Documentation
How the WAGMI Terminal works, end to end. Methodology, tracking, analysis, roadmap, and FAQ.
Platform Overview
WAGMI Terminal is a real-time crypto intelligence platform built for operators, analysts, and funds. It combines multi-model AI inference with wallet-behavior clustering and liquidity analytics across 14 chains. The terminal collapses what used to require five tools, three dashboards, and a Telegram bot into one screen.
Every screen is designed to be useful in under three seconds. We don't ship features that don't earn their place on a working trader's monitor.
Signal Methodology
Signals are produced by an ensemble of models scored across four orthogonal dimensions: momentum, wallet activity, liquidity health, and sentiment. The composite is calibrated against a 90-day rolling backtest with conservative shrinkage to suppress overfit confidence.
- Momentum — micro-structure features over 1m, 5m, 15m, and 1h windows.
- Wallet Activity — net new smart-money entries weighted by wallet score.
- Liquidity — depth, slippage curve, and pool age.
- Sentiment — social signal aggregated and de-botted.
Confidence is reported as a calibrated probability that the signal reaches its target window. We deliberately under-report confidence on tail-risk regimes.
Smart Money Tracking
We cluster 12,847 wallets by performance, holding behavior, and rotation cadence. Wallets are scored on a rolling 30-day basis using PnL, win rate, and survivorship-adjusted alpha. Cohorts are rebuilt nightly.
Whale events are detected on transaction flow with chain-aware heuristics. Coordinated entries — three or more high-score wallets accumulating the same asset within a short window — are surfaced as accumulation alerts.
Liquidity Analysis
Pool depth, fee tier, age, and net inflow are tracked across the major venues on each supported chain. The heatmap surfaces hour-by-hour pressure per chain. The chain rotation panel computes net inter-chain flow and ranks the destinations capital is moving toward.
Spike detection runs on a rolling baseline with a robust z-score. We dedupe spikes that originate from a single LP rebalance to avoid false positives.
Beta Roadmap
- Q1 2026Cohort 02 invites · multi-chain expansion · public read-only API
- Q2 2026Webhook v2 · custom signal templates · cohort copy-trade alerts
- Q3 2026Open beta · institutional connectors · backtesting workbench
- Q4 2026Team workspaces · shared watchlists · exportable research