Naly Engineering Notes: Polymarket Gamma as a Reproducible Market Snapshot Layer

TL;DRPolymarket Gamma ingestion gives Naly a public, structured view of prediction-market events, markets, outcomes, prices, tags, and resolution metadata. Naly uses that layer to choose article candidates, anchor mispricing roundups, support KBO prediction stories, preserve source citations, and create durable market snapshots for later verification. The engineering claim is simple: editorial trust starts with reproducible market input, not generated prose.

Abstract

The Polymarket Gamma API should be treated as Naly's market-discovery and metadata source, not as a trading interface. Official Polymarket documentation separates Gamma from Data and CLOB APIs: Gamma covers events, markets, tags, series, sports metadata, search, comments, and public profiles, while CLOB handles order books, prices, spreads, and authenticated trading operations. For Naly articles, that boundary is useful. Most editorial workflows need question text, market identity, outcome labels, displayed probabilities, category tags, deadlines, and resolution context before they need tick-level order-book detail.

The thesis of this note is that Naly should ingest Gamma into a versioned market snapshot layer. A snapshot is not the truth; it is the observed public market state at a specific time, tied to source URLs and preserved raw payloads. That lets Naly publish what others hide: the market context, the limits of the signal, and the exact evidence used when an article was generated.

Where it sits in Naly

Gamma ingestion sits before article generation and after external source discovery. In Naly's publishing path, it provides the prediction-market side of the evidence bundle for four downstream uses:

Mispricing roundups need current event and market candidates, with enough metadata to compare market-implied probabilities against source-backed reasoning.
KBO prediction articles need sports markets, team or event tags, event deadlines, and outcome labels that can be rendered without hand-entered market context.
Source citations need stable market URLs, event slugs, and market identifiers so a reader can audit why the article discussed a specific contract.
Prediction verification needs historical snapshots so later resolution can be scored against the probability Naly actually saw at publish time.

The local runtime facts matter mostly for shape, not novelty. TypeScript 5.9 and tsx are a reasonable fit for ingestion scripts; drizzle-orm with @neondatabase/serverless can persist normalized records and raw JSON; Next.js 16 and React 19 can render the resulting article pages; the ai and Claude SDK packages can consume the prepared evidence bundle only after ingestion has made the market state explicit.

Technical mechanism

A robust Gamma ingestion loop has six stages.

Discovery. Use Gamma events when Naly needs broad active-market discovery, and use market or event slugs when an article is anchored to a known Polymarket URL. Polymarket's market-data guide recommends events for complete active discovery because events include associated markets, reducing API calls. For category work such as sports, tags and sports metadata should narrow the crawl before ranking candidates.
Pagination. Gamma supports both offset-style endpoints and keyset endpoints. For large or repeated crawling, keyset pagination is safer because next_cursor becomes the continuation token and offset is explicitly rejected on the keyset markets endpoint. For smaller targeted article jobs, offset pagination can still be acceptable when the query window is narrow and the output is immediately snapshotted.
Normalization. The key normalization rule is that outcome labels and outcome prices must be mapped by position. Polymarket's market-data overview documents outcomes and outcomePrices arrays as one-to-one arrays, so index discipline matters. A swapped array is not a cosmetic bug; it reverses the article's market interpretation.
Snapshotting. Store both normalized columns and raw JSON. Normalized columns should include provider, fetched timestamp, event id or slug, market id or slug, question text, condition id when present, token identifiers when present, outcome labels, outcome prices, active and closed flags, end time, tags, source URL, and payload hash. Raw JSON protects Naly against schema drift and lets verification replay the exact ingestion artifact.
Candidate scoring. Article jobs should rank markets using editorial criteria, not trading execution criteria: freshness, volume or liquidity where available, resolution proximity, source availability, category fit, and whether the market can produce a clear reader-facing contrast. Candidate scoring should never imply that a market price is automatically wrong; it should mark a market as worth researching.
Publication binding. When a generated article cites a market, the article should link to the stored snapshot id, the Polymarket source URL, and the external evidence set used by the writer. This is the difference between a page that says the market was 62 percent and a page that can prove when, where, and from what payload that value came.

Rate limits are part of the mechanism. Polymarket documents Cloudflare-enforced throttling and specific Gamma limits for /events, /markets, /tags, and /public-search. Naly does not need to approach those limits for daily publishing, so the correct posture is conservative: bounded concurrency, retry with jitter, payload hashes for idempotency, and alerting when a job receives throttled, empty, or structurally unexpected responses.

What the literature says

Recent arXiv work supports the snapshot-first design. Jia et al. describe decentralized prediction-market data as fragmented across off-chain metadata, fill-level trading records, and oracle-resolution events, then argue for a unified relational system with identifier resolution, incremental updates, and consistency mechanisms. Naly's smaller publishing system has the same class of problem: market metadata alone is not enough unless it can later be joined to prices, article claims, and resolution outcomes.

Le's calibration study is directly relevant to article language. Using large Kalshi and Polymarket datasets, the paper argues that calibration depends on domain, horizon, and trade-size effects; consumers who treat prices as face-value probabilities can systematically misinterpret them. For Naly, this means an article should say market-implied probability, observed price, or public market signal, not truth. The article can discuss a possible mispricing only after separating the raw market observation from Naly's model or source-backed reasoning.

Dubach's Polymarket microstructure paper is mostly about order-book data, but it gives a useful warning for ingestion design: high-frequency market evidence has source-specific error modes, ingestion delays, and joins that matter. Gamma is less granular than order-book feeds, yet the same principle applies. The source of a number must be visible. A Gamma snapshot, a CLOB midpoint, and an on-chain trade record are related but not interchangeable.

Madrigal-Cianci, Monsalve Maya, and Breakey frame prediction markets as Bayesian inverse problems where observed probability and volume histories are noisy, endogenous, and shaped by heterogeneous trader types. That reinforces Naly's editorial stance. Ingested market state is evidence, not a verdict. Good engineering makes that uncertainty inspectable instead of washing it out during generation.

Design trade-offs

Gamma versus CLOB is the first trade-off. Gamma gives Naly discoverability, article context, and metadata. CLOB gives sharper pricing and order-book mechanics. For daily engineering articles and prediction-market roundups, Gamma should be the default input because it answers what the market is about. CLOB can be added when the article needs spread, midpoint, or price-history precision.

Event-first versus market-first ingestion is the second trade-off. Event-first is cheaper for broad scans because an event can contain multiple markets and shared metadata. Market-first is cleaner for verification because the eventual score attaches to a specific binary outcome. Naly should ingest both shapes but make article links point to the market-level claim whenever a single outcome is being evaluated.

Raw JSON versus typed schema is the third trade-off. Raw JSON alone is easy to store but hard to query safely. Typed columns alone are easy to query but brittle when provider fields evolve. The durable pattern is both: typed fields for ranking, rendering, and joins; raw payloads for audit, replay, and migration.

Live render versus frozen render is the fourth trade-off. A live market widget can be useful, but article claims must bind to the publish-time snapshot. Otherwise, an old article can silently change meaning when Polymarket moves, closes, or resolves a market. For Naly's trust strategy, frozen evidence beats freshness when a page makes a dated claim.

Failure modes

The most dangerous failure is outcome-price misalignment. If Yes and No labels are parsed separately from prices, the article can invert the signal while still looking plausible.

The second failure is stale market reuse. A market may be active during discovery and closed by publish time. Each article job should revalidate active, closed, archived, and end-time fields immediately before binding the snapshot.

The third failure is multi-market ambiguity. A Polymarket event can contain several tradable markets. A round-up can cite the event, but a verification record must cite the specific market and outcome that Naly evaluated.

The fourth failure is schema drift. Public APIs change field names, defaults, and nested relations. Snapshot storage should include payload hashes, parser version, provider URL, and validation errors so drift is observable.

The fifth failure is overclaiming calibration. A market price is a useful public signal, but the literature warns against treating it as a clean probability across all domains and horizons. Naly's copy should preserve the difference between market-implied probability, Naly estimate, and realized outcome.

The sixth failure is citation loss. If an article only stores rendered markdown, later verification cannot prove which market page and payload supported the claim. Article-market joins should be first-class records, not embedded prose.

Implementation notes

A practical Naly implementation can keep the ingestion contract small:

provider: polymarket-gamma.
provider_url: the exact Gamma endpoint or public market URL used for discovery.
fetched_at: server timestamp in UTC.
event_key and market_key: stable provider identifiers or slugs.
question: canonical market question text.
outcomes: ordered labels.
outcome_prices: ordered decimal strings normalized to numeric values only after validation.
status: active, closed, archived, and publishability flags.
raw_payload: full provider response.
payload_hash: deterministic hash for idempotency and replay.
parser_version: the code contract that interpreted the payload.

The article generator should receive this as structured evidence, not as loose prompt text. It can then produce markdown through the existing AI stack, render with the site pipeline, and keep citations attached to source records. A verification worker can later join the article's market snapshot to resolution data and score whether the published claim aged well.

The engineering principle is deliberately conservative: ingest, normalize, snapshot, cite, then write. That order is slower than asking a model to browse and summarize a market page, but it creates the artifact Naly actually needs for acquisition and retention: a public article whose market evidence can be replayed after the result is known.