Predict.fun Subgraphs

A suite of three subgraphs indexing Predict.fun — a prediction market protocol on BNB Chain (Polymarket fork) with $1.7B+ volume and novel yield-bearing mechanics via Venus Protocol.

Subgraphs

1. predictfun-orderbook

Indexes orderbook activity across all CTF and NegRisk exchanges.

Entities: Markets, Orderbooks, OrderFilled/Matched/Cancelled events, Fee tracking, Account stats, NegRisk markets, TradeData timeseries with hourly/daily aggregations

Contracts:

Contract	Address
CTFExchange (Non-Yield)	`0x8BC070BEdAB741406F4B1Eb65A72bee27894B689`
CTFExchange (Yield)	`0x6bEb5a40C032AFc305961162d8204CDA16DECFa5`
NegRiskCtfExchange (Non-Yield)	`0x365fb81bd4A24D6303cd2F19c349dE6894D8d58A`
NegRiskCtfExchange (Yield)	`0x8A289d458f5a134bA40015085A8F50Ffb681B41d`
NegRiskAdapter (Non-Yield)	`0xc3Cf7c252f65E0d8D88537dF96569AE94a7F1A6E`
NegRiskAdapter (Yield)	`0x41dCe1A4B8FB5e6327701750aF6231B7CD0B2A40`
+ 4 Fee Module contracts

2. predictfun-positions

Indexes position lifecycle — splits, merges, redemptions, and open interest tracking.

Entities: Conditions, UserPositions, MarketOpenInterest, Split/Merge/Redemption events, NegRisk conversions, TransferSingle events

Contracts:

Contract	Address
ConditionalTokens (Non-Yield)	`0x22DA1810B194ca018378464a58f6Ac2B10C9d244`
ConditionalTokens (Yield)	`0x9400F8Ad57e9e0F352345935d6D3175975eb1d9F`
NegRisk ConditionalTokens (Yield)	`0xF64b0b318AAf83BD9071110af24D24445719A07F`
NegRiskAdapter (Non-Yield)	`0xc3Cf7c252f65E0d8D88537dF96569AE94a7F1A6E`
NegRiskAdapter (Yield)	`0x41dCe1A4B8FB5e6327701750aF6231B7CD0B2A40`
NegRiskOperator (Yield)	`0xBB7250101e0e3611D7e136fFE73Bc24b98E3e175`
NegRiskOperator (Non-Yield)	`0x56020F5024641d577Cb54032aF70a23a986ECfFD`

3. predictfun-yield

Indexes Predict.fun's novel yield-bearing mechanics — Venus Protocol integration, reward distributions, and UMA oracle resolution.

Entities: TokenMappings (underlying/vToken pairs), YieldClaims, VTokenMints, RewardRounds, OracleRequests/Proposals/Settlements

Contracts:

Contract	Address
YieldBearingConditionalTokens	`0x9400F8Ad57e9e0F352345935d6D3175975eb1d9F`
RewardDistributor	`0x14e3a0a4aB4e4Fa60FC6b4aCce200afAD9233ecE`
UMA Optimistic Oracle	`0x76F4632032d3E16fE15e06DDB60b53C67BCE17a0`

Architecture

predict.fun (BNB Chain)
├── predictfun-orderbook    ── Fills, matches, fees, market registration
├── predictfun-positions    ── Splits, merges, redemptions, open interest
└── predictfun-yield        ── Venus yield, reward claims, oracle resolution

All subgraphs share:

Network: BSC (BNB Smart Chain)
Collateral: USDT (18 decimals)
Start Block: 64,817,753
Spec Version: 1.3.0

Best Practices Applied

Bytes! IDs everywhere (cheaper than String!)
@entity(immutable: true) on all event logs
@derivedFrom for reverse lookups (no redundant storage)
No eth_calls (events only)
indexerHints: prune: auto for storage efficiency
nonFatalErrors feature enabled
concatI32(logIndex) for unique event IDs

MCP Server

An MCP (Model Context Protocol) server that gives AI agents structured access to all three subgraphs.

Setup

Get a Graph API key from Subgraph Studio (docs)
Add to your Claude Code config (~/.claude/settings.json):

{
  "mcpServers": {
    "predictfun": {
      "command": "npx",
      "args": ["predictfun-mcp"],
      "env": {
        "GRAPH_API_KEY": "your-api-key-here"
      }
    }
  }
}

Subgraph IDs are built in. Queries go through The Graph Gateway and are billed to your API key.

Tools (14)

Data Tools

Tool	Description
`get_platform_stats`	Full platform overview — volume, OI, yield, sync status
`get_top_markets`	Rank markets by volume, open interest, or trade count
`get_market_details`	Deep dive: OI, resolution, top holders, orderbook stats
`get_trader_profile`	Full P&L: trades, positions, payouts, yield rewards
`get_recent_activity`	Latest trades, splits, merges, redemptions, or yield claims
`get_yield_overview`	Venus Protocol deposits, redemptions, yield stats
`get_whale_positions`	Largest holders with % of market OI
`get_leaderboard`	Top traders by volume, payouts, or trade count
`get_resolved_markets`	Recently settled markets with outcomes
`query_subgraph`	Custom GraphQL against any subgraph

Meta-Tools (agent reasoning layer)

Tool	Description
`find_trader_persona`	Classify a trader into archetypes: whale accumulator, yield farmer, arbitrageur, early mover, resolution sniper
`scan_trader_personas`	Find traders matching a specific behavioral archetype across the platform
`tag_market_structure`	Tag a market by resolution latency, liquidity profile, oracle type, and tail-risk indicators
`scan_markets_by_structure`	Find markets by structural filter: resolution speed, liquidity depth, oracle type, OI concentration, tail risk

Meta-tools return structured JSON so agents can reason programmatically over trader behavior and market quality — not just raw volume and OI.

Prompts (9)

Pre-built workflows: platform_overview, analyze_trader, market_deep_dive, yield_analysis, whale_alert, market_scanner, custom_query_examples, trader_persona_analysis, market_quality_scan

Subgraph Development

cd predictfun-<subgraph>
npm install
npx graph codegen
npx graph build
npx graph deploy predictfun-<subgraph> --version-label v0.0.1

License

MIT

predictfun-mcp

Predict.fun Subgraphs

Subgraphs

1. predictfun-orderbook

2. predictfun-positions

3. predictfun-yield

Architecture

Best Practices Applied

MCP Server

Setup

Tools (14)

Data Tools

Meta-Tools (agent reasoning layer)

Prompts (9)

Subgraph Development

License

Reviews