Halvings compress reward schedules and commonly increase market stress, so market-making systems that assume steady liquidity or fixed reward-driven flows need adjustment. Two algorithm families are most affected: limit order book market makers and automated market makers on chained exchanges.
Limit order book algorithms and inventory models
Algorithms based on the Avellaneda-Stoikov framework formulated by Marco Avellaneda New York University are designed to balance quoted spreads against inventory risk. Around a halving, volatility and asymmetric order flow often increase as miners and speculators reprice expected future issuance. These algorithms require wider quoted spreads, dynamic inventory caps, and faster re-estimation of adverse selection and execution probabilities. If the model's parameters are estimated from calm historical data, it will underprice the jump in execution risk and suffer inventory accumulation or heavy losses.
On-chain AMMs and concentrated liquidity
Concentrated liquidity models introduced by Hayden Adams Uniswap Labs change how passive liquidity is deployed across price ranges. A halving can produce larger, one-sided price moves and transient illiquidity. Liquidity managers must adjust range widths, reduce concentration, or introduce dynamic fee schedules to prevent excessive impermanent loss and withdrawal by retail providers. Automated strategies that rebalance only on time schedules will lag rapid post-halving repricing and increase slippage for takers.
Relevance, causes, and consequences tie to network economics and human factors. The root cause is a deterministic cut to block rewards that alters miner incentives and can trigger hashrate redistribution, temporary sell pressure, and sentiment-driven trading. Consequences include wider bid-ask spreads, higher slippage, temporary order book thinning, and increased withdrawals from on-chain liquidity pools. For miners and local communities dependent on mining revenue, these technical dynamics carry cultural and territorial effects when rigs are switched off or relocated, affecting electricity demand and local employment in mining hubs. Algorithmic market makers that ignore these socio-technical feedbacks risk amplifying stress.
Practical adjustments include parameter updates to risk-aversion and execution probability in Avellaneda-Stoikov style systems, and active reallocation of liquidity ranges and fees for concentrated liquidity AMMs. During a halving window, combining conservative quoting with faster parameter learning and human oversight reduces the chance that automated systems compound transient market dislocations.