The Mercury Trading System faces an interesting challenge: how to effectively utilize large language models (LLMs) for comprehensive trading analysis while maintaining reasonable response times and resource utilization. This post explores our approach to decomposing a complex trading analysis system into manageable, sequential tasks that can leverage high-performance models efficiently.
This Addendum addresses specific operational challenges, regulatory requirements, and implementation alternatives for the Hermes Crypto Sentiment Analysis System. Its goal is to complement the main strategy by covering:
All references to concepts (phases, basic architecture, 4-hour quick start code, etc.) already detailed in the main Hermes Strategy have been omitted here to avoid duplication.
In the latest update to Mercury Bot (v0.126.0), we've introduced a significant improvement to our market ranking system using the QuickSelect algorithm. This optimization reduces the number of comparisons needed to find the top K markets, making our analysis more efficient and responsive.
Our previous implementation compared every market with every other market to establish rankings. With N markets, this resulted in O(N²) comparisons. For 100 markets, we needed up to 4,950 comparisons! This was: