Mid-frequency Trading (MFT)
Mid-frequency trading (MFT) is a trading approach that operates in the time horizon between high-frequency trading and traditional trading strategies. MFT typically involves holding positions for minutes to hours, utilizing automated systems with moderate latency requirements and focusing on statistical arbitrage and market-making opportunities.
How mid-frequency trading works
Mid-frequency trading bridges the gap between ultra-fast trading and traditional investment approaches. MFT strategies typically:
- Hold positions for minutes to hours
- Process market data at millisecond to second intervals
- Execute multiple trades per day
- Balance speed with deeper market analysis
- Focus on liquid instruments across multiple asset classes
Unlike high-frequency trading that requires expensive colocation services and specialized hardware, MFT can operate effectively with standard enterprise infrastructure and direct market access.
Market microstructure considerations
MFT strategies must carefully consider market microstructure elements:
The strategy lifecycle involves continuous monitoring of market impact cost and liquidity conditions to optimize execution timing.
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.
Trading strategy characteristics
MFT strategies often employ:
- Statistical arbitrage across related instruments
- Mean reversion trading
- Event-driven strategies
- Relative value opportunities
- Market making with wider spreads
These approaches require sophisticated risk controls and position management systems to handle multiple concurrent positions.
Technology requirements
While less demanding than HFT, MFT still requires robust infrastructure:
- Reliable market data feed handlers
- Efficient order management systems
- Real-time risk assessment capabilities
- Automated trade surveillance
The technology stack must support consistent execution while maintaining reasonable operational costs.
Performance metrics
Key performance indicators for MFT strategies include:
- Sharpe ratio and risk-adjusted returns
- Trade execution quality
- VWAP slippage
- Position holding costs
- Market impact analysis
These metrics help optimize strategy parameters and assess overall effectiveness.
Risk management
MFT requires comprehensive risk management frameworks that address:
- Market risk from extended holding periods
- Operational risk from automated systems
- Liquidity risk during position exit
- Counterparty risk across multiple venues
Risk limits and monitoring must adapt to changing market conditions while maintaining strategy profitability.
Market impact and adaptation
MFT strategies must continuously adapt to:
- Changes in market structure
- Evolving regulatory requirements
- Competition from other traders
- Shifting liquidity patterns
- Market regime changes
This adaptation often involves machine learning and statistical techniques to identify and exploit persistent market inefficiencies.