# A.M.M.Q Definition

UNIKO A.M.M.Q (Adaptive Market Making Quant) is the execution and intelligence framework that drives trading operations. It combines:

·      Quant research discipline

·      Automated execution and routing

·      Liquidity-aware response

·      AI-assisted optimisation

·      Continuous monitoring and risk governance

#### Core components

**Quant Research Console**

Purpose:

·      Market observation and spread structure analysis

·      Signal construction and hypothesis formation

·      Backtesting and historical validation

·      Parameter review and performance diagnostics

Outputs:

·      Strategy candidates and parameter sets

·      Risk budgets and exposure constraints

·      Recommended execution profiles per market regime

**Strategy Decision and Allocation Engine**

Purpose:

·      Convert research outputs into live decisions under constraints

·      Allocate capital across strategies and venues

·      Select execution style (maker vs taker, CEX vs DEX, hybrid routing)

Typical inputs:

·      Volatility regime classification

·      Liquidity depth and slippage sensitivity

·      Risk budgets (inventory limits, drawdown limits)

**Execution Engine**

Purpose:

·      Translate strategy intent into market actions

·      Handle order decomposition, timing, and route selection

·      Track fills, cancellations, and execution quality

·      Enforce slippage controls and guardrails

Typical execution primitives (examples):

·      Passive quoting (maker orders)

·      Adaptive grid execution

·      Inventory rebalancing

·      Multi-venue routing

·      Slippage-aware DEX swaps

Key metrics:

·      Realised spread capture

·      Slippage and adverse selection

·      Fill rate and cancellation ratio

·      Latency and rejection or failure rate

**Liquidity Support Module**

Purpose:

·      Monitor local depth and liquidity deterioration

·      Reduce friction caused by shallow depth or unstable market states

·      Coordinate with execution and risk systems in real-time

Example behaviours:

·      Reduce order size when depth thins

·      Widen quoting bands during volatility spikes

·      Pause execution on a degraded venue

·      Prefer venues with healthier liquidity states

**Risk Console**

Purpose:

·      Centralise exposure control and anomaly monitoring

·      Enforce thresholds and circuit breakers

·      Supervise system health continuously (not after damage occurs)

Controls include:

·      Position and exposure limits

·      Execution anomaly detection

·      Venue health supervision

·      Drawdown-based throttles

·      Kill-switch and recovery procedures

**Data Intelligence Layer**

Purpose:

·      Standardise inputs from order books, trades, execution logs, and on-chain sources

·      Provide synchronised, quality-supervised data to all layers

·      Support model training and long-term performance analysis

Data types:

·      Market data: trades, top-of-book, depth snapshots, funding/fees (as applicable)

·      Execution data: fills, cancellations, rejects, slippage outcomes

·      On-chain data: wallet activity, pool liquidity changes, event-driven shifts


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