danjsiegel/whaledoxer-stream

Whale-Stream Telemetry

This dataset contains 6-hour rolling telemetry exports from the WhaleDoxer pipeline. For more context on the background of this project check out the substack article I wrote: https://danjsiegel.substack.com/p/building-a-sub-second-middle-finger

Each file is a time-sliced Parquet export of recent tripwire events, joined to paper-trade scoring data when a paper trade was actually written. It is intended to answer operational questions such as:

• is the pipeline alive
• how large were the detected anomalies
• how long did the hot path and downstream scoring take
• when a paper trade was written, what composite score and scorer breakdown were attached

Files are appended every 6 hours as separate Parquet objects:

• data/whale_stream_YYYY-MM-DDTHH00Z.parquet

Example:

• data/whale_stream_2026-03-06T1200Z.parquet

What This Dataset Contains

This export is built from the tripwires table for the last 6 hours, filtered to rows where z_score > 0, with optional columns pulled from paper_trades when a paper trade exists for the same trigger trade.

This is not the full raw trade stream. It is a rolling export of tripwire telemetry plus any attached paper-trade scoring data.

Data Source and Export Semantics

• Source window: last 6 hours at export time
• Export cadence: every 6 hours
• Storage format: Parquet with Zstandard compression
• Join shape:
  • base table: tripwires
  • optional enrichment: paper_trades
  • optional market titles: Kalshi and Polymarket market catalogs

Privacy and Anonymization

Wallet addresses are anonymized in Python before export:

• wallet_hash = sha256(lower(wallet) + salt)

The following values are emitted as null instead of being hashed:

• empty strings
• anonymous_lookup_required
• AMM_Liquidity_Pool
• values starting with kalshi::
• any non-0x identifier

This preserves continuity for real on-chain addresses without inventing fake identities for anonymous or synthetic actors.

Schema

Column	Type	Description
wallet_hash	string | null	SHA-256 hash of a lowercased on-chain wallet address. Null for sentinels and non-0x identities.
event_timestamp	timestamp	Exchange-reported timestamp for the triggering trade event.
source	string	Exchange source: kalshi or polymarket.
market_id	string	Exchange market identifier: Kalshi ticker or Polymarket condition ID.
market_title	string | null	Human-readable title from the Kalshi or Polymarket market catalog when available.
z_score	double	Volume anomaly z-score at tripwire fire time.
sniper_latency_ms	double | null	Milliseconds from WebSocket receipt to tripwire persistence on the sniper side.
p_insider	double | null	Composite suspicion score from the downstream forensics scorer. Null when no paper trade was written.
trigger_latency_ms	double | null	End-to-end latency from the triggering trade timestamp to paper-trade write time. Null when no paper trade was written.
scorer_breakdown_json	string | null	JSON text copied from paper_trades.scorer_breakdown when a paper trade exists.
sniper_received_at_ms	int64 | null	Unix milliseconds at trade handler entry on the sniper side.
triage_completed_at_ms	int64 | null	Unix milliseconds immediately after sniper-side anomaly processing completed.
tripwire_enqueued_at_ms	int64 | null	Unix milliseconds immediately before the tripwire was dispatched downstream.
triage_latency_ms	double | null	Derived latency: triage_completed_at_ms - sniper_received_at_ms.
enqueue_latency_ms	double | null	Derived latency: tripwire_enqueued_at_ms - triage_completed_at_ms.

Notes

• p_insider, trigger_latency_ms, and scorer_breakdown_json are nullable because not every tripwire becomes a paper trade.
• scorer_breakdown_json is exported as JSON text in Parquet, not as a nested Arrow or DuckDB VARIANT type.
• This dataset is best understood as operational and forensic telemetry, not as the full universe of exchange trades.

Loading Example

from datasets import load_dataset

ds = load_dataset("dansiegel/whale-stream-telemetry")
df = ds["train"].to_pandas()

# Rows that actually produced a paper-trade scoring decision
scored = df[df["p_insider"].notna()]