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lerchen3
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kalshi-orderbook-alpha

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Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
schema_version: int64
record_type: string
received_ts_ns: int64
received_time: string
exchange_ts_ms: null
receive_latency_ms: null
ticker: string
series_ticker: string
shard_id: int64
sid: int64
seq: int64
selection_sources: list<item: string>
  child 0, item: string
selection_volume_48h: double
ignore_qty: double
book: struct<yes: struct<bid_levels: list<item: list<item: double>>, ask_levels: list<item: list<item: dou (... 251 chars omitted)
  child 0, yes: struct<bid_levels: list<item: list<item: double>>, ask_levels: list<item: list<item: double>>, ignor (... 66 chars omitted)
      child 0, bid_levels: list<item: list<item: double>>
          child 0, item: list<item: double>
              child 0, item: double
      child 1, ask_levels: list<item: list<item: double>>
          child 0, item: list<item: double>
              child 0, item: double
      child 2, ignore_qty_bid: list<item: double>
          child 0, item: double
      child 3, ignore_qty_ask: list<item: double>
          child 0, item: double
  child 1, no: struct<bid_levels: list<item: list<item: double>>, ask_levels: list<item: list<item: double>>, ignor (... 66 chars omitted)
      child 0, bid_levels: list<item: list<item: double>>
          child 0, item: list<item: double>
              child 0, item: double
      child 1, ask_levels: list<item: list<item: double>>
          child 0, item: list<item: double>
              child 0, item: double
      child 2, ignore_qty_bid: list<item: double>
          child 0, item: double
      child 3, ignore_qty_ask: list<item: double>
          child 0, item: double
snapshot_yes_bids: list<item: list<item: double>>
  child 0, item: list<item: double>
      child 0, item: double
snapshot_no_bids: list<item: list<item: double>>
  child 0, item: list<item: double>
      child 0, item: double
market_count: int64
markets: list<item: struct<ticker: string, series_ticker: string, sources: list<item: string>, volume_48h: do (... 6 chars omitted)
  child 0, item: struct<ticker: string, series_ticker: string, sources: list<item: string>, volume_48h: double>
      child 0, ticker: string
      child 1, series_ticker: string
      child 2, sources: list<item: string>
          child 0, item: string
      child 3, volume_48h: double
to
{'schema_version': Value('int64'), 'record_type': Value('string'), 'received_ts_ns': Value('int64'), 'received_time': Value('string'), 'market_count': Value('int64'), 'markets': List({'ticker': Value('string'), 'series_ticker': Value('string'), 'sources': List(Value('string')), 'volume_48h': Value('float64')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              schema_version: int64
              record_type: string
              received_ts_ns: int64
              received_time: string
              exchange_ts_ms: null
              receive_latency_ms: null
              ticker: string
              series_ticker: string
              shard_id: int64
              sid: int64
              seq: int64
              selection_sources: list<item: string>
                child 0, item: string
              selection_volume_48h: double
              ignore_qty: double
              book: struct<yes: struct<bid_levels: list<item: list<item: double>>, ask_levels: list<item: list<item: dou (... 251 chars omitted)
                child 0, yes: struct<bid_levels: list<item: list<item: double>>, ask_levels: list<item: list<item: double>>, ignor (... 66 chars omitted)
                    child 0, bid_levels: list<item: list<item: double>>
                        child 0, item: list<item: double>
                            child 0, item: double
                    child 1, ask_levels: list<item: list<item: double>>
                        child 0, item: list<item: double>
                            child 0, item: double
                    child 2, ignore_qty_bid: list<item: double>
                        child 0, item: double
                    child 3, ignore_qty_ask: list<item: double>
                        child 0, item: double
                child 1, no: struct<bid_levels: list<item: list<item: double>>, ask_levels: list<item: list<item: double>>, ignor (... 66 chars omitted)
                    child 0, bid_levels: list<item: list<item: double>>
                        child 0, item: list<item: double>
                            child 0, item: double
                    child 1, ask_levels: list<item: list<item: double>>
                        child 0, item: list<item: double>
                            child 0, item: double
                    child 2, ignore_qty_bid: list<item: double>
                        child 0, item: double
                    child 3, ignore_qty_ask: list<item: double>
                        child 0, item: double
              snapshot_yes_bids: list<item: list<item: double>>
                child 0, item: list<item: double>
                    child 0, item: double
              snapshot_no_bids: list<item: list<item: double>>
                child 0, item: list<item: double>
                    child 0, item: double
              market_count: int64
              markets: list<item: struct<ticker: string, series_ticker: string, sources: list<item: string>, volume_48h: do (... 6 chars omitted)
                child 0, item: struct<ticker: string, series_ticker: string, sources: list<item: string>, volume_48h: double>
                    child 0, ticker: string
                    child 1, series_ticker: string
                    child 2, sources: list<item: string>
                        child 0, item: string
                    child 3, volume_48h: double
              to
              {'schema_version': Value('int64'), 'record_type': Value('string'), 'received_ts_ns': Value('int64'), 'received_time': Value('string'), 'market_count': Value('int64'), 'markets': List({'ticker': Value('string'), 'series_ticker': Value('string'), 'sources': List(Value('string')), 'volume_48h': Value('float64')})}
              because column names don't match

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Kalshi Live Orderbook Alpha Feed

This public dataset contains live Kalshi order-book snapshots and derived state after every orderbook_delta message for a research universe refreshed every 10 minutes.

The current universe is the 200 highest-volume open markets over a rolling 48-hour public-trade window. It is spread by volume across four websocket connections of 50 markets each, then periodically rebalanced using observed per-market delta traffic.

Early files used an expanded allowlist/blocklist union. Per-record selection_sources and universe records identify that historical scope.

Files

Data is stored as gzip-compressed JSON Lines under data/YYYY/MM/DD/kalshi_orderbooks_*.jsonl.gz. Files are uploaded only after they are closed and compressed.

schema_version=1 records have one of three record_type values:

snapshot: the initial full YES and NO bid ladders plus derived features.
delta: the raw changed side, price, and quantity plus derived state after applying the delta.
universe: the selected ticker universe and selection reason at a discovery boundary.

For both YES and NO, book contains the top three displayed bid levels, the top three asks derived from the opposite outcome's bids, and the best bid and ask whose displayed level quantity is strictly greater than 1,000 contracts. Prices are in cents and quantities can be fractional.

Timestamps include local receipt nanoseconds, Kalshi's exchange timestamp when present, and their observed difference. Network, scheduling, and processing latency mean the feed is not a colocated ground-truth record.

This is public market-data collection for research, not investment advice.

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