Daily build 2026-08-24

README.md

@@ -39,8 +39,8 @@ configs:
 # Polymarket Order Book Dataset
 
 Order-book snapshots from a prediction market, collected continuously between
-**2026-07-10** and **2026-08-22**: `307,493,410` quote observations across
-`175,808` markets, plus settlement outcomes and a separate high-frequency feed
+**2026-07-10** and **2026-08-23**: `316,243,312` quote observations across
+`177,095` markets, plus settlement outcomes and a separate high-frequency feed
 that records actual traded prices.
 
 It is published so other people can build and train on it without first spending
@@ -72,16 +72,16 @@ import duckdb
 duckdb.sql("SELECT * FROM 'polymarket-data/quotes/**/*.parquet' LIMIT 5").show()
 ```
 
-**GitHub Releases** (a single dated tarball, ~160 MB):
+**GitHub Releases** (a single dated tarball, ~163 MB):
 
 ```bash
-gh release download data-2026-08-23 --repo DineshKumar8399/polymarket-orderbook-dataset
+gh release download data-2026-08-24 --repo DineshKumar8399/polymarket-orderbook-dataset
 tar --zstd -xf polymarket-orderbook-*.tar.zst
 ```
 
-Each release is a frozen snapshot, so `data-2026-08-23` is reproducible: cite the
+Each release is a frozen snapshot, so `data-2026-08-24` is reproducible: cite the
 tag and anyone can reconstruct the exact data you trained on. Latest build:
-**2026-08-23**.
+**2026-08-24**.
 
 ---
 
@@ -89,13 +89,13 @@ tag and anyone can reconstruct the exact data you trained on. Latest build:
 
 | File | Rows | What it is |
 |---|---|---|
-| `quotes/dt=YYYY-MM-DD/*.parquet` | `307,493,410` | Book quotes for every tracked market, partitioned by date |
-| `markets.parquet` | `175,808` | One row per market: question text, category, coverage |
-| `labels.parquet` | `156,198` | Binary settlement outcomes, with a `source` column |
-| `watch_quotes.parquet` | `1,841,883` | High-frequency feed — **the only table with traded prices** |
+| `quotes/dt=YYYY-MM-DD/*.parquet` | `316,243,312` | Book quotes for every tracked market, partitioned by date |
+| `markets.parquet` | `177,095` | One row per market: question text, category, coverage |
+| `labels.parquet` | `157,328` | Binary settlement outcomes, with a `source` column |
+| `watch_quotes.parquet` | `1,891,971` | High-frequency feed — **the only table with traded prices** |
 | `data_quality.parquet` | 7 | The known issues below, as queryable rows |
 
-Total: about `160 MB` of ZSTD-compressed Parquet.
+Total: about `163 MB` of ZSTD-compressed Parquet.
 
 ---
 
@@ -174,7 +174,7 @@ also the cost per unit of payoff.
 | `first_ts` / `last_ts` | timestamp | Coverage window |
 
 `question` is stored here rather than on every quote row — repeating it
-`307,493,410` times is most of why the raw CSV was 32 GB.
+`316,243,312` times is most of why the raw CSV was 32 GB.
 
 ### `labels.parquet`
 
@@ -252,7 +252,7 @@ has been **dropped rather than shipped as an empty column named `last`**.
 
 If your question is "did this actually transact" — fill realism, execution
 modelling, print-versus-quote — it is only answerable on `watch_quotes`, which
-covers 5,311 markets rather than 175,808. Note `last_traded` is itself 83.6%
+covers 5,415 markets rather than 177,095. Note `last_traded` is itself 83.7%
 populated, not 100%.
 
 ### 4. Crossed books
@@ -264,8 +264,8 @@ your method is sensitive to it.
 ### 5. Labels are time-censored
 
 Most `source = 'api'` labels come from a one-off backfill run on 2026-07-23/24.
-So "has a label" correlates strongly with "settled before Jul 24" — 151,655
-markets (86%) carry an authoritative label, and they are **not a random 86%**.
+So "has a label" correlates strongly with "settled before Jul 24" — 155,179
+markets (88%) carry an authoritative label, and they are **not a random 88%**.
 
 This bites hardest on walk-forward validation: naively splitting train/test on a
 late date can leave you with an empty test set and a script that reports success
@@ -299,9 +299,9 @@ Markets by category:
 
 ```
    category  slugs
-     sports 171308
-   politics   2124
-    culture    993
+     sports 172148
+   politics   2565
+    culture    999
     climate    977
       macro    154
  technology     93

labels.parquet

@@ -1,3 +1,3 @@
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-oid sha256:33bc1893bd8cf97aab2eaadf04d478d6895f658d2e4bb5b85424d582e4af3a4c
-size 612477
+oid sha256:fc88f66cac16a76ecf87da2e3d57fffeecf08737cf79b9939f464dca4cd5af02
+size 613097

markets.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b9b054051d94afb196d2693d59b6206bec44b0afc171e955b736b8c6f1e3dc50
-size 2353369
+oid sha256:3b81c3cf821272ff86200e9cbe64a47ad5ce2625691b654a31b8b42e90e0d082
+size 2368520

quotes/dt=2026-08-23/data_0.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:757b67bc43be427b53107898392d76de8bb7dca3b516c057496b1c13f2660c78
+size 2513129

watch_quotes.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f378e3d53fc5d20f937bccc5e5dfd3aad16ae65131da547705ffcf5bf485de73
-size 5929357
+oid sha256:e6085e287a2b1f1b45b1dca82378163dd2a05c88276ba965738b3890642bcbed
+size 6088727