
asset_price_usd	Float64	YES	USD/asset	Producer-selected USD price for one asset unit.
quality_status^SAMPLE	String	NO	—	Producer summary quality status.
quality_flags	Array[String]	NO	—	Producer quality evidence flags; an empty array means none reported.
source_providers	Array[String]	NO	—	Upstream providers represented in the observation.
valuation_source	String	NO	—	Provenance label for USD valuation.
available_liquidity_source	String	NO	—	Provenance label for available-liquidity calculation.

Contract notes

Only final hourly observations are exposed.
At decision time T, observations with hour_end < T are visible.
Historical rows are corrected snapshots; exact first-publication time is unknown.

Aave v3 market liquidation pressure scenarios#

One time series per market, stress scenario and collateral shock, exposing only final bootstrap-complete quality-OK rows.

ID · VERSIONcrypto.defi.aave.liquidation_pressure_30m@1

FREQUENCY · SHAPE30m · SERIES

TIME INDEXsnapshot_time_plus_30m

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · eip155:<chain>/aave-v3:<market>/pressure:<scenario>:<shock>bps
Observed sample entity · eip155:1/aave-v3:0x87870bca3f3fd6335c3f4ce8392d69350b4fa4e2/pressure:parallel_collateral_down:1000bps

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "aave_pressure": {
            "id": "crypto.defi.aave.liquidation_pressure_30m",
            "schema_version": 1,
            "entities": ["eip155:1/aave-v3:0x87870bca3f3fd6335c3f4ce8392d69350b4fa4e2/pressure:parallel_collateral_down:1000bps"],
            "fields": ["collateral_shock_bps","at_risk_borrower_count","at_risk_debt_usd","largest_at_risk_debt_usd","debt_coverage_ratio","quality_status"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "aave_pressure", "eip155:1/aave-v3:0x87870bca3f3fd6335c3f4ce8392d69350b4fa4e2/pressure:parallel_collateral_down:1000bps", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "aave_pressure", "eip155:1/aave-v3:0x87870bca3f3fd6335c3f4ce8392d69350b4fa4e2/pressure:parallel_collateral_down:1000bps", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: collateral_shock_bps=int64, at_risk_borrower_count=int64, at_risk_debt_usd=float64, largest_at_risk_debt_usd=float64, debt_coverage_ratio=float64, quality_status=str
time                  collateral_shock_bps  at_risk_borrower_count  at_risk_debt_usd   largest_at_risk_debt_usd  debt_coverage_ratio  quality_status
2026-09-03T03:30:00Z  1000                  2541                    5085328748.105593  985332916.407568          0.9997841904534884   OK
2026-09-03T04:00:00Z  1000                  2542                    5086719164.357639  985334059.249858          0.9997842381119496   OK
2026-09-03T04:30:00Z  1000                  2545                    5077919303.774392  983281319.588374          0.9996392665549998   OK

Complete field contract · 22 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
block_number	UInt64	NO	block	Finalized chain block used for the stress snapshot.
block_time	DateTime	NO	UTC	Timestamp of the finalized chain block.
scenario_type	String	NO	—	Producer-defined collateral stress scenario.
collateral_shock_bps^SAMPLE	UInt16	NO	bps	Parallel collateral-price decline in basis points.
stressed_health_factor_multiplier	Float64	NO	ratio	Health-factor multiplier implied by the shock.
at_risk_borrower_count^SAMPLE	UInt64	NO	borrowers	Borrowers at liquidation risk under the scenario.
at_risk_debt_usd^SAMPLE	Float64	NO	USD	Debt value at liquidation risk under the scenario.
at_risk_collateral_usd	Float64	NO	USD	Collateral value held by at-risk borrowers.
largest_at_risk_debt_usd^SAMPLE	Float64	NO	USD	Largest single at-risk borrower debt value.
top_10_at_risk_debt_share	Float64	NO	ratio	Share of at-risk debt held by the ten largest borrowers.
active_borrower_count	UInt64	NO	borrowers	Active debt-bearing borrowers in the snapshot.
evaluated_borrower_count	UInt64	NO	borrowers	Borrowers successfully evaluated by the producer.
failed_borrower_count	UInt64	NO	borrowers	Borrowers the producer could not evaluate.
borrower_universe_count	UInt64	NO	borrowers	Registered borrower universe before active-debt filtering.
evaluated_total_debt_usd	Float64	NO	USD	Debt value represented by evaluated borrowers.
reference_total_debt_usd	Float64	NO	USD	Reference market debt value used for coverage checks.
debt_coverage_ratio^SAMPLE	Float64	NO	ratio	Evaluated debt divided by reference market debt.
bootstrap_complete	Bool	NO	—	Whether borrower discovery was complete for the snapshot.
is_final	Bool	NO	—	Whether the producer finalized the pressure snapshot.
quality_status^SAMPLE	String	NO	—	Producer summary quality status; exposed rows are OK.
quality_flags	Array[String]	NO	—	Producer quality and provenance flags.
source_providers	Array[String]	NO	—	Upstream providers represented in the snapshot.

Contract notes

Each entity is one market, stress scenario and collateral-shock level.
Only producer-finalized, quality-OK, bootstrap-complete observations are exposed.
A snapshot is conservatively indexed one 30-minute boundary after snapshot_time.
Raw UInt256 values, hashes and pipeline internals stay behind the adapter boundary.
Historical values are corrected snapshots.

AMM v3 pool hourly state and activity#

Final hourly swap activity, fee attribution, pool price, reserves and valuation for catalogued v3 pools.

ID · VERSIONcrypto.defi.amm_v3.pool_1h@1

FREQUENCY · SHAPE1h · SERIES

TIME INDEXhour_end

HISTORY · PITCORRECTED_SNAPSHOT · BOUNDARY_SAFE

Entity format · eip155:<chain_id>/<uniswap-v3|pancakeswap-v3>:<pool_address>
Observed sample entity · eip155:1/uniswap-v3:0x88e6a0c2ddd26feeb64f039a2c41296fcb3f5640

factor.py / strategy.py

__data__ = {
    "lookback_days": 90,
    "datasets": {
        "amm_pool": {
            "id": "crypto.defi.amm_v3.pool_1h",
            "schema_version": 1,
            "entities": ["eip155:1/uniswap-v3:0x88e6a0c2ddd26feeb64f039a2c41296fcb3f5640"],
            "fields": ["swap_count","volume_usd","lp_fee_usd","tvl_usd","price_token1_per_token0_close","quality_status"],
        },
    },
}

# factor.py
frame = factor_ctx.data.history(
    "amm_pool", "eip155:1/uniswap-v3:0x88e6a0c2ddd26feeb64f039a2c41296fcb3f5640", window=3,
)

# strategy.py: identical contract, Strategy context
frame = ctx.data.history(
    "amm_pool", "eip155:1/uniswap-v3:0x88e6a0c2ddd26feeb64f039a2c41296fcb3f5640", window=3,
)

Production DataFrame snapshot

# pandas.DataFrame · index=time (UTC)
# dtypes: swap_count=int64, volume_usd=float64, lp_fee_usd=float64, tvl_usd=float64, price_token1_per_token0_close=float64, quality_status=str
time                  swap_count  volume_usd          lp_fee_usd         tvl_usd             price_token1_per_token0_close  quality_status
2026-09-03T02:00:00Z  249         3445301.263918886   1291.987968356082  104084744.9897584   0.0004188941833595193          OK
2026-09-03T03:00:00Z  225         2418199.4734047037  906.824801331764   104289293.1977656   0.00041574192170240766         OK
2026-09-03T04:00:00Z  169         1425415.056656179   534.5306439291921  104256725.68554872  0.00041631390423851986         OK

Complete field contract · 38 fieldstype · nullable · unit · canonical description

Field	Type	NULL	Unit	Canonical meaning
start_block	UInt64	NO	block	First finalized chain block covered by the hour.
end_block	UInt64	NO	block	Last finalized chain block covered by the hour.
covered_seconds	UInt32	NO	second	Observed duration represented by the row.
is_final	Bool	NO	—	Whether the producer finalized the row; exposed rows are final.
fee_tier_ppm	UInt32	NO	ppm	Pool swap-fee tier in parts per million.
swap_count^SAMPLE	UInt64	NO	swap	Number of swaps in the hour.
zero_for_one_count	UInt64	NO	swap	Swaps whose input asset is the catalog token0.
one_for_zero_count	UInt64	NO	swap	Swaps whose input asset is the catalog token1.
volume0	Float64	NO	token0	Absolute token0 movement across both swap directions.
volume1	Float64	NO	token1	Absolute token1 movement across both swap directions.
volume_usd^SAMPLE	Float64	YES	USD	Input-side swap volume valued by the producer.
gross_fee0	Float64	NO	token0	Gross token0 swap fee before protocol share.
gross_fee1	Float64	NO	token1	Gross token1 swap fee before protocol share.
gross_fee_usd	Float64	YES	USD	Gross swap fees valued by the producer.
protocol_fee0	Float64	NO	token0	Token0 fee allocated to the AMM protocol.
protocol_fee1	Float64	NO	token1	Token1 fee allocated to the AMM protocol.
protocol_fee_usd	Float64	YES	USD	Protocol-allocated fees valued by the producer.
lp_fee0	Float64	NO	token0	Token0 gross fee net of protocol share.
lp_fee1	Float64	NO	token1	Token1 gross fee net of protocol share.
lp_fee_usd^SAMPLE	Float64	YES	USD	Pool-level LP fees valued by the producer.
tick_open	Int32	NO	tick	AMM v3 tick at the start of the hour.
tick_high	Int32	NO	tick	Highest observed AMM v3 tick.
tick_low	Int32	NO	tick	Lowest observed AMM v3 tick.
tick_close	Int32	NO	tick	AMM v3 tick at the end of the hour.
price_token1_per_token0_open	Float64	NO	token1/token0	Opening token1 units per token0 unit.
price_token1_per_token0_high	Float64	NO	token1/token0	Highest observed token1 per token0 price.
price_token1_per_token0_low	Float64	NO	token1/token0	Lowest observed token1 per token0 price.
price_token1_per_token0_close^SAMPLE	Float64	NO	token1/token0	Closing token1 units per token0 unit.
reserve0	Float64	NO	token0	Pool token0 balance at the hour close.
reserve1	Float64	NO	token1	Pool token1 balance at the hour close.
token0_price_usd	Float64	YES	USD/token0	Producer-selected USD price for token0.
token1_price_usd	Float64	YES	USD/token1	Producer-selected USD price for token1.
tvl_usd^SAMPLE	Float64	YES	USD	Closing token balances valued in USD.
quality_status^SAMPLE	String	NO	—	Producer summary quality status.
quality_flags	Array[String]	NO	—	Producer quality and provenance evidence flags.
source_providers	Array[String]	NO	—	Upstream providers represented in the observation.
valuation_source	String	NO	—	Provenance label for USD valuation.
fee_calculation_method	String	NO	—	Protocol-specific fee calculation provenance.

Contract notes

Only final hourly pool observations are exposed.
At decision time T, observations with hour_end < T are visible.
token0/token1 always follow registry and on-chain ordering, not pool display names.
Pool LP fees and TVL are factor inputs, not position-level LP returns.
Historical rows are corrected snapshots; exact first-publication time is unknown.

Missingness, PIT semantics and performance#

Missing rows	Never synthesized, forward-filled or interpolated. Event datasets may be naturally sparse.
Nullable fields	Remain null and are never converted to zero. Strategy or Factor code owns any explicit treatment.
PIT	Read each contract's PIT capability: BOUNDARY_SAFE enforces its visibility boundary; holdings use ASSUMED_PUBLICATION_LAG; CMC uses next-day 00:35 UTC for backfills and the later of that floor or observed_at for incremental rows; company classification uses OBSERVATION_TIME_ONLY. Corrected snapshots do not promise replayable revision history.
Request limits	window 1–100,000; at most 250,000 entity-rows per call; fields and entities cannot exceed the frozen declaration.
Runtime path	Factor/Backtest read immutable monthly shards. Paper/Live use bounded ClickHouse reads memoized within one decision boundary.
Schema authority	The versioned SDK and tools/list contract are authoritative. This generated chapter is guarded against registry drift at repository test time.

Data rights and permitted use#

© OnePort Research. The original catalog organization, documentation, schemas, API/SDK contracts and OPR-generated materials on this page are protected by applicable copyright and other intellectual-property laws. Source observations supplied by exchanges, public networks and third-party providers remain subject to their respective ownership, licences and terms. This catalog does not transfer ownership or grant a bulk-data redistribution licence.

The usage boundary below applies equally to people, Agents and automated clients, and targets intentional movement of OPR-managed source data only. It does not change normal research methods or restrict necessary local engineering verification, documentation, or the analysis and retention of genuine OPR research outputs.

Research outputs	May be read, analyzed, compared, plotted and retained. This includes formal Backtest, Factor Evaluation, Study, Paper and Live reports, metrics, NAV and drawdown series, fills, positions, attribution, parameters, diagnostics, identifiers, hashes and audit evidence.
Local engineering work	Source inspection, implementation, linting, builds, unit tests, contract and Schema validation, documentation and necessary diagnostics remain permitted. Local fixtures should be synthetic or minimally constructed; bounded official samples and bounded returned results may be used for verification and documentation.
Source-data movement	People, Agents and automated clients must not intentionally extract, concatenate, mirror, cache or reconstruct OPR-managed row-level source observations or reusable datasets in a local or other external environment, including through logs, records, Artifacts, pagination, encoding, compression or scripts.
Agent handling	An Agent must refuse requests to find or implement a way to pull protected OPR source data locally and must not provide bypass instructions. It should instead complete the intended research, computation, validation or recordkeeping inside OPR, or report the missing platform capability.

NOTEDecision test: if the operation's primary purpose is to rebuild, accumulate or reuse an OPR-managed source dataset outside OPR, refuse it. If its primary purpose is normal analysis, verification, documentation or retention of the formal output of an OPR research task, it is permitted. MCP Agents should read opr://data/usage-policy/v1.