Implementation Architecture

The arbitrage engine is implemented as a distributed system that processes real-time market data across multiple cryptocurrency exchanges. The architecture is designed for high-frequency operation with sub-millisecond latency requirements and comprehensive risk management integration.

Data Flow Architecture

The system processes market data through a multi-stage pipeline:

WebSocket Streams → Redis Pub/Sub → Buffered Processor → Arbitrage Calculator → Opportunity Logs

Stage 1 - Data Ingestion:

• WebSocket connections maintain persistent streams from all exchanges

• Funding rates and orderbook updates are published to Redis channels

• Buffered message processor handles high-frequency data bursts

• Connection state monitoring ensures reliable data flow

Stage 2 - Data Processing:

• Redis acts as the central data hub for all market information

• Structured key-value storage enables fast lookups and updates

• Historical data retention supports backtesting and analysis

• Pub/sub mechanism enables real-time data distribution

Stage 3 - Arbitrage Calculation:

• Parallel processing of all exchange and asset combinations

• Real-time fee calculation and profitability assessment

• Risk parameter integration for position sizing

• Opportunity prioritization based on profit potential

Stage 4 - Execution and Monitoring:

• Redis-based logging of all arbitrage opportunities

• Performance metrics tracking and alerting

• Historical analysis for strategy optimization

• Dashboard integration for real-time visualization

Processing Pipeline Details

The arbitrage detection follows a systematic workflow:

1. Data Synchronization:

   • Continuous collection from 3 exchanges (Aster, Backpack, zkLighter)

   • Market data validation and timestamp synchronization

   • Orderbook depth analysis and liquidity assessment

2. Arbitrage Calculation Engine:

   • Parallel computation across all trading pairs (158+ combinations)

   • Multi-dimensional analysis (temporal, spatial, financial)

   • Fee structure integration by exchange and market type

   • Profitability thresholds with risk-adjusted parameters

3. Opportunity Validation:

   • Net profit calculation after all fees and costs

   • Slippage estimation based on orderbook depth

   • Liquidity risk assessment and position limits

   • Market impact analysis for large orders

4. Risk Management Integration:

   • Position sizing using Kelly Criterion optimization

   • Drawdown protection and stop-loss mechanisms

   • Exposure limits per exchange and asset

   • Circuit breaker activation for extreme conditions

5. Logging and Analytics:

   • Structured opportunity logging in Redis

   • Performance metrics collection and aggregation

   • Historical analysis for strategy refinement

   • Alert generation for significant opportunities

Redis Data Structure

The system uses a hierarchical key structure for efficient data organization:

Market Data Keys:

exchange:token:market_type
├── aster:BTC:spot      → {"symbol": "BTCUSDT", "best_ask": 45000, ...}
├── backpack:ETH:futures → {"symbol": "ETH_PERP", "best_bid": 2800, ...}
└── lighter:SOL:futures  → {"symbol": "SOL/USD", "funding_rate": 0.0001, ...}

Opportunity Logs:

arb:timestamp:type:exchange:asset → {
    "strategy": "CROSS_EXCHANGE",
    "profit_pct": 0.25,
    "fees_pct": 0.15,
    "net_profit_pct": 0.10,
    ...
}

Performance Metrics:

metrics:arbitrage:daily → {
    "opportunities_found": 450,
    "profitable_trades": 23,
    "success_rate": 87.0,
    "avg_profit_pct": 0.08,
    ...
}

The system is optimized for high-frequency arbitrage detection:

Latency Metrics:

• Detection Latency: <1ms from data receipt to opportunity identification

• Calculation Speed: Processes 158 trading pairs in parallel

• WebSocket Throughput: Handles 3000+ messages per second per exchange

• Redis Operations: Sub-millisecond key lookups and updates

Scalability Parameters:

• Concurrent Connections: 9 WebSocket streams (3 exchanges × 3 data types)

• Memory Usage: Efficient buffering with automatic cleanup

• CPU Utilization: Multi-threaded processing with async operations

• Network Bandwidth: Optimized message batching and compression