RFQ Backtest refers to the analytical process of evaluating the historical performance of a Request for Quote (RFQ) trading strategy or a specific liquidity provider’s quotes against past market data. In crypto RFQ environments, this involves simulating trades using historical quote requests and actual market prices to assess execution quality, pricing competitiveness, and overall trading costs. Its purpose is to validate the effectiveness of a trading approach or the reliability of a dealer’s quotes under various historical market conditions.
Mechanism
The operational mechanism of an RFQ backtest typically involves reconstructing historical RFQ interactions by feeding past quote requests into a simulated trading engine. This engine then applies the evaluated strategy or uses recorded dealer quotes, comparing the simulated execution prices against a benchmark such as the true market mid-price at the time. Key metrics like slippage, spread capture, and fill rates are calculated to quantify performance over different time horizons and market regimes.
Methodology
The strategic approach behind RFQ backtesting aims to optimize trading algorithms and refine dealer selection processes by providing empirical evidence of past performance. By rigorously testing hypotheses against historical data, institutional investors and market makers can identify profitable strategies, quantify execution costs, and mitigate future risks. This methodology extends knowledge by offering a systematic, data-driven framework for improving trading decisions and platform configurations, enhancing efficiency and profitability in institutional crypto trading.
CLOB anonymity simplifies backtesting by replacing complex, assumption-heavy models of dealer behavior with data-driven simulations of market mechanics.
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