README.md

Oracle Disagreement Labels — Polymarket vs Kalshi 15-min crypto windows

Training and evaluation data for predicting when the two independent oracles settling a cross-platform hedged crypto binary trade will disagree on direction, causing a double-loss event.

• Platforms: Polymarket (settles via Chainlink Data Feeds) and Kalshi (settles via CF Benchmarks real-time index, e.g. BRTI for BTC).
• Asset universe: BTC, ETH, SOL, XRP.
• Window length: 15 minutes, aligned to UTC :00, :15, :30, :45.
• Data range: 2026-04-14 → 2026-04-22 (8 days of labels; dense samples 2026-04-19 → 2026-04-22 only, Phase 1 instrumentation shipped on Apr 19).
• Base disagreement rate: 5.7% across assets; 6.1% BTC / 3.9% ETH / 7.2% SOL / 5.6% XRP.

Problem statement

For each 15-minute window, predict the binary target:

disagreement = (poly_settled == 'UP') != (kalshi_settled == 'UP')

from features observable at T = window_close − k seconds, where k ∈ [60, 300].

Economics drive the task, not accuracy:

• Catching a real disagreement saves ~$12.23 on BTC, ~$4.13 on SOL, ~$4.86 on XRP, ~$3.44 on ETH (per-asset average at current position sizing).
• A false positive costs ~$1.00 (round-trip exit friction: fees + bid-ask).
• Breakeven precision: FP / (FP + TP) — so ~8% on BTC, ~20% on ETH.
• Recall matters ~15× more than precision at BTC sizing.

The production baseline rule coinbase_dist_bp < 4 OR sign_flip(binance − poly_strike, coinbase − kalshi_floor) at T−60s on BTC gives PPV 24%, Recall 82%, EV +$592 over 20 days (in-house backfill validation; see ship target in source repo docs).

Files

File	Rows	Purpose
windows.parquet	2,781	One row per labeled window. Target: disagreement.
dense_samples.parquet	28,408	10-s-cadence Binance + Coinbase spot prices for each window's final 5 min.
orderbook_snapshots.parquet	15	Per-minute book state (bid/ask/depth each side each platform) for in-flight hedged trades. Sparse — instrumentation shipped 2026-04-21.
trades.parquet	407	Live-traded rows with realized P&L. Useful for EV-per-window validation.
stats.json	—	Row counts, per-asset base rates, date range.

windows.parquet

Column	Type	Notes
asset	str	btc / eth / sol / xrp
window_start	timestamp[UTC]	Window open time
window_end	timestamp[UTC]	Window close time (target timestamp)
window_ts	int64	Unix-seconds of window_start; primary join key
oracle_agreed	bool	Raw label (True = same direction on both oracles)
disagreement	bool	Target — NOT oracle_agreed
poly_settled	str	UP or DOWN per Chainlink
kalshi_settled	str	UP or DOWN per CF Benchmarks
kalshi_expiration_value	float	CF Benchmarks index value at close
poly_ptb	float	Poly strike (price-to-beat, scraped)
poly_rtds	float	Poly RTDS-reported strike (may differ from poly_ptb by ~$0.16 for ETH; see notes)
kalshi_floor	float	Kalshi strike
strike_gap	float	poly_ptb − kalshi_floor in absolute units
ptb_source	str	Where the strike came from (PTB scraper vs RTDS vs fallback)
delta_ptb_vs_rtds	float	Cross-check delta
settled_at	timestamp[UTC]	When the row was finalized

dense_samples.parquet

Per-observation spot prices. Join to windows on window_ts + asset.

Column	Type	Notes
asset	str
ts	timestamp[UTC]	Sample timestamp
window_id	str	Format: {asset}-{window_ts}
window_ts	int64	Join key
trade_id	str | null	Set when an active hedged trade was in this window
binance	float	Binance spot, Chainlink proxy
coinbase	float	Coinbase spot, CF Benchmarks proxy
poly_strike	float	Chainlink-aligned strike
kalshi_floor	float	CF-Benchmarks-aligned strike
secs_to_close	int	Seconds from sample to window close
sample_age_ms	int	Staleness of the spot fetch
coinbase_dist_bp	float	(coinbase − kalshi_floor) / kalshi_floor × 10000 — signed
binance_dist_bp	float	Same, Binance side
sign_flip	int8	1 if Binance and Coinbase are on opposite sides of their respective strikes
cross_spot_bp	float	`

Cadence: ~10 seconds per sample, only during the final 300 seconds of each window (Phase 1 sampler design). Four assets sampled in parallel.

orderbook_snapshots.parquet

Best bid/ask and depth from both platforms for in-flight hedged trades, emitted once per 60 seconds in the final 5 minutes. Sparse because the logger shipped 2026-04-21 and has only been on for ~12 hours at dataset build time.

Columns: asset, trade_id, window_id, ts, secs_to_close, plus {poly,kalshi}_{yes,no}_{bid,ask,depth}.

trades.parquet (sanitized)

This file has been sanitized for external research use. All fill prices, position sizes, Kelly fractions, fees, sides, and dollar P&L have been removed. What remains is derivable from public market data plus two anonymous outcome booleans.

Column	Type	Notes
id	uuid	Opaque row identifier
matched_event_id	str	crypto-<asset>-15m-<asset>-updown-15m-<window_ts>
asset	str	Derived from matched_event_id
window_ts	int64	Join key matching windows.window_ts
oracle_agreed	bool	Label for this trade's window
created_at	timestamp[UTC]	Entry time (reveals minute_in_window but that's also explicit below)
resolved_at	timestamp[UTC]	Settlement time
poly_strike	float	Strike at entry (public)
kalshi_strike	float	Strike at entry (public)
spot_at_trade	float	Binance spot at entry (public)
binance_spot	float	Binance spot at entry (public)
coinbase_spot	float	Coinbase spot at entry (public)
strike_gap_pct	float	Derived: (poly_strike − kalshi_strike) / kalshi_strike
flat_candle_move_pct	float	Derived: spot movement since window open
minute_in_window	int	0–14, minute at which entry occurred
binance_coinbase_divergence_pct	float	Cross-exchange spread at entry
was_win	bool	Anonymous: did this trade resolve profitably?
was_double_loss	bool	Anonymous: was this a double-loss event?

What's useful in this file:

• Identify which windows were traded (window_ts) for selection-bias analysis.
• Compare model fire-points against real traded windows.
• Cross-check that was_double_loss ⊆ NOT oracle_agreed (sanity).
• Aggregate win rate for validating the EV formula.

Per-asset trade counts: BTC 176, ETH 95, XRP 68, SOL 68. Overall win rate 89.19%, double-loss rate 10.81%.

What's NOT in this file (and why):

• Fill prices, sizes, fees, P&L, Kelly fraction — all stripped to avoid exposing the bot's live execution characteristics. If you need ground-truth EV numbers, use the per-asset aggregates in the project docs (avg DL cost: BTC −$12.23, ETH −$3.44, SOL −$4.13, XRP −$4.86; avg FP cost ~$1.00).
• Sides (YES/NO per platform) — not needed for disagreement prediction (the target is window-level, not side-level) and would reveal our direction-selection logic.

Recommended splits

• Walk-forward: train on rolling N=5 days, test on next M=1 day. No single fixed split — our live signal drifts with volatility regimes and platform fee structure. The 2026-04-21 windows are the most recent and should dominate the test set in every fold.
• Never mix traded and non-traded windows within a fold. Traded windows (matched by trade_id in dense_samples) are a biased subset — the entry guards specifically select near-strike windows. Use non-traded windows for training signal, traded windows for out-of-sample realism checks.

Known gotchas

1. poly_rtds is unreliable on ETH: RTDS has been measured ~$0.16 off vs the PTB scraper's ground truth. Prefer poly_ptb when both are present; fall back to poly_rtds + sanity-check gap only if PTB missing.
2. Phase 1 dense sampling started 2026-04-19. Windows before that date have labels but no dense_samples rows. Your dense-feature model is trained + evaluated only on 2026-04-19 onward.
3. Pre-rotation log data is not in the bundle. The rotated .zst logs from earlier dates contain only pre-Phase-1 output without ORACLE_SAMPLE lines, so re-running the ingestion script won't recover earlier dense data. Historical reconstruction requires pulling raw exchange tick data and simulating the Phase 1 sampling cadence.
4. Sampling coverage varies. If either Binance or Coinbase fetch failed at a tick, the sample is emitted with a null field. Impute or drop as your model requires; current production rule drops.
5. Strike uniqueness: strike_snapshots can have duplicate rows per (asset, window_ts). The build script picks the first occurrence; a more principled approach (earliest-vs-latest) is an ablation point.
6. Labels are settlement-truth, not prediction-time truth. The kalshi_expiration_value in the label is the CF Benchmarks value at close, which can differ from the same index's value at T-60s by several basis points. Do NOT use kalshi_expiration_value as a feature — it leaks the target.

Reproduction

This dataset was generated by:

python3 scripts/build_oracle_disagreement_dataset.py

against the production Supabase database and rotated log files. Source is in the repo under scripts/build_oracle_disagreement_dataset.py.

License

Internal research data. Do not redistribute outside the Anthropic / polymarket-arb project without permission.

Baseline EV numbers to beat

Horizon	Rule	PPV	Recall	EV per 20d (BTC only)
T−60s	coinbase_dist<4 OR sign_flip	24%	82%	+$592
T−120s	same rule	18%	64%	+$219
T−300s	same rule	11%	52%	+$89

Ship target: PPV ≥ 35% at recall ≥ 70% at T−60s, AND PPV ≥ 13% at recall ≥ 60% at T−5 min on held-out walk-forward validation.

Contact / questions

See docs/architecture/oracle_disagreement_exit_guard.md in the source repo (especially §13–14) for the full backtest methodology and ruled-out features.