Unlocking Success: Why MELT+ is Essential for Mastering Algorithmic Trading and Automated Systems
MELT+ refers to a comprehensive framework encompassing Metrics, Events, Logs, Traces, and Performance Profilers — a holistic approach designed to enhance the efficiency and reliability of algorithmic trading.
Metrics provide quantifiable insights into system, performance and up time. Events offer real-time updates on predefined scenarios, logs capture transaction details for analysis, traces trace the execution flow, and performance profilers pinpoint areas for optimization.
It empowers traders to pinpoint bottlenecks, address problems, and enhance strategies with a proactive and pre-emptive approach. With MELT+, algorithmic traders gain a competitive edge, ensuring their systems operate seamlessly and adapt swiftly to adverse and unfavourable scenarios. MELT+ is the linchpin for precision and agility in algorithmic trading strategies.
In this article, I will elucidate using an example of the reference architecture employed at ErgoSum Technologies for researching and constructing our technical blueprints.
This foundational framework encompasses live data ingestion, including Level 1 (L1), Level 2 (L2), and Option Data. The data feeds into Trade Signal Analysis, incorporating technical patterns, and Price Action Classifiers. Further involves Dynamic Model Selection and Forecast Generation. Human Reinforcement Learning (RL) observation and inputs are integrated, along with Observers representing key algorithmic logic. These components collectively contribute to trade execution and the continuous monitor order books in a seamless and dynamic algorithmic trading setup.
Now, let’s delve into practical and real-world scenarios that I’ve encountered over the past decade, involving advanced and intricate distributed trading architecture.
Understanding When an Application Fails, Stops, or Crashes is “Not Good Enough”
It’s evident that observability informs us when a system fails — a rather straightforward notion. Addressing downtime or failover scenarios swiftly, observability enables intervention at a faster pace, but unfortunately, the damage is often already incurred by that point. For serious investment banking, hedge funds, and buy-side firms, merely knowing when a system fails falls short of meeting the requisite standards.
However, algorithmic trading introduces intriguing challenges, particularly in the realm of High-Frequency Trading (HFT) and non-directional trade executions with low latency. This is where live logging, business events, distributed traces, and profilers come into play. By triangulating these capabilities, potential failures can be proactively averted, and remedial measures can be implemented well in advance of any impending disaster.
Silent Killer: The Impact of Untracked System Component Performance is Opportunity Loss
In many consulting engagements, found a prevalent observation that while many prioritize and invest significantly in addressing “Network Latency” issues — a commendable focus — a substantial portion of these organizations grapple with suboptimal application designs for various reasons.
Continuous and live profilers play a pivotal role in ensuring continuous, unobtrusive tracking of components. This proactive approach proves instrumental in identifying critical precursors of potential loss, including stress levels of components, underlying infrastructure health, memory issues, and overall poor designs. These factors collectively contribute to significant delays, with observed underperformance ranging from 100ms to 2000ms during continuous profiling analysis.
The Stress Key Performance Indicators (s-KPIs) of components enable teams to intervene proactively, mitigating the risk of system failures before they occur.
Behind Enemy Lines: Leveraging MELT+ to Backtest Infrastructure and Application Optimization for Auto-Corrective Measures with Synthetic Observability (AI Agents)
Leveraging collected observability data provides a valuable opportunity to conduct backtesting for infrastructure and application enhancements. By analyzing the data, can reconstruct the entire trade cycle, scrutinizing each component’s performance and interactions.
This retrospective examination becomes a powerful tool to validate the Return on Investment (RoI) of implemented improvements and assess overall system availability. Backtesting allows for a comprehensive evaluation of the applied changes in a controlled environment, shedding light on how the system would have performed under past market conditions.
It serves as a crucial step in ensuring that modifications not only meet their intended objectives but also stand the test of historical market scenarios. This iterative process of using observability data to refine and validate improvements becomes instrumental in fostering a resilient and high-performing algorithmic trading system.
More on MELT+
Stay tuned for upcoming articles delving into the technical intricacies of implementing each MELT+ component and its seamless integration with the aforementioned blueprint. For those eager to dive into the finer details, consider following or subscribing to ensure access to the entire series.
About Author
As the creator of ErgoQuantX, I am excited to introduce an ecosystem that has been meticulously crafted to incorporate the power of popular Open Source Stack and Public Cloud.
If any of these topics intrigue you, you’ve come to the right place to join us on our exploration of Rust, Python, Kafka, MLFlow, TimescaleDB, Spark, Azure Data and Apache Iceberg within the realms of system trading, algorithmic trading, and ML and AI in the financial market arena. Stay connected with us on LinkedIn to follow our journey.
