Why Prediction Market APIs Matter Now
Prediction markets have quietly become a serious asset class.
AP reports combined Polymarket + Kalshi volume at $26.6B, up from $9.75B just months earlier. TRM Labs estimates monthly prediction‑market volume grew from $1.2B in early 2025 to over $20B in January 2026, with 840k monthly unique wallets.
If you’re building trading terminals, dashboards, or consumer apps, you can’t treat prediction markets as a niche side feature anymore.
You need:
- Low‑latency market data
- Correct lifecycle and resolution states
- Cross‑venue coverage (Polymarket, Kalshi, and beyond)
- Production‑grade APIs that abstract away protocol details
This guide breaks down how prediction markets work on‑chain, the data frontends actually need, and how Codex’s prediction market API lets product teams ship faster without building their own indexing stack.
How On‑Chain Prediction Markets Actually Work
At a high level, a prediction market lets users trade tokens that pay out based on the outcome of a future event.
Examples:
- “Will BTC close above $80k on Dec 31?”
- “Will candidate X win the election?”
- “Will the Fed cut rates at the next meeting?”
Under the hood, most venues follow a similar lifecycle.
1. Market Creation & Metadata
Every market starts with a definition:
- Event description: what is being predicted
- Categories & tags: politics, macro, sports, crypto, etc.
- Outcomes: YES/NO or multi‑outcome (Team A / Team B / Draw)
- Parameters: open/close times, resolution rules, oracle reference
On Polymarket, for example, market and event objects include:
- Category, tags, resolution source
- Outcomes and outcome tokens
- Liquidity and volume metrics
Most of this lives off‑chain (venue databases) but references on‑chain assets (outcome tokens, pool addresses).
2. Trading & On‑Chain Liquidity
Once the market is live, trading happens via:
- Order books (CLOB) with bids/asks
- AMMs or pools for outcome tokens
Polymarket exposes:
- Book snapshots
- Price changes and tick‑size changes
- Best bid/ask, last trade price
Kalshi’s API returns:
- YES and NO bids (YES bids and NO asks are equivalent in binary markets)
- Real‑time order book, trades, and positions via authenticated WebSockets
On‑chain, this corresponds to fills, transfers, and pool updates that need to be indexed and normalized.
3. Oracle, Dispute, and Resolution
Resolution is where prediction markets get tricky.
- Polymarket uses UMA’s Optimistic Oracle.
- Winning outcome tokens redeem for $1; losing tokens become worthless.
- Kalshi defines explicit lifecycle states:
initialized,active,inactive,closed,determined,disputed,amended,finalized.
For a frontend, you must:
- Show when trading is disabled (e.g.,
closed) - Mark final outcomes and settlement status
- Correctly update P&L, balances, and charts after resolution
Academic work on Polymarket highlights how this lifecycle spans off‑chain metadata, on‑chain fills, and oracle events, which must be integrated into one system for reliable analytics.
4. Full‑Lifecycle Data Complexity
A 2026 paper analyzing Polymarket reports:
- 770k+ market records
- 943M+ fill records
- ~2M oracle events
- Another dataset counts 1.20B trades, 1.30M markets, and $61B nominal volume
That’s not something you casually “loop over with web3.js”.
You need an indexer and enrichment pipeline just to get a coherent view of:
- Which markets exist
- Who holds what
- What each position is worth now
- How and when markets resolved
What Frontends Actually Need from Prediction Market APIs
Venue APIs (Polymarket, Kalshi, Gemini) are built for execution: placing orders, streaming book updates, and managing positions.
Frontends, however, need a unified data layer that spans multiple venues.
Broadly, product teams need five categories of data.
1. Market & Event Discovery
For homepages, search, and category views, you need:
- Event metadata (title, description, tags)
- Market status (active, closed, resolved)
- Outcomes and current prices
- Time windows (open, close, expected resolution)
Polymarket’s event API, for example, exposes:
- Liquidity, volume, open interest
- 24h/1w/1m/1y volume
- Competitive score and resolution metadata
Good APIs let you filter and rank events:
- By category (e.g., geopolitics, crypto, sports)
- By trading activity (volume, open interest)
- By “interestingness” (near 50‑50, high uncertainty)
2. Real‑Time Pricing & Order Book Depth
For trading UIs, bots, and alerts you need streaming:
- Best bid/ask and spread
- Last trade price and size
- Book snapshots and deltas
Polymarket offers public WebSockets for:
- Book snapshots
- Price changes
- Market lifecycle events (new market, market_resolved)
Kalshi and Gemini similarly provide WebSocket streams for:
- Order book depth
- Trade events
- Positions and fills
But schemas, IDs, and status fields differ across venues. Frontends either:
- Normalize this themselves, or
- Use a unified prediction market API that standardizes it.
3. Liquidity, Volume, and Open Interest
Discovery and ranking rely on capital at stake, not just price.
Key metrics:
- Total and 24h volume
- Open interest (value of unresolved contracts)
- Liquidity (depth at best bid/ask, or pool reserves)
These drive UX features like:
- “Trending markets” carousels
- “Most traded today” lists
- Filters for minimum liquidity (to avoid illiquid markets)
Codex’s prediction market API, for instance, builds server‑side ranking signals:
- Trending – growth velocity of volume/open interest
- Relevance – absolute capital at stake
- Competitive – genuine uncertainty, with prices near 50‑50
4. Resolution States and Settlement Data
Resolution is where mistakes become expensive.
Frontends must distinguish between:
- Active markets (trading allowed)
- Closed markets (waiting for result)
- Disputed or amended markets
- Finalized markets (settled P&L)
Kalshi’s explicit lifecycle (initialized, active, closed, determined, disputed, amended, finalized) is a good model.
APIs should expose:
- Current lifecycle state
- Resolution timestamp
- Winning outcome
- Settlement amount per contract
After resolution, price data must reflect the switch to:
- Winning tokens = $1
- Losing tokens = $0
Without clean resolution states and settlement data, portfolio UIs will:
- Show stale prices
- Miscompute P&L
- Mislead users about current risk exposure
5. Trader‑Level Analytics
To build serious trading products, you need aggregations over traders, not just raw trade feeds.
Useful data:
- Per‑trader volume and win rate
- Net positions per event
- Historical performance by category (e.g., politics vs crypto)
These power features like:
- Leaderboards and social trading feeds
- Copy‑trading or “follow top forecasters” modules
- Risk dashboards for power users or institutional participants
Academic work on prediction markets emphasizes that full‑lifecycle trader analytics require integrating:
- Off‑chain market metadata
- On‑chain trade/fill logs
- Oracle resolution events
Doing that in‑house is non‑trivial.
Why Stitching Native Venue APIs is Painful
On paper, you could just consume Polymarket, Kalshi, and Gemini APIs directly.
In practice, product teams quickly hit three issues.
1. Heterogeneous Schemas and IDs
Each venue defines its own:
- Market IDs and event identifiers
- Lifecycle statuses
- Outcome naming conventions
- Price formats and units
This makes cross‑venue features hard:
- Unified search (“all political markets across venues”)
- Consolidated portfolios (“your positions on both Polymarket and Kalshi”)
- Cross‑venue analytics (“total exposure to election outcomes”)
2. Mixed On‑Chain and Off‑Chain Sources
As the Polymarket lifecycle research shows, relevant data lives in:
- Off‑chain databases (events, descriptions, categories)
- On‑chain logs (fills, pool updates, transfers)
- Oracle systems (resolution, disputes)
Building and maintaining indexers across multiple chains and venues means:
- Running RPC nodes or buying node access
- Writing custom ETL pipelines
- Handling reorgs, data gaps, and schema changes
3. Latency and Reliability at Scale
Once you’re serving:
- Millions of requests per day
- Tens of thousands of concurrent users
- High‑traffic trading interfaces
You need:
- Sub‑second responses
- High uptime SLAs
- Robust caching and failover
Venue APIs are optimized for their own apps and execution; they’re not designed to be your all‑in‑one data warehouse and analytics layer.
Codex: A Unified Prediction Market Data Layer
Codex approaches prediction market APIs from an infrastructure perspective.
The company already indexes 70M+ tokens, 80+ networks, and 700M+ wallets, powering apps like Coinbase, TradingView, Uniswap, Magic Eden, Rainbow, MoonPay, Farcaster, and pump.fun.
Prediction markets are a natural extension of this data stack.
Cross‑Venue Normalization (Polymarket + Kalshi)
Codex’s prediction market API (currently in beta for Polymarket and Kalshi) provides:
- A single GraphQL‑style schema across venues
- Consistent market and event objects
- Normalized lifecycle states and resolution data
Instead of stitching:
- Polymarket’s Gamma/Data/CLOB APIs
- Kalshi’s REST + WebSockets
You query one endpoint, and Codex handles:
- ID normalization
- Status mapping
- Outcome token linkage
Trading‑Ready Data: Prices, Charts, and Aggregates
Codex’s core strength is trading‑grade market data.
For prediction markets, you get:
- Real‑time prices and OHLC candles
- Volume, liquidity, and open interest
- TVL‑like stats for markets and events
This sits on the same infrastructure that delivers:
- Token prices (USD and native) across 80+ chains
- High‑frequency chart data (candles, volume)
- Holders and balances for 700M+ wallets
Frontends can:
- Render trading views with live charts
- Filter markets by liquidity and risk
- Compute P&L and exposure in real time
Server‑Side Ranking & Discovery
Codex builds ranking signals directly into the API:
- Trending – markets with fast‑growing volume
- Relevance – markets with high open interest
- Competitive – markets with genuine uncertainty (prices near 50‑50)
Instead of rolling your own scoring logic, you can:
- Ask for “top trending political markets this week”
- Surface “most competitive macro markets”
- Build “Featured markets” sections in minutes
Trader Analytics & Portfolio Views
Because Codex indexes both on‑chain and venue‑level data, it can expose:
- Trader stats (volume, win rate, positions)
- Cross‑venue portfolio views
- Event‑level exposure by wallet
This helps product and engineering teams ship:
- Unified profiles (“Your prediction track record”)
- Social discovery (“Top forecasters in crypto markets”)
- Risk dashboards for institutional or power users
Built on a Mature, High‑Performance Data Pipeline
Codex isn’t a lightweight aggregator.
It’s a production‑grade data infrastructure that has spent years building:
- High‑throughput indexers (thousands of transactions per second)
- Normalization pipelines for long‑tail tokens and launchpads
- Trading‑grade chart services with sub‑second latency
Prediction markets plug into this existing stack, giving product teams:
- Proven reliability (already powering Coinbase, TradingView, Uniswap, etc.)
- One vendor for tokens and prediction markets
- Fewer systems to maintain and debug
How Product Teams Use Codex’s Prediction Market API
1. Frontend Discovery & Homepages
Typical workflow:
- Call Codex to fetch prediction events across Polymarket and Kalshi.
- Apply server‑side ranking (trending, relevance, competitive).
- Render category carousels and “Top markets” sections.
Result:
- Fast, relevant discovery for users
- No custom scoring logic or multi‑API stitching
2. Trading Interfaces & Terminals
For high‑traffic trading UIs, teams:
- Use Codex for prices, candles, volume, and liquidity
- Use venue WebSockets for order submission and execution
This separation lets Codex specialize in:
- Fast and reliable market data
- Cross‑venue normalization
While venues focus on:
- Order routing and settlement
3. Analytics Dashboards and Risk Tools
Data/quant teams building internal tools:
- Query Codex for historical trades and chart data
- Build exposure breakdowns by category, outcome, and venue
- Track resolution and P&L over time
Because Codex covers both tokens and prediction markets, they can:
- Overlay prediction prices with spot/derivatives data
- Analyze how markets price macro events vs actual outcomes
4. Wallets and Portfolios
Wallet and portfolio apps use Codex to:
- List prediction‑market positions alongside tokens
- Show live pricing and expected payoff per contract
- Update balances automatically at resolution (winning = $1, losing = $0)
This simplifies UX for users who treat prediction markets like any other on‑chain asset.
Actionable Steps for Teams Evaluating Prediction Market APIs
If you’re deciding between building your own stack and using a provider, take these steps.
-
List all features your frontend needs
- Discovery: search, filters, rankings
- Trading views: charts, depth, live prices
- Portfolios: positions, P&L, resolution handling
- Analytics: historical data, trader stats
-
Map which data fields power each feature
- Market metadata (title, categories, tags)
- Volume, liquidity, open interest
- Lifecycle states and resolution outcomes
- Trader‑level aggregates
-
Benchmark native venue APIs vs unified providers
- How many endpoints do you need to hit per view?
- How much glue code and ETL do you need?
- How will you handle schema changes or new venues?
-
Test Codex’s prediction market endpoints
- Integrate a single view (e.g., “Top markets” page)
- Measure latency, uptime, and coverage
- Evaluate how much code you delete versus a DIY stack
-
Plan for growth and new venues
- TRM and AP data show prediction markets are growing fast.
- More venues and categories will emerge.
- A unified API lets you add coverage without re‑architecting.
FAQ: Prediction Market APIs and On‑Chain Data
What is a prediction market API?
A prediction market API exposes structured data about markets that trade contracts on future events.
It typically includes:
- Event and market metadata
- Outcome prices and order book depth
- Liquidity, volume, and open interest
- Lifecycle states and resolution outcomes
- Trader analytics and positions
Codex’s prediction market API unifies this data across venues like Polymarket and Kalshi.
Why can’t I just use Polymarket or Kalshi’s API directly?
You can for execution, but for production‑grade frontends you’ll need to:
- Normalize schemas, IDs, and status fields across venues
- Stitch together off‑chain metadata, on‑chain trades, and resolution events
- Maintain indexers, ETL pipelines, and caching layers
Using a unified on‑chain data API like Codex lets you focus on product instead of building and maintaining infrastructure.
What data do prediction market frontends need most?
The critical data layers are:
- Market and event metadata for discovery
- Real‑time prices and order book depth for trading views
- Liquidity, volume, and open interest for ranking
- Resolution state and settlement data for P&L and portfolios
- Trader‑level analytics for leaderboards and risk tools
Codex exposes these via one GraphQL‑style schema.
How does Codex handle resolution states and P&L?
Codex ingests venue lifecycle states (e.g., Kalshi’s active, closed, determined, finalized) and oracle events (e.g., Polymarket UMA resolution) and normalizes them into consistent fields.
This allows frontends to:
- Detect when markets close and resolve
- Update contract values to $1 or $0 for prediction tokens
- Recompute P&L and balances with correct settlement data
Is Codex suitable for high‑traffic trading apps?
Yes.
Codex’s infrastructure already powers leading apps like Coinbase, TradingView, Uniswap, Magic Eden, Rainbow, and MoonPay.
It indexes thousands of transactions per second across 80+ networks, supports 70M+ tokens, and tracks 700M+ wallets, with sub‑second latencies for trading‑grade data.
Prediction market data sits on top of this same infrastructure, making Codex one of the best on‑chain data providers for high‑traffic prediction market frontends.