Historical Analytics Tool for Hyperliquid
Advanced market microstructure analysis platform for Hyperliquid L1 DEX
Live Demo: [Coming Soon]
HyperAnalyze is a professional-grade market microstructure analysis platform built specifically for Hyperliquid. It provides deep insights into orderbook dynamics, wallet behavior, liquidation patterns, and market efficiency.
- 🔥 Dynamic Order Book Heatmaps - Visualize liquidity evolution over time
- 🔄 Maker vs Taker Flow Analysis - Track liquidity provision patterns
- 📏 Multi-Asset Spread Analysis - Compare market efficiency across coins
- 📊 Volume Profile - Identify fair value zones and support/resistance
- 👥 Trader Analytics - Identify whales and track top market participants
- 🎯 Wallet Impact Analysis - Discover which wallets move each orderbook
- 💥 Liquidation Heatmaps - Map dangerous price levels and liquidation zones
- 📋 Interactive Data Explorer - Dive deep into raw trade data
# Clone the repository
git clone https://github.com/ConejoCapital/hyperanalyze.git
cd hyperanalyze
# Install dependencies
pip3 install -r requirements.txt# Launch the Streamlit app
streamlit run dashboard.py
# Or use the launcher script
./run_dashboard.shThe dashboard will open at http://localhost:8501
Visualize market depth evolution across time and price levels:
- Time × Price × Depth 3D visualization
- Bid/ask imbalance indicators
- Support/resistance zone identification
- Liquidity gap detection
Analyze liquidity provision vs consumption:
- Stacked volume charts
- Maker ratio trends
- Fee economics analysis
- Market health indicators
Compare bid-ask spreads across multiple assets:
- Real-time spread tracking (basis points)
- Spread vs volume correlation
- Cross-asset efficiency comparison
- Market quality metrics
Distribution of trading volume across price levels:
- Horizontal volume bars (buyer vs seller)
- Point of Control (POC) identification
- Value Area calculation (70% volume zone)
- High/Low Volume Nodes (HVN/LVN)
Track top market participants:
- Top 50 traders by volume
- Maker vs taker ratio analysis
- P&L tracking
- Individual trader drill-down
- Trading pattern identification
Research Feature: Identify which wallets move each orderbook:
- Volume-based ranking with taker ratio coloring
- Average price impact per trade
- Aggressive trading volume (orderbook consumption)
- Trade size distribution patterns
- Answers: "Who controls the orderbook?"
Research Feature: Map liquidation-prone price levels:
- Liquidation hotspot scatter plots
- Price-level concentration histograms
- Cumulative close volume tracking
- Close direction distribution
- Answers: "Where do liquidations happen?"
Interactive data filtering and exploration:
- Coin-level summary statistics
- Advanced filtering by coin, side, time
- Raw trade data viewer
- Export capabilities
hyperanalyze/
├── dashboard.py # Main Streamlit application
├── data_loader.py # Data pipeline & preprocessing
├── visualizations.py # All visualization classes
├── requirements.txt # Python dependencies
├── run_dashboard.sh # Launch script
├── test_installation.py # Installation verification
│
├── Hyperliquid Data Expanded/
│ ├── node_fills_*.json # Trade execution data
│ └── misc_events_*.json # Other blockchain events
│
├── Documentation/
│ ├── QUICK_START.md
│ ├── PHASE1_README.md
│ ├── RESEARCH_FEATURES.md
│ └── VISUALIZATION_RECOMMENDATIONS.md
│
└── processed_data.parquet # Cached processed data (auto-generated)
- Identify whale activity - Track large market movers
- Find liquidation zones - Avoid getting liquidated
- Analyze spread efficiency - Choose liquid markets
- Monitor maker/taker flow - Gauge market sentiment
- Market microstructure studies - Academic research
- Orderbook concentration - Decentralization metrics
- Liquidity provision - Market making analysis
- Price discovery - Information asymmetry research
- Spread analysis - Optimize quoting strategies
- Volume profile - Identify profitable price levels
- Adverse selection - Toxicity indicators
- Competition analysis - Track other MMs
- Historical backtesting - Strategy development
- Market impact - Execution cost analysis
- Liquidity dynamics - Depth evolution patterns
- Correlation studies - Cross-asset relationships
✅ "Who controls the BTC orderbook?"
→ Wallet Impact → Top 15 control X%
✅ "Where will ETH liquidations trigger?"
→ Liquidations → Check histogram peaks
✅ "Is this wallet a market manipulator?"
→ Wallet Impact + Trader Detail + P&L patterns
✅ "What price levels have the most leverage?"
→ Liquidations → High-Risk Price Levels table
✅ "Are liquidation cascades happening?"
→ Liquidations → Cumulative volume steep slopes
✅ "Who has the biggest price impact per trade?"
→ Wallet Impact → Price impact scatter
HyperAnalyze works with Hyperliquid historical data:
node_fills_*.json- Trade execution events (primary data source)misc_events_*.json- Other blockchain events
{
"local_time": "2025-10-27T17:00:00.063787823",
"block_time": "2025-10-27T16:59:59.797563798",
"block_number": 777010829,
"events": [
[
"0x4264b5a132e4f263d6de2e0d01512a99ea21ec6e",
{
"coin": "ETH",
"px": "4215.9",
"sz": "0.3565",
"side": "B",
"crossed": true,
"fee": "0.676335",
"closedPnl": "0.0",
...
}
]
]
}- Frontend: Streamlit (Python web framework)
- Visualization: Plotly (interactive charts)
- Data Processing: Pandas, NumPy
- Storage: Parquet (fast columnar format)
- Performance: Numba (JIT compilation)
Update in the sidebar or directly in dashboard.py:
data_path = "Hyperliquid Data Expanded/node_fills_YYYYMMDD_HHMM-HHMM.json"For large datasets, enable data limiting:
- Check "Limit data for testing" in sidebar
- Set max lines (e.g., 5,000-10,000 blocks)
- Loads in ~30 seconds vs 2-5 minutes
First load processes raw JSON and creates processed_data.parquet:
- First load: 2-5 minutes (full dataset)
- Subsequent loads: 5-10 seconds (from cache)
- Delete cache file to reprocess data
From Oct 27, 2025 (17:00-18:00 UTC):
- 350K+ trades across 187 coins
- 5,000+ unique traders
- Top 5 coins: BTC ($7.4M), ETH ($2.2M), TRUMP ($1.4M), SOL ($918K), HYPE ($876K)
- Top 20 traders = 40-50% of all volume
- Market concentration: Varies by asset
- Liquidation zones: Identified at key price levels
- Dynamic Order Book Heatmap
- Maker vs Taker Flow Analysis
- Spread Analysis Dashboard
- Volume Profile by Asset
- Trader Analytics
- Wallet Impact Analysis
- Liquidation Heatmaps
- Market impact visualization (Kyle's lambda)
- Order flow imbalance (OFI)
- Multi-asset correlation matrix
- Liquidity depth evolution
- Real-time WebSocket integration
- Toxicity indicators
- 3D order book surface
- Hawkes process modeling
- Price level lifetime analysis
- Spoofing detection algorithms
Contributions are welcome! Please feel free to submit a Pull Request.
# Install dev dependencies
pip3 install -r requirements.txt
# Run tests
python3 test_installation.py
# Launch development server
streamlit run dashboard.py --server.runOnSave=trueThis project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.
- Hyperliquid Docs: https://hyperliquid.gitbook.io/hyperliquid-docs/
- Hyperliquid GitHub: https://github.com/hyperliquid-dex
- Python SDK: https://github.com/hyperliquid-dex/hyperliquid-python-sdk
- Streamlit: https://streamlit.io
This tool is for research and analysis purposes only. Not financial advice. Past performance does not indicate future results. Trade at your own risk.
For questions, issues, or feature requests:
- GitHub Issues: https://github.com/ConejoCapital/hyperanalyze/issues
- Repository: https://github.com/ConejoCapital/hyperanalyze
Built with data from Hyperliquid, the performant L1 blockchain with a native DEX.
Special thanks to the Hyperliquid community for feedback and support.
⭐ If you find this tool useful, please consider starring the repository!
Last updated: October 29, 2025