Systematic Arbitrage
Systematic arbitrage refers to automated trading strategies that identify and exploit price discrepancies across related financial instruments, markets, or asset classes. These strategies use mathematical models and computer algorithms to detect temporary mispricings and execute trades to capture risk-adjusted profits while maintaining market neutrality.
Core principles of systematic arbitrage
Systematic arbitrage combines quantitative analysis with automated execution to identify and capitalize on market inefficiencies. Unlike traditional manual arbitrage, systematic approaches:
- Monitor hundreds or thousands of instruments simultaneously
- Execute trades automatically when opportunities arise
- Manage positions and risk exposure programmatically
- Scale across multiple markets and asset classes
The key advantage is the ability to detect and act on small price discrepancies faster and more consistently than human traders.
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Types of systematic arbitrage strategies
Statistical arbitrage
Statistical arbitrage strategies identify temporary price divergences between historically correlated instruments. These strategies typically:
- Calculate expected price relationships using statistical models
- Monitor for deviations from expected relationships
- Trade when divergences exceed statistical thresholds
- Unwind positions when prices revert to expected levels
Cross-market arbitrage
Cross-asset trading strategies exploit price differences for the same or similar instruments trading on different venues. This includes:
- Exchange vs OTC markets
- Different geographic locations
- Cash vs futures markets
- ETFs vs underlying baskets
Volatility arbitrage
Volatility arbitrage strategies capitalize on inconsistencies in implied volatility across related options or other derivatives. These strategies often involve:
- Options on the same underlying with different strikes/expirations
- Options vs. futures
- Cross-asset volatility relationships
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Risk management considerations
Market neutrality
Systematic arbitrage strategies typically aim to maintain market neutrality by:
- Balanced long/short positions
- Delta hedging of options positions
- Cross-asset hedging
- Risk factor neutralization
Execution risk
Trade execution quality is critical for arbitrage strategies since profits depend on capturing small price differences. Key considerations include:
Model risk
Systematic strategies rely heavily on mathematical models, introducing several risk factors:
- Model assumptions may break down
- Market regime changes can invalidate historical relationships
- Parameter estimation errors
- Data quality issues
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Technology infrastructure
Data processing requirements
Systematic arbitrage requires robust infrastructure for:
- Real-time market data processing
- Historical data analysis
- Signal generation
- Risk calculations
- Position management
Execution systems
Trading infrastructure must support:
- Low-latency order execution
- Smart order routing
- Risk controls
- Position monitoring
- P&L calculation
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Regulatory considerations
Systematic arbitrage strategies must comply with various regulations:
- Market Abuse Regulation (MAR)
- Pre-trade risk checks
- Best execution policies
- Market manipulation rules
Firms must implement robust compliance monitoring and reporting systems to ensure regulatory requirements are met.
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Market impact and evolution
The proliferation of systematic arbitrage has led to:
- Increased market efficiency
- Reduced arbitrage opportunities
- Higher technology requirements
- Evolution of more sophisticated strategies
- Focus on alternative data and ML/AI approaches
As markets become more efficient, systematic arbitrage strategies continue to evolve, seeking new sources of alpha and expanding into new asset classes and markets.