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Hyperliquid TP/SL Heatmap

A real-time browser dashboard that visualizes resting Hyperliquid TP/SL trigger orders as price-level clusters, anchored against live bid/ask prices from Quicknode Hypercore gRPC streams.

Frontend-Framework/Bibliothek:
Reagieren
Sprache:
TypeScript
Build-Tool/Entwicklungsserver:
Vite
Vorschau der Beispiel-App

Übersicht

This app connects to Quicknode's Hyperliquid gRPC endpoint to stream live TP/SL trigger order events (StreamTpslUpdates) and best bid/offer prices (StreamBboBook). Open orders are clustered into configurable price buckets and pushed to the browser over WebSocket, where a canvas-based heatmap renders the aggregated trigger pressure at each price level in real time.

The app ships with a built-in demo mode that uses a synthetic feed, so you can explore the UI without any credentials.

This example app is provided for educational and demonstration purposes only.

Architektur

Browser (React + Canvas)

│ WebSocket

Node.js HTTP + WebSocket Server

├── gRPC: StreamTpslUpdates ──┐
│ ├── Quicknode Hyperliquid gRPC Endpoint
└── gRPC: StreamBboBook ──────┘

Funktionen


  • Real-time TP/SL heatmap: price-bucket clusters updated live from Quicknode gRPC streams
  • BBO anchor: current bid and ask prices overlaid on the heatmap for market context
  • TP/SL mix indicator: each bucket shows a small green/red marker for take-profit vs. stop-loss share
  • Multi-asset support: monitor BTC, ETH, SOL, HYPE, and any other Hyperliquid perp symbol simultaneously
  • Configurable bucket width: BUCKET_SIZE_PCT scales bucket size relative to the current mid price
  • Demo mode: runs with a synthetic feed when credentials are missing or DEMO_MODE=true
  • Health endpoint: GET /health returns server status and current run mode as JSON

Voraussetzungen


  • Quicknode account with a Hyperliquid endpoint (gRPC access required)
  • Node.js v18+
  • pnpm

Projektstruktur

proto/
orderbook.proto # gRPC service definition
src/
client/
App.tsx # Coin selector and WebSocket client
HeatmapCanvas.tsx # Canvas-based heatmap renderer
main.tsx # React entry point
styles.css # App styles
server/
cluster-store.ts # Order clustering and BBO state
config.ts # Env var loading
demo.ts # Synthetic demo feed
grpc.ts # gRPC client setup
server.ts # HTTP + WebSocket server
streams.ts # Live gRPC stream connections
shared/
types.ts # Shared TypeScript types

Umgebungsvariablen

Kopieren .env.example zu .env and fill in your credentials:

# https://your-endpoint.hype-mainnet.quiknode.pro:10000
QUICKNODE_GRPC_ENDPOINT=your-endpoint.hype-mainnet.quiknode.pro:10000
QUICKNODE_GRPC_TOKEN=your-auth-token
TARGET_COINS=BTC,ETH,SOL
DEFAULT_COIN=BTC
BUCKET_SIZE_PCT=0.75
DEMO_MODE=false
PORT=8787
VariableBeschreibung
QUICKNODE_GRPC_ENDPOINTYour Quicknode Hyperliquid gRPC endpoint (host:port)
QUICKNODE_GRPC_TOKENAuth token for the gRPC endpoint
TARGET_COINSComma-separated list of Hyperliquid perp symbols to monitor
DEFAULT_COINWhich coin to display on load (must be in TARGET_COINS)
BUCKET_SIZE_PCTBucket width as a percentage of mid price (e.g., 0.75 = ~$500 buckets on BTC near $64k)
DEMO_MODESet to wahr to force demo mode regardless of credentials
PORTHTTP server port (default: 8787)

Erste Schritte

1. Das Repo klonen

git clone https://github.com/quiknode-labs/qn-guide-examples.git
cd qn-guide-examples/sample-dapps/hyperliquid-tpsl-heatmap

2. Abhängigkeiten installieren

pnpm installieren

3. Configure environment

cp .env.example .env

Open .env and set QUICKNODE_GRPC_ENDPOINT und QUICKNODE_GRPC_TOKEN with your Quicknode Hyperliquid endpoint credentials. Omitting them will start the app in demo mode automatically.

4. Start the app

pnpm dev

Open http://localhost:8787 in your browser.


Vorschau

Hyperliquid TP/SL Heatmap dashboard preview
Beiträge & Feedback
Wir würden uns sehr über Ihr Feedback freuen und begrüßen jeden Beitrag zu dieser Beispiel-App!
Um Probleme zu melden oder Feedback zu geben, eröffnen Sie ein GitHub-Issue im qn-Anleitung-Beispiele Repository.
Um einen Beitrag zu leisten, gehen Sie bitte wie folgt vor:
  1. Das Repository forken
  2. Erstelle einen Feature-Branch:
    git checkout -b feature/amazing-feature
  3. Speichere deine Änderungen:
    git commit -m "Tolle Funktion hinzufügen"
  4. Deinen Branch pushen:
    git push origin feature/amazing-feature
  5. Erstelle einen Pull Request.