The enhancement of the success ratio for quotes provided by a market-making or algorithmic trading system in crypto markets, particularly within a Request for Quote (RFQ) framework. It refers to increasing the proportion of quotes that are accepted and result in executed trades, relative to the total number of quotes issued. This metric directly reflects the effectiveness of pricing models and liquidity provision.
Mechanism
Hit rate improvement is achieved by refining the underlying pricing algorithms and risk parameters to better align quotes with prevailing market conditions and counterparty expectations. This involves incorporating more granular data feeds, improving latency in price generation, and dynamically adjusting spreads based on inventory levels and market volatility. Feedback loops from executed trades and rejected quotes are used to adapt the pricing logic.
Methodology
The strategic approach focuses on data-driven optimization of quoting parameters. This includes analyzing historical trade data, counterparty behavior, and market microstructure to identify patterns that lead to higher acceptance rates. A/B testing of different pricing strategies and continuous machine learning model training are applied to iteratively enhance the precision and competitiveness of generated quotes, thereby improving execution probability.
The key performance indicators for quote lifetime prediction models center on execution quality, risk mitigation, and capital efficiency, driven by real-time predictive accuracy.
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