Effective algo-monitoring goes beyond standard surveillance tools

The challenge firms are facing….

large body of applicable regulations and guidelines globally to keep track of and understand – e.g., ESMA, Mifid II, FCA, Bank of England, SEC, FINRA, HKMA, ASIC etc. have all issued stuff
• regulatory expectations continuously evolving, as are business practices
• needs to be done in real-time
• data – volumes, messy, difficulty sourcing timely data
• not always clear what is an algo. in a large firm could be gaps in the governance, for example, and there are nuances – the partially automated, Smart order routers etc. – not just a fully-automated thing that executes trades. there are grey areas between the manual and the automated and different types of automated trading, for control teams to look at and design monitoring for.

How can firms address these problems?

Ongoing tracking of regulatory expectations – there are services you can subscribe to that help with this challenge (or you can build your own, as NLP and AI tooling improves)
• Robust data framework
  • Establish a robust data architecture that incorporates data lineage tracking.
  • Implement data reconciliation mechanisms to identify and rectify data quality issues in real-time.
  • Utilize data profiling tools to monitor data integrity and ensure compliance with regulatory reporting requirements.
  • Implement data lakes or warehouses to store historical data for compliance audits and investigations.
• need a monitoring system that can keep up with the real-time challenges
  • Deploy a time series database optimized for in-memory queries to handle large volumes of real-time data efficiently.
  • Implement complex event processing (CEP) engines for real-time data analysis and alert generation.
  • Utilize stream processing frameworks like Apache Kafka and Apache Flink for handling high-frequency data streams.
• Flexible framework that enables you to write, configure and and test your own alerts
  • write alerts in tools like Python, or GUI-driven approach
  • back-testing capability
  • You need a flexible alerting system that allows customization and configuration of alerts based on specific regulatory requirements.
  • One that provides scripting capabilities in languages like Python to enable users to define custom alert logic.
  • And / or incorporates a GUI-driven approach for creating and managing alerts, making it accessible to non-technical users and ensuring full audit trail of all changes
  • Implement back-testing capabilities to assess the effectiveness of alerts and refine them over time, while providing full audit trail of all tests
  • Enables promotion of changes into the production monitoring library in a controlled way
• SME understanding of types of algo trading allied with anomaly detection
  • collaborate with subject matter experts (SMEs) who understand different types of algo trading strategies and market behavior.
    • what’s normal / abnormal for the strategies they are supposed to be executing on, and how markets behave, etc. – e.g. if you’re market making that has some specific expectations attached to it, whereas if its an algo that’s trying to execute client orders with minimal price impact across multiple venues its expected behaviour is very different – you need to configure / adapt for these different behavioural patterns to spot what’s unusual / abnormal, or in breach of specific regulatory demands
  • Develop anomaly detection models that leverage machine learning techniques to identify abnormal behavior.
    • ability to spot changing behavioural patterns that could indicate a re-written algo, or newly deployed algo, that requires review – anomaly detection
  • Configure the system to recognize normal/abnormal behavior patterns for specific trading strategies.
  • Implement continuous monitoring of behavioral patterns and alerting for deviations.
  • Use statistical process control (SPC) methods to detect shifts in trading behavior indicative of potential issues or violations.
• powerful visualisations
  • Create interactive dashboards and visualizations to provide a comprehensive overview of trading activities.
  • Utilize data visualization libraries such as D3.js, Matplotlib, or Tableau for creating informative charts and graphs.
  • Implement real-time visualizations to monitor key performance indicators (KPIs) and anomaly alerts.
• audit trail / case management etc.
  • Implement a robust audit trail system to track all actions, decisions, and alerts generated by the monitoring platform.
  • Develop a case management module that allows for the systematic investigation and resolution of alerts.
  • Ensure compliance with data retention and archiving regulations for audit trail data.
  • Integrate with incident management tools to facilitate efficient case resolution and reporting to regulatory authorities.

By addressing these technical aspects, financial institutions can build a comprehensive algo monitoring system that not only meets regulatory compliance but also enhances risk management and anomaly detection capabilities in an increasingly complex trading environment.

If you would like to find out more about algo monitoring, 1st Line of Defence control frameworks and risk technology, please contact us at….