Naly Engineering Notes: Polymarket Gamma API ingestion for prediction-market articles

TL;DRNaly ingests Polymarket's Gamma API as a deterministic discovery-and-pricing substrate for all prediction-market workflows, replacing ad hoc news scrapes with structured market entities. Every cycle, it converts live events and markets into article-ready signals for mispricing roundups, KBO previews, citation bundles, and later outcome verification, so story generation always starts from publicly observable probabilities and market structure rather than inferred opinions.

Abstract

Naly is using prediction-market market data as infrastructure, not as an overlay, so editorial artifacts become directly tied to an external market state that can be audited later. The Gamma API gives a read path for events, markets, tags, and prices without requiring wallet-level keys. The design challenge is to keep that ingest layer strict enough for reliability while still flexible enough for content teams that need fast topic discovery.

Where it sits in Naly

Polymarket Gamma ingestion sits at the upstream boundary between raw market primitives and publishable editorial assets. It is the first step of a broader pipeline:

Input layer: fetch events, markets, tags, and market statuses from Gamma.
Interpretation layer: normalize into Naly's internal schema (event_id, market_id, token IDs, outcomes, probabilities, timestamps, active/closed flags).
Narrative layer: feed normalized inputs to mispricing roundups and KBO prediction drafting flows.
Validation layer: keep resolved/closed market states for later article truth-checking and retrospective scorecards.

As of June 10, 2026, this is particularly aligned with active tactics that require trustworthy, citation-ready forecasting evidence: prediction calibration visibility, repeatable content sourcing, and later verification workflows.

Technical mechanism

Polymarket defines three APIs, with Gamma as the public discovery plane for event/market browsing and metadata, while order book/trade-style data is exposed by CLOB and user/positions data by the Data API (docs). Gamma and Data are public according to Polymarket docs, while CLOB has private/trading surfaces that require authentication for order operations.

Naly can implement a robust daily flow with only public endpoints:

Discover active candidate markets via GET /events with active=true, closed=false, pagination (limit, offset), and optional ordering filters.
Expand to constituent markets using event-level payloads, since events carry associated markets and reduce API calls compared to separate market lookups.
Target exact entities using slug-based calls when a known event or market is already identified.
Normalize pricing by mapping outcomes and outcomePrices arrays index-by-index into named probabilities.
Persist audit artifacts as both normalized rows and raw snapshots so every article can trace each sourced figure.
Gate downstream generation on freshness + schema checks; stale or incomplete snapshots are marked for refresh before use.

The Gamma documentation describes exactly this operational shape: public endpoints such as /events, /markets, /public-search, /tags, and /series are available for discovery, while pagination and filtering are supported via limit/offset, tag_id, and related filters. It also provides direct recommendations for three retrieval patterns: slug lookup, tag-based discovery, and event enumeration for broad scans. For Naly, the event-first pattern is the most cost-effective when building large daily candidates because each event can surface many market records.

In practice, a minimal source-of-truth record for Naly should include:

event and market IDs
market question
clobTokenIds (for downstream price reconciliation with CLOB where needed)
outcomes and outcomePrices
enableOrderBook
active, closed, and temporal fields (start/end timestamps)
fetch timestamp and source URL

Although Gamma can already provide a strong probabilistic baseline, a second refinement path is optional: when Naly needs shorter-interval intraday updates, CLOB endpoints like /price, /prices, or /book can be merged in later.

What the literature says

Research on prediction markets supports this data-first approach but adds guardrails around interpretation.

The market data model in prediction markets is only useful if calibrated and interpreted correctly; prices are not universal probabilities without context. A 2026 study on Polymarket and Kalshi found systematic calibration patterns that vary by domain and horizon, including measurable underconfidence in specific spaces.
Another 2026 lifecycle-focused study emphasizes that meaningful market analysis requires synchronized multi-layer data engineering: market metadata, trading events, and resolution signals need explicit linkage and periodic consistency checks, rather than isolated pulls.
Earlier work on market microstructure shows that market prices transmit trader information under a continuous-auction style flow, which is why Naly can treat market prices as collective-forecast signals while still validating outcomes over time.
Forecasting literature that compares market prices with other methods (for example survey-based forecasting) shows prediction markets can be strongly predictive but only when outcome verification and model discipline are preserved.

The practical consequence for Naly is straightforward: ingest everything with provenance, never treat a single price snapshot as a final truth, and separate readiness (data freshness + integrity) from story quality (editorial framing).

Design trade-offs

Naly intentionally optimizes for reliability over speed in ingestion.

Gamma-only vs Gamma+CLOB: Gamma gives stable discovery and public context quickly; adding CLOB improves microstructure richness but adds auth and endpoint complexity.
Daily snapshot vs continuous streaming: a deterministic scheduled pull is easier to audit and reproduce than continuous streams, but misses sub-minute regime shifts.
Event-first pull vs market-first pull: event-first reduces duplicate calls and improves contextual coverage; market-first gives slightly lower payload size for narrow tasks.
Wide schema vs strict schema: a broad JSON-first schema speeds integration but increases schema drift risk; strict normalization catches drift earlier but increases migration overhead.
Generalized fields vs domain-specific fields: using shared fields improves reuse across articles; adding domain-specific extensions (e.g., sport-specific confidence windows) increases immediate precision but can fragment long-term maintenance.

Given Naly’s objective of user trust and retention, strict reproducibility and citation quality should dominate immediate latency optimization.

Failure modes

The biggest failure modes are operational, not algorithmic.

Missing data due pagination bugs: if limit and offset windows change between polls, duplicates or gaps can appear. Mitigation: checkpoint pagination cursors and enforce idempotent upserts.
Default closed=false dropping historical context: open-market pulls omit resolved items unless closed=true is explicitly requested. Mitigation: run a dedicated historical backfill path for verification tasks.
Slug instability: product URLs and human-readable slugs can drift. Mitigation: prefer primary IDs internally and retain slug as secondary key.
Semantic field drift: outcomes/outcomePrices interpretation can break if schema order assumptions are wrong. Mitigation: assert array alignment and length checks at ingest.
Transient API availability or throttling: public endpoints can fail or return partial payloads. Mitigation: retry with exponential backoff, poison-queue on repeated failures, and keep prior snapshots.
Late resolution and stale narratives: verification articles may run before settlement settles cleanly. Mitigation: store settlement status as part of publish-state and update post-hoc with an immutable correction log.

Given Naly’s trust-first strategy, the pipeline should fail closed: better to delay an article than publish with unverifiable market state.

Implementation notes

Using the stated runtime stack, a practical implementation remains straightforward:

Use Next.js server handlers (next@16.0.7) to host ingestion endpoints and scheduled jobs.
Persist normalized rows in Neon using drizzle-orm@^0.44.7 over @neondatabase/serverless@^1.0.2 with explicit unique constraints on market identifiers.
Store raw payload snapshots in Vercel Blob (@vercel/blob@^2.0.0) for auditability and post-mortem diffing.
Keep markdown source generation and article assembly outside ingestion core; use marked@^17.0.1 for safe transformation and ai@6.0.0-beta.105 plus @anthropic-ai/claude-agent-sdk@^0.2.15 only after data integrity checks pass.
Use tsx@^4.21.0/typescript@^5.9.3 for reproducible one-off replays when backfilling historical windows.

On June 10, 2026, the architecture should prioritize three hard outputs: raw snapshot immutability, deterministic projection into internal schema, and a verification-oriented audit trail from source API URL to final article citation.