Signal-Execution-Labs Liquidity Monitor & Order Book Analysis
Description
Real-time order book depth analysis and liquidity monitoring for optimal trade execution.
Features
- Order Book Depth - Track bid/ask depth at multiple levels
- Liquidity Scoring - Rate asset liquidity (A-F grade)
- Spread Analysis - Bid-ask spread monitoring with alerts
- Slippage Estimator - Predict slippage for order sizes
- Whale Detection - Identify large orders/walls
- Imbalance Alerts - Order book imbalance notifications
- VWAP Tracking - Volume-weighted average price
- Execution Quality - Post-trade analysis vs benchmarks
Use Cases
- Optimal Entry - Wait for better liquidity before entry
- Exit Planning - Size exits based on available liquidity
- Market Making - Identify spread opportunities
- Whale Following - Track large order movements
- HFT Signals - Microstructure-based signals
Commands
liquidity check BTC/USDT
orderbook depth ETH/USDT --levels 20
slippage estimate BTC/USDT --size 50000
spread alert BTC/USDT --max 0.1%
whale watch BTC/USDT --min-size 1000000
vwap track SPY --window 1d
execution analyze --trade-id xyz123
liquidity heatmap --top 50
Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
| levels | int | 10 | Order book depth levels |
| min_size | float | 10000 | Minimum USD for whale detection |
| spread_alert | float | 0.5% | Alert when spread exceeds |
| imbalance_ratio | float | 2.0 | Bid/ask imbalance threshold |
| update_interval | string | 1s | Real-time update frequency |
| exchange | string | auto | Exchange for order book data |
Liquidity Grades
| Grade | Spread | Depth ($1M) | Description |
|---|---|---|---|
| A+ | <0.01% | >$50M | Institutional grade |
| A | <0.05% | >$10M | Highly liquid |
| B | <0.1% | >$1M | Good liquidity |
| C | <0.5% | >$100K | Moderate |
| D | <1% | >$10K | Thin |
| F | >1% | <$10K | Illiquid - avoid |
Outputs
- Real-time order book visualization
- Liquidity score and grade
- Slippage estimates for order sizes
- Whale order alerts
- Spread history charts
- Execution quality reports
Technical Implementation
- WebSocket connections to exchange order books
- Redis for real-time data caching
- Time-series database for historical depth
- Streaming aggregation for VWAP
- ML models for slippage prediction
Risk Considerations
- Order books can be spoofed (fake orders)
- Liquidity can evaporate quickly in volatility
- Different exchanges have different depth
- Latency matters for execution decisions