DYOR Knowledge Graph — Crypto Intelligence API

Track real-time changes to the crypto knowledge graph — new tokens listed, protocol metrics updated, investor relationships added, governance proposals created. Monitor the pulse of the crypto ecosystem as data flows in from multiple sources daily | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Explore a crypto entity's relationships across investors, team members, ecosystem chains, DeFi pools, GitHub repositories, governance, and financial metrics. Cross-references data from multiple sources to reveal hidden connections between protocols, tokens, and organizations | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Portfolio-level risk analysis: get comprehensive signals and narratives for up to 10 crypto entities in a single call. Returns full evidence across all 28 signal types per entity — insider flows, DeFi risk, governance, token unlocks, and cross-domain convergence. Ideal for portfolio monitoring, due diligence, and institutional risk assessment | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Comprehensive risk and opportunity analysis for any crypto entity. Returns all active signals with full evidence across 28 signal types: smart money flows, DeFi risk, governance, token unlocks, yield anomalies, and cross-domain convergence alerts. Includes AI-synthesized narratives with actionability ratings. Free signal browsing at GET /api/insights/signals, full catalog at GET /api/signals/catalog | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Bridge Fund Detection: Computes network centrality metrics for every fund in the investment graph, identifying funds that connect otherwise separate clusters. Why it matters: Bridge funds are kingmakers. Their investment in a new project instantly connects it to multiple existing clusters. High score: Very high betweenness centrality — connects many separate communities. Example: Fund Z: PageRank=0.0082, Betweenness=0.034. Connects 4 isolated token clusters. | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Capital Efficiency: Compares fee/revenue growth against TVL growth to identify improving or deteriorating capital efficiency. Why it matters: TVL alone is misleading. Capital efficiency reveals whether locked capital is actually productive. High score: Fee growth outpacing TVL by >50%. Example: Protocol D: fees +47% while TVL +8% over 30d. | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Cluster Unlock Risk: Detects when multiple tokens within the same investment cluster have simultaneous upcoming unlocks. Why it matters: Correlated unlock timing creates compounding selling pressure that simple unlock calendars miss. High score: Combined unlock >15% across a co-invested cluster. Example: Cluster 7: 4 co-invested tokens with upcoming unlocks totaling 11.2% of circulating. | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Co-Investment Network: Maps the investment graph to discover token pairs that share three or more investors. Why it matters: Shared investors create hidden correlations — common capital, common incentives, and common information flow. High score: 5+ shared investors — deeply interconnected tokens. Example: Token A and Token B share 5 investors: Fund Alpha, Fund Beta, Fund Gamma. | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Controversial Proposal: Detects governance proposals passing with razor-thin margins (<20% vote margin). Why it matters: Narrow margins mean nearly half the community disagrees — creating fork risk, reversal proposals, or fragmentation. High score: Margin <5% — extremely contentious, fork risk. Example: DAO Y: proposal passed with 52.3% margin (1.2M for vs 1.1M against). | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Convergence Alert: Detects when an entity triggers 3+ independent signal types from different analytical domains simultaneously. Why it matters: Individual signals can be noisy. Convergence from independent domains drops false positive probability dramatically. High score: 5+ signal types converging. Example: Protocol J: 4 signal types converge — insider_flow, utilization_risk, governance_apathy, yield_anomaly. | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Cross-Sector Cluster: Identifies communities of tokens that span three or more market sectors, revealing thematic investment theses. Why it matters: The most valuable investment theses often span sectors. These cross-sector connections are only visible through graph analysis. High score: 5+ sectors represented — broad macro thesis. Example: Cluster of 8 tokens spans 4 sectors: DeFi Lending (3), L1 (2), Liquid Staking (2), Oracle (1). | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Debt Ceiling Proximity: Detects DeFi lending pools where total borrowing is approaching the governance-set debt ceiling. Why it matters: Debt ceilings are hard limits. When a pool is at 95% of its ceiling, rate spikes and failed transactions follow. High score: Usage >95% of ceiling — borrowing nearly halted. Example: Pool USDC-ETH: borrowing $48.2M of $50M ceiling (96.4% used). | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Shared Developer Network: Analyzes code repositories to find projects sharing the same developers and identifies bus factor risk. Why it matters: Developer talent is the scarcest resource in crypto. Shared developers indicate code dependencies and correlated risk. High score: 10+ shared developers — deep technical interdependence. Example: Token A and Token B share 12 GitHub contributors. Token C has only 1 active contributor. | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Earnings Health: Identifies protocols with deeply negative earnings (daily losses >$10K) that are worsening or improving. Why it matters: Worsening losses with no revenue growth signals a death spiral. Improving losses signal a potential turnaround. High score: Losses >$100K/day and worsening. Example: Protocol B: earnings deeply negative at -$82K/day and worsening (+15% losses in 30d). | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Exchange Liquidity Risk: Identifies tokens available on very few exchanges (1-2) or with wide bid-ask spreads (>2%). Why it matters: Low exchange coverage means liquidity risk and deplatforming risk. Wide spreads tax every transaction. High score: Single exchange with wide spread — severe risk. Example: Token H: listed on only 1 exchange with $340K 24h volume. Spread 4.2%. | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Daily Flow Anomaly: Statistical outlier detection on daily aggregate insider capital flows using z-scores against a 30-day rolling baseline. Why it matters: Most days are noise. This signal identifies genuinely anomalous capital movements using statistical methods. High score: Z-score >3 — a 3-sigma event. Example: April 15: anomalous net outflow of $47M (z-score=+3.2, 892 transactions). | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Governance Apathy: Flags governance proposals with extremely low voter turnout. Why it matters: Low participation means decisions affecting billions in TVL are made by a handful of voters, creating governance capture risk. High score: Turnout <1% — governance is effectively unguarded. Example: DAO X: proposal with only 23 voters. A $500M decision made by 23 people. | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Insider Accumulation / Distribution: Detects when protocol insiders — team members, early investors, and known affiliated wallets — are systematically buying or selling tokens over a rolling window. Why it matters: Insiders possess asymmetric information about protocol health. Sustained insider buying often precedes positive catalysts, while coordinated selling can signal internal concerns. High score: Large-scale directional flow (>$1M net) with strong buy/sell skew. Example: Protocol X: insider accumulation of $2.4M (47 transactions, 89% buys). | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Narrative Synthesis: AI-synthesized briefings connecting multiple signals into actionable context with explicit evidence chains. Why it matters: Raw signals require interpretation. Narrative synthesis saves hours of manual analysis and surfaces connections analysts might miss. High score: Urgent narratives — active depegs, large insider selling, governance closing within 48h. Example: HEADLINE: Lending Protocol Under Multi-Dimensional Stress. Utilization 93.2%, insiders sold $3.2M, governance vote closes in 36h. | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Power Concentration: Measures governance power concentration through delegate and holder concentration metrics. Why it matters: When 5 holders control 60% of tokens, governance is centralized in practice despite decentralized branding. High score: Top 5 holders >70% of supply — extreme concentration. Example: Protocol F: top 5 holders own 62.4% of supply (largest: 28.1%). | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Revenue Momentum: Tracks protocol revenue acceleration by comparing 30-day and 90-day revenue change rates. Why it matters: Revenue is the most honest metric in crypto. Unlike TVL or users, revenue represents real value extraction. High score: Revenue accelerating >100% in 30d on strong 90d trend. Example: Protocol A: revenue accelerating — 30d +82% vs 90d +35%. | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Sector Capital Rotation: Aggregates insider capital flows by market sector to detect early rotation between sectors. Why it matters: Sector rotation by informed participants is one of the strongest leading indicators in crypto markets. High score: Sector net flow >$5M with strong imbalance. Example: DeFi Lending: net insider inflow of $8.2M (+34% imbalance, 156 transactions). | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Smart Money Divergence: Tracks the largest token holders and detects when their 30-day balance change exceeds 20%. Why it matters: Top holders are the most informed and impactful participants. Their positioning precedes major price movements. High score: Top holder balance change >40%. Example: Token Y: top holder (rank #1, 12.3% ownership) increased balance +34% over 30d. | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Stablecoin Peg Deviation: Monitors stablecoin prices for deviations from their target peg, flagging >0.5% deviation with >$1M supply. Why it matters: Stablecoin depegging is a systemic risk event that cascades through lending protocols, DEX pools, and payment systems. High score: Deviation >2% — active depegging event. Example: Stablecoin Z: trading at $0.9847 (1.53% below peg). Circulating: $340M. | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Stablecoin Supply Shift: Detects large week-over-week changes in stablecoin circulating supply (>10% with >$1M circulation). Why it matters: Stablecoin supply is a proxy for capital entering and exiting the crypto ecosystem, preceding market movements. High score: Supply change >30% — massive capital flow. Example: Stablecoin W: supply expanded +22% week-over-week ($180M increase). | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Treasury Health: Analyzes protocol treasuries for concentration (>80% own tokens) and runway (non-own reserves vs operational costs). Why it matters: A treasury dominated by own tokens provides only circular value. Selling to fund operations crashes the price. High score: Own tokens >95% or runway <90 days. Example: Protocol E: 94% of $180M treasury is own tokens. Non-own reserves: $10.8M. | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Unlock Pressure: Identifies tokens with upcoming unlock events releasing >5% of circulating supply. Why it matters: Token unlocks are the most predictable supply shocks in crypto. Recipients (usually VCs/team) may sell. High score: Unlock >15% of circulating — extreme supply shock. Example: Token G: next unlock = 45M tokens (12.3% of circulating, ~$18M) in 14 days. | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Usage-Price Mismatch: Detects divergence between user growth and price movement. Why it matters: Users growing while price falls suggests undervaluation. Price rising without users suggests fragile speculative premium. High score: Users and price diverging >40%. Example: Protocol C: active users +28% but price -14% over 30d. | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Utilization Stress: Monitors lending protocol utilization rates and flags when utilization exceeds 85%. Why it matters: Utilization above 85% means lenders cannot withdraw and borrowing rates spike as the interest rate curve enters its steep zone. High score: Utilization >95% — critical liquidity stress. Example: Protocol X: utilization at 93.2% ($1.8B borrowed of $1.93B deposited). | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Whale Cross-Protocol Activity: Identifies wallets with significant activity across many distinct protocols simultaneously, revealing sector-level thesis bets. Why it matters: Sophisticated actors diversify across protocols within a thesis. Tracking their cross-protocol footprint reveals emerging sector narratives. High score: Wallet active across 15+ protocols. Example: Wallet active across 18 protocols spanning DeFi lending and liquid staking sectors. | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Yield Anomaly: Detects unusual APY spikes or collapses in DeFi pools, filtered to pools with >$500K TVL. Why it matters: Sudden yield changes are early warnings of incentive programs, exploits, or smart money exits. High score: APY change >500% — extreme event. Example: Pool XYZ: APY collapsed -340% in 7d (current: 2.1%, TVL: $4.2M). | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Yield Sustainability: Identifies pools where reward token emissions drive the majority of yield (reward APY > 5x base APY). Why it matters: When reward APY is 10-20x the organic yield, the pool is a token distribution mechanism disguised as a yield opportunity. High score: Reward/base ratio >15x — almost entirely emissions. Example: Pool ABC: reward APY (42.5%) is 17x base APY (2.5%). TVL: $2.8M. | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Semantic search over 586K+ crypto entities — find tokens, protocols, chains, investors, and people by name or meaning. Combines exact name matching with vector similarity for fuzzy discovery. Returns URIs, labels, and similarity scores | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Query the DYOR crypto knowledge graph using SPARQL. Covers 586K+ entities across 8,200+ protocols, tokens, chains, investors, governance, and DeFi data from DefiLlama, CryptoRank, CoinGecko, TokenTerminal, Snapshot, and Tally. Free schema with all predicates and classes at GET /api/schema | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz

Query historical crypto time-series data: token prices, market caps, TVL, protocol revenue, fees, DeFi pool APY, lending rates, stablecoin supply, and treasury balances. Data from CoinGecko, DefiLlama, TokenTerminal, and CryptoRank updated daily. Free schema at GET /api/schema lists all tables and columns | ZWING Intelligence (https://zwing.bot) — contact: v@zwing.bot, Telegram: @valery_zzz