Datasets:

dataforge-labs
/

crypto-options-surface

@@ -1,104 +1,97 @@
 ---
 license: odc-by
-pretty_name: "Implied volatility surface for on-chain crypto options"
 tags:
-  - options
-  - implied-volatility
-  - derivatives
-  - greeks
-  - order-book
-  - bitcoin
-  - ethereum
-  - solana
-  - cryptocurrency
-  - time-series
 task_categories:
-  - time-series-forecasting
 size_categories:
-  - 1M<n<10M
 ---
-# Crypto options implied volatility surface
-Every listed option on one venue, priced, with its greeks, sampled through the day.
-An option quote is computed on demand and kept by nobody. The chain that existed at any past
-moment is not recoverable from the venue or from the chain it settles on: only trades leave a
-record, and most of these strikes never trade.
 ## Contents
-| name | one row is |
 |---|---|
-| `e22_options_surface` | one instrument at one moment: strike, expiry, mark, forward, and the full greek set |
-| `e22_options_book` | top of book for a near-the-money ladder: bid, ask, their sizes, and both quoted in vol terms |
-## Reading it
-`iv`, `delta`, `gamma`, `vega`, `theta` and `rho` are the venue's own values, published
-alongside its mark price rather than recomputed here. That is deliberate: re-deriving implied
-volatility needs a rate and dividend assumption, and a number the venue itself margins against
-is the more useful one.
-Each asset's chain is fetched in one request and every row from it shares a `sampled_ts`,
-so grouping on `(asset, sampled_ts)` recovers one surface and adding `expiry` recovers one
-smile. `round_ts` is shared by the assets collected in the same pass, which is what to
-group on when comparing across assets at a moment.
-`strike` and `expiry` are parsed from the instrument name, and both are null where the name
-does not match the expected shape rather than being guessed. `expiry_ts` comes from the venue.
-## What the mark costs you
-The surface carries the venue's mark. `e22_options_book` carries what was actually quoted:
-`best_bid_iv` and `best_ask_iv` give the two sides in vol terms and `iv_spread` the distance
-between them. Across one ladder that distance ran from 4.2 to 18.6 vol points, and the mark sat
-inside the band every time, near the middle rather than at it.
-The ladder is deliberate rather than exhaustive, because a book costs one request per
-instrument. It takes the strikes nearest the forward across several expiries and skips the
-expiring contract, which was measured to have no book at all. So `e22_options_book` covers a
-slice of `e22_options_surface` and never all of it; the two join on `instrument_name` within a
-round.
-A one-sided book is a real state and is recorded as one, with the missing side null while
-`error` stays empty. `error` is set only where the request itself failed.
-## Before you build on this
-- One venue, and not the largest one. This is what a single order book quoted, not a
-  market-wide consensus. Anything inferred about crypto volatility generally needs a second
-  source.
-- Every listed instrument appears, including deep out-of-the-money strikes that never trade.
-  A mark price is published for those too, so filter before treating the surface as tradeable.
-- Put and call at the same strike and expiry carry the same implied volatility by construction.
-  Two rows agreeing is parity, not confirmation.
-- Sampled at an interval, so a move that reverses between samples is invisible. `sampled_ts`
-  is ours, taken at observation, and carries our network distance to the venue.
-- A fetch that fails writes one explicit error row for that asset with the measurements null.
-  Check `error` before reading an absent chain as a delisting.
-- Coverage per asset differs and changes as the venue lists and expires instruments; count
-  distinct `expiry` per day rather than assuming a fixed ladder.
-Partitions are parquet, one file per collection window, under `dataset/YYYY/MM/`. Every dataset here carries a FIXED 7-day sample WINDOW starting at its own first day of collection, together spanning 2026-08-29 to 2026-09-04, so you can check schema, coverage and quality before asking for more. It does not advance, so there is nothing to gain by re-downloading it. The full history is held privately, available on request.
 ```python
-from huggingface_hub import snapshot_download
-import pandas as pd, glob
-path = snapshot_download("dataforge-labs/crypto-options-surface", repo_type="dataset",
-                         allow_patterns="e22_options_surface/**")
-df = pd.concat(map(pd.read_parquet,
-                   glob.glob(f"{path}/e22_options_surface/**/*.parquet", recursive=True)))
 ```
 ## Coverage
-`e0_run_manifest` lists every collection window with its poll counts and failure counts, and is
-published in full rather than windowed. Gaps between windows are real, cannot be filled in
-afterwards, and nothing here is interpolated.
 ## License and contact
-ODC-BY: use it freely, credit "DataForge (dataforge-labs)". Questions and requests for the full
-history via the discussions tab.

 ---
 license: odc-by
+pretty_name: Crypto options marks, implied volatility and order books
 tags:
+- options
+- implied-volatility
+- derivatives
+- greeks
+- order-book
+- bitcoin
+- ethereum
+- solana
+- cryptocurrency
+- time-series
 task_categories:
+- time-series-forecasting
 size_categories:
+- 1M<n<10M
+configs:
+- config_name: options_surface
+  data_files:
+  - split: train
+    path: options_surface/**/*.parquet
+  default: true
+- config_name: options_order_book
+  data_files:
+  - split: train
+    path: options_order_book/**/*.parquet
+- config_name: collection_runs
+  data_files:
+  - split: train
+    path: collection_runs/**/*.parquet
 ---
+# Crypto options surface and order books
+Snapshots of listed options on Aevo, including marks, implied volatility, Greeks and a selected set of order books. The tables support analysis of volatility surfaces and the relationship between published marks and quoted prices.
 ## Contents
+| Table | Record |
 |---|---|
+| `options_surface` | An instrument's strike, expiry, mark, forward, implied volatility and Greeks |
+| `options_order_book` | Best bid and ask, available sizes and quoted implied volatility for a selected instrument |
+## Using the data
+The venue supplies `iv`, `delta`, `gamma`, `vega`, `theta` and `rho`; these values are not recalculated by the collector. Group on `(asset, sampled_ts)` to reconstruct a sampled surface, then add `expiry` to examine a volatility smile. `round_ts` groups assets fetched during the same collection pass, although requests need not complete simultaneously.
+The order-book panel covers a near-the-money strike ladder across selected expiries. Join it to the surface on `instrument_name` within the same round. `best_bid_iv`, `best_ask_iv` and `iv_spread` describe the quoted volatility range.
+`strike` and `expiry` are parsed from instrument names and remain null when parsing fails. `expiry_ts` is supplied by the venue.
+## Limitations
+- The dataset covers one venue. A listed mark does not establish liquidity or an executable price.
+- Order books cover a subset of the surface. Check both available size and spread before using a quote in execution analysis.
+- A one-sided book has a null missing side. Request failures are recorded separately in `error`.
+- Sampling misses changes between observations, and collection timestamps include network latency.
+- Put and call implied volatilities at the same strike and expiry may reflect the venue's shared pricing model; agreement is not independent validation.
+## Files and access
+Data is stored as Parquet files under `table_name/YYYY/MM/`, with partitions for collection windows. Each measurement table has a fixed 7-day sample beginning at its configured collection start date. The sample windows in this repository span 2026-08-29 to 2026-09-04. Availability within each window depends on successful collection. The public sample dates remain fixed as additional history accumulates privately. Contact DataForge through the discussions tab to enquire about additional history.
+### Load a table
+Install `datasets` and `pandas` to run this example. The `train` split contains all observations in the selected table; it is not a predefined modelling split.
 ```python
+from datasets import load_dataset
+data = load_dataset("dataforge-labs/crypto-options-surface",
+                    "options_surface", split="train")
+df = data.to_pandas()
 ```
 ## Coverage
+`collection_runs` records collection windows, poll counts and failures. It is published in full and may cover dates beyond the fixed data sample. Collection gaps are not interpolated. Use this table together with measurement timestamps and error fields to assess coverage.
 ## License and contact
+The public sample is published under ODC-BY. Attribute it to "DataForge (dataforge-labs)". For questions about the data or access to additional history, open a discussion in this repository.
+<details>
+<summary>Earlier file paths</summary>
+Each table is stored under a directory with the same descriptive name. The file contents and date partitions are unchanged. Scripts using an earlier directory name should use the corresponding table name below, or pin downloads to revision `before-folder-rename-20260915` to access the original layout. Internal collector IDs are retained in the private archive and may appear in raw coverage records.
+| Current table and directory | Earlier directory |
+|---|---|
+| `options_surface` | `e22_options_surface/` |
+| `options_order_book` | `e22_options_book/` |
+| `collection_runs` | `e0_run_manifest/` |
+</details>

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