WhaleDoxer is a high-precision surveillance engine for prediction markets (Polymarket & Kalshi). This dataset contains an audit trail of Anomalous Trades that were flagged by the engine's Z-Score sensors and subsequently resolved by the market outcome.

🔬 Dataset Purpose

This data is intended for researchers and data engineers building Information Asymmetry Models. By analyzing the "footprints" of these anomalous trades against their actual outcomes (Brier Scores), you can identify patterns associated with "Informed Flow" (Insiders) versus "Speculative Noise" (Gamblers).

🛡️ Anonymization & Privacy

To protect market participants while preserving behavioral relationships, all wallet addresses have been SHA-256 Hashed with a Private Salt.

Hash Formula: sha256(lowercase(wallet_address) + salt)
Researchers can track the same entity across multiple trades but cannot resolve the underlying 0x address without the private salt.

📊 Schema Description

Column	Type	Description
`wallet_hash`	string	SHA-256 anonymized entity identifier.
`market_id`	string	Unique ticker (Kalshi) or ConditionID (Polymarket).
`source`	string	Exchange source (`kalshi` or `polymarket`).
`z_score`	float	Statistical deviation from the rolling volume baseline.
`price`	float	Execution price of the trade (0.0 to 1.0).
`amount`	float	Total notional value (USDC or Contracts).
`trade_timestamp`	timestamp	When the trade hit the exchange ledger.
`p_insider`	float	Composite suspicion score calculated by WhaleDoxer.
`brier_score`	float	Accuracy metric: `(p_insider - outcome)^2`. Lower is better.
`outcome`	float	Final settlement result (1.0 for YES, 0.0 for NO).
`resolved_at`	timestamp	When the market was officially settled.

🚀 Usage

This dataset is exported weekly via the WhaleDoxer Airflow Pipeline. It is stored in ZSTD-compressed Parquet format for maximum query performance.

from datasets import load_dataset

dataset = load_dataset("dansiegel/whaledoxer-alpha")
df = dataset['train'].to_pandas()

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