The operational process of adjusting bid and offer prices in a market, accounting for real-time conditions, liquidity, and assessed risk. In crypto trading, this function optimizes quotes within request-for-quote (RFQ) systems and institutional options platforms, balancing market participation with desired return. It ensures prices accurately reflect current market state and inventory exposure.
Mechanism
Automated systems continuously ingest diverse market data streams, including order book depth, transaction volume, volatility metrics, and external pricing. Proprietary algorithms then process this information to dynamically adjust bid-offer spreads and price levels. This process systematically manages inventory risk and maintains competitive quoting, adhering to predetermined profitability thresholds.
Methodology
A feedback-driven algorithmic approach that iteratively evaluates discrepancies between an internal fair value model and observable market prices. It integrates factors such as adverse selection risk, hedging costs, and counterparty-specific parameters into pricing logic. This systematic adjustment aims for optimal market making and risk exposure control within dynamic digital asset environments.
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