Quote Filtering Efficacy quantifies the effectiveness of an automated system’s ability to selectively accept or reject liquidity provider quotes based on predefined quality and suitability criteria. It measures how successfully the filtering process achieves optimal trade execution outcomes in dynamic crypto markets.
Mechanism
Efficacy is quantitatively assessed by evaluating the difference between the actual execution price and a theoretical optimal price, accounting for factors like market impact, latency, and spread. Key performance indicators include reductions in slippage, improvements in fill rates, and minimized transaction costs. The underlying filtering mechanism employs various parameters such as price, size, and counterparty reliability.
Methodology
The assessment involves rigorous backtesting of filtering algorithms against extensive historical market data and continuous real-time performance monitoring. This validates their capacity to identify and select the most competitive and suitable quotes. Continuous refinement of filtering parameters and rules, based on empirical data, ensures the system consistently selects optimal liquidity, thereby enhancing overall trading profitability and risk management.
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