Automated Workflow for Quantitative Market-Making on Derivatives Exchanges

This core pricing agent continuously ingests underlying price feeds and options market data to calculate Delta, Gamma, Vega, and Theta for the entire book. It dynamically adjusts the firm's volatility surface using realized vs. implied volatility signals and competitor quote analysis. Built for sub-millisecond latency, it outputs a stream of theoretical values that feed the quoting engine.

Implementation: Typically deployed as a stateful service using C++/Rust for core math, integrated with a high-frequency data fabric (e.g., Aeron, Solace). Requires continuous calibration against liquid option chains to prevent model drift.

This agent monitors the net Delta, Gamma, and Vega exposure across all traded strikes and expiries. It calculates the inventory's sensitivity to market moves and dynamically adjusts quoting spreads or triggers hedging orders in the underlying futures market. The logic includes pre-defined risk limits, P&L attribution by Greek, and circuit-breakers to pause quoting if inventory breaches thresholds.

Implementation: An event-driven service consuming position updates from the execution layer. It integrates with the firm's risk system (e.g., Murex, Sophis, custom) and sends hedging instructions to the smart order router. Exception routing to a human risk manager is required for limit breaches.

This execution agent receives priced quotes from the Greeks agent and risk-adjusted spreads from the inventory agent. It manages the lifecycle of thousands of live quotes across multiple exchanges (e.g., CME, ICE, Eurex), handling fills, cancellations, and modifications. The router implements venue selection logic based on liquidity, fee structures, and fill rates, and can switch between aggressive and passive quoting styles based on market conditions.

Implementation: A low-latency C++ application directly connected to exchange APIs (FIX/ITCH) or via a vendor gateway. Requires a dedicated quoting risk gateway (QRG) to enforce exchange-mandated quote-to-trade ratios and maximum order counts.

This observational agent analyzes the order book dynamics, fill rates, and the quoting behavior of identified competitors. It detects regime shifts (e.g., from normal to volatile markets) and signals when to widen spreads, reduce size, or withdraw from certain strikes. It uses statistical models to identify toxic order flow and potential adverse selection.

Implementation: A Python/Julia service using streaming analytics (e.g., Apache Flink, ksqlDB) to process level-2 market data. Its signals are fed as contextual features into the quoting engine's spread adjustment logic. Requires a feedback loop from the P&L attribution system to validate signal efficacy.

This operational agent performs real-time trade matching with counterparties and prime brokers, ensuring the firm's book is accurate. It attributes P&L to market moves (Delta, Vega), realized spread, financing costs, and fees. Breaks and anomalies are automatically classified and routed to operations dashboards. This component is critical for understanding profitability per product and for daily risk reporting.

Implementation: Integrates with the firm's OMS/EMS and settlement systems (e.g., Broadridge, Calypso). Built using an event-sourcing pattern to maintain an immutable audit trail of all trades, quotes, and risk states for regulatory reconstruction.

This is the central nervous system of the workflow, implemented using a framework like LangGraph or Temporal. It defines the state machine governing agent interactions: starting/stopping the system, managing failover between primary and disaster recovery sites, and enforcing global kill switches. It sequences the daily startup routine (data warm-up, risk limit loading) and coordinates graceful shutdowns.

Implementation: A control plane service that exposes a UI/API for traders and engineers. It monitors heartbeats from all agents, and if an agent fails or latency spikes beyond a threshold, it can automatically divert flow, trigger a hedging close-out, or halt all quoting. All control actions are logged with pre-and-post state for audit.

This team defines the core pricing and risk models. They are responsible for developing the mathematical logic for Greeks calculation, volatility surface fitting, and spread-adjustment algorithms. Their work is encapsulated in model libraries (often Python/C++) that the engineering team containerizes and deploys. They validate model performance against historical data and define the key performance indicators (KPIs) for spread capture and inventory risk.

Owns the real-time execution fabric. This team architects the data ingestion pipelines for exchange price feeds (using protocols like ITCH/OUCH), builds the order management system (OMS), and implements the smart order router (SOR) for multi-venue quoting. They ensure microsecond-level latency through kernel-bypass networking, FPGA/GPU acceleration for model inference, and robust CI/CD for strategy deployment and rollback. They integrate with systems like KX kdb+ or proprietary time-series databases.

Provides the human-in-the-loop governance layer. This team sets the real-time pre-trade risk limits (position, Greeks, concentration) and monitors the autonomous system via dashboards streaming live P&L, inventory skew, and quote activity. They define the escalation matrix for exception handling—such as a "kill switch" for extreme market events—and manage the approval gates for model parameter changes or new product onboarding. They are the bridge between autonomous logic and regulatory compliance.

Builds the foundational data utility. This team is responsible for the reliability of all inbound data (market data, volatility surfaces, reference data) and the storage of high-frequency tick data for post-trade analysis (TCA) and model retraining. They implement data quality checks, schema management, and the historical replay capabilities crucial for strategy backtesting. They often manage relationships with data vendors (e.g., Refinitiv, Bloomberg) and alternative data providers.

Manages the commercial and technical external interfaces. This team negotiates exchange connectivity agreements, colocation contracts, and fee structures. They work with engineering to certify the firm's systems with each derivatives exchange (e.g., CME, Eurex, ICE) and manage the onboarding process for new trading venues or products. They also handle relationships with prime brokers for clearing and financing, ensuring the operational flow for margin and settlement.

Ensures the books and records are accurate. Post-trade, this team oversees the automated reconciliation workflow between the internal OMS and the prime broker/custodian statements. They manage the exception queue for trade breaks (DKs) and ensure straight-through processing (STP) for standard trades. They are also responsible for daily P&L attribution, collateral management automation, and generating accurate financial reports for internal and regulatory purposes.