Author: YQ
Translation: Yangz, Techub News
According to different data sources, the scale of cryptocurrency liquidation events from October 10 to 11, 2025, is estimated to be between $19 billion and $40 billion—this makes the obscure technical mechanism of Automatic Deleveraging (ADL) a harsh reality experienced by millions of traders.
What is Automatic Deleveraging (ADL)?
Essentially, ADL is a solvency protection mechanism for leveraged trading systems. When a trader's margin is insufficient, and closing positions through regular market mechanisms would lead to bad debts, the system will forcibly close profitable positions in the market to absorb the losses.
Its operational model can be understood as a three-tier waterfall risk control system:
Level One—Market Forced Liquidation: When the margin falls below the maintenance margin, the exchange attempts to close positions through the order book. If the liquidation price is better than the bankruptcy price (the price at which the margin goes to zero), the remaining funds will flow into the insurance fund.
Level Two—Insurance Fund: When market forced liquidation may lead to bad debts, the insurance fund will bear the losses. This fund comes from the exchange's capital injection and the accumulation of historical liquidation surpluses.
Level Three—Automatic Deleveraging: When the insurance fund cannot cover the losses, the system will identify profitable positions and forcibly close them to maintain the overall solvency of the platform.
Analysis of Automatic Deleveraging Ranking Algorithms of Major Exchanges
By analyzing the implementation plans of different exchanges, I found that although the core concepts are consistent, each exchange adjusts its calculation formulas based on its risk philosophy. Here are the algorithms actually used:
Binance:
- Profit Position Ranking Formula: ADL Ranking Value = Return Rate% × Effective Leverage
- Loss Position Ranking Formula: ADL Ranking Value = Return Rate% / Effective Leverage
Where: Return Rate% = Unrealized Profit / Absolute Value (Position Nominal Value); Effective Leverage = Absolute Value (Position Nominal Value) / (Account Balance - Unrealized Loss + Unrealized Profit)
Bybit: ADL Ranking Value = Leveraged Profit and Loss
Profit Position = (Mark Price / Average Opening Price - 1) × Leverage Multiple
Loss Position = (Mark Price / Average Opening Price - 1) / Leverage Multiple
Note: Bybit explicitly states that even loss positions may be selected, but profitable positions are prioritized.
OKX: ADL ranking uses a standard formula but is equipped with dynamic triggering conditions. Automatic deleveraging is triggered under three conditions:
Rapid Consumption: Insurance fund drops by 30% within 8 hours
Volatility Threshold: Fund net value 8-hour average - Maximum Value (30%, $50,000)
Liquidity Risk: Forced liquidation cannot be executed due to insufficient market depth
Hyperliquid:
- ADL Ranking Index = (Mark Price / Opening Price) × (Nominal Position Value / Total Account Assets)
This clever formula captures both the profit ratio and the proportion of the position relative to the total account size in a single calculation.
Common Characteristics of All Formulas
Despite the differences, all ADL formulas follow core principles:
Profit Amplification: A higher percentage of unrealized profit will increase ADL priority
Leverage Multiplier: A higher effective leverage will significantly improve ranking
Direction Isolation: Long and short positions are ranked independently
Profit Priority: Loss positions will only be selected after all profitable positions are exhausted
These subtle differences reflect the risk management philosophies of each exchange: Binance considers the overall account status through detailed calculations of effective leverage; Hyperliquid's ratio method is mathematically elegant; OKX's multiple triggering mechanisms demonstrate precision beyond simple capital consumption.
These formulas ensure that the positions with the highest leverage and profits will be prioritized for deleveraging. In other words, those who take the greatest risks in pursuit of the highest returns will bear the cost of maintaining system stability—this mechanism embodies a cruel yet precise aesthetic.
CEX vs. DEX: Two Fundamental Differences in Automatic Deleveraging
The implementation of automatic deleveraging reveals profound ideological differences between CEX and DEX. Analyzing their performance during the "10.11" crisis, the contrast is striking.
CEX's Automatic Deleveraging: Black Box Model
CEX operates automatic deleveraging with extremely low transparency, creating what I call an "uncertainty premium" in trader behavior:
Opaque Trigger Mechanisms: The levels of the insurance fund and consumption thresholds are always strictly confidential business secrets
Internal Execution: Automatic deleveraging occurs within a private matching engine, completely lacking external visibility
Limited Reporting Mechanisms: Only filtered and often "purified" data is released post-event
Discretionary Power: Manual intervention can modify rules without prior notice or disclosure
In this crisis, such opacity fostered market panic. Traders were unable to assess their actual risks, leading to a surge in preemptive liquidation actions, thereby amplifying the chain reaction of the market crash. This is a typical case where the lack of transparency caused more severe consequences than the risks themselves.
DEX's Automatic Deleveraging: A Double-Edged Sword of Transparency
DEX implements automatic deleveraging through smart contracts, forming a mechanism that is completely transparent but lacks flexibility:
Public Parameters: All trigger conditions and ranking formulas are visible in the code
Real-Time Observability: Each automatic deleveraging event is immutably recorded on the blockchain
Immutable Rules: The execution process of smart contracts completely excludes the possibility of manual intervention
Verifiable Fairness: Anyone can audit and verify whether automatic deleveraging strictly follows established rules
Hyperliquid's performance during this crisis perfectly illustrates the advantages and limitations of this transparency. The platform triggered 35,000 automatic deleveraging events among 20,000 users, providing an unprecedented dataset for analysis. However, this transparency also exposed the mechanical cruelty of the system—smart contracts cannot recognize when they will destroy a carefully constructed hedging portfolio.
Analysis of the Pros and Cons of Automatic Deleveraging
After studying thousands of automatic deleveraging cases during October, I have developed a more nuanced understanding of this controversial mechanism.
Arguments in Favor of Automatic Deleveraging
Preventing Exchange Bankruptcy: Without automatic deleveraging, exchanges would face bankruptcy when losses exceed reserve funds. The collapse of an exchange means that all users' assets go to zero. Automatic deleveraging at least maintains platform operations, allowing trading to resume.
Maintaining Market Integrity: By preventing the accumulation of bad debts, automatic deleveraging ensures that the zero-sum nature of perpetual contracts remains mathematically intact. For every dollar lost in the system, there is necessarily a corresponding dollar gained elsewhere.
Providing Risk Transparency: The ADL five-level indication system, while not perfect, provides traders with the ability to assess their deleveraging risks in real-time. This at least theoretically allows for dynamic position management.
Optimizing Exit Timing: Contrary to intuition, data from October shows that automatic deleveraging actually helped many traders by forcing short positions to close near the market bottom. Without this mechanism, most would have continued holding during the rebound, reducing profits.
Arguments Against Automatic Deleveraging
Punishing the Successful: Automatic deleveraging explicitly prioritizes the most profitable traders. This creates a distorted incentive structure—overly successful traders become a burden, akin to punishing top students to help struggling ones.
Disrupting Portfolio Hedging: When automatic deleveraging destroys carefully constructed hedging strategies, it can have the most devastating impact. A profitable short position used to hedge long risk, if forcibly closed, may trigger a chain loss across the entire portfolio.
Time Blind Spots: Even with warning indicators, automatic deleveraging can still trigger within minutes during extreme volatility. During this crisis, traders' risk levels surged from level one to level five within 300 seconds—beyond human reaction limits.
Socializing Individual Losses: Automatic deleveraging essentially forces profitable traders to involuntarily bear the losses of others' failed positions. This contradicts the fundamental principle of individual responsibility in the market.
Is Automatic Deleveraging a Blessing or a Curse in the "10.11" Crash?
The truth revealed by the data is more complex than initial reports suggested. My analysis shows that automatic deleveraging is both a savior and a destroyer, often playing both roles simultaneously.
Positive Effects of Automatic Deleveraging
Analysis of Hyperliquid's transparent data reveals a surprising phenomenon: automatic deleveraging actually improved the profit and loss of the vast majority of short positions. Why? Timing. The 35,000 automatic deleveraging events concentrated within a 5-minute window (UTC time 21:16-21:21), during which most asset prices were at the bottom. The forced liquidation at Bitcoin's $102,000, while painful at the time, proved to be a wise move when the price rebounded to $108,000 within hours.
The solvency of trading platforms remained stable overall. Despite facing the largest liquidation event in crypto history, all major exchanges did not go bankrupt. The HLP insurance fund even achieved a profit of $40 million in a single day, proving that liquidity provision during a large-scale automatic deleveraging can still generate profits.
The system prevented worse outcomes. Without automatic deleveraging, a chain of bankruptcies could have paralyzed the market for days or even weeks. This mechanism ensured that the market returned to normal trading within hours after the peak of the crisis.
Negative Effects of Automatic Deleveraging
The real damage stems from the market structure failures that made automatic deleveraging necessary. Between UTC time 20:40 and 21:35, market makers orchestrated what I call a "coordinated exit." Market depth plummeted by 98% from $1.2 million to just $27,000. This was not panic—but rather a calculated self-preservation strategy when institutions discovered that 87% of positions were long and anticipated the subsequent trend.
Automatic deleveraging amplified the chain reaction by destroying portfolios. A documented typical case involved a trader holding a $5 million BTC long (3x leverage), hedged with a $500,000 DOGE short (15x leverage), while also holding a $1 million ETH long (5x leverage). The highly leveraged profitable DOGE short was automatically deleveraged first, and after losing its hedging protection, the BTC and ETH positions subsequently liquidated within minutes. This ultimately led to the complete collapse of the portfolio.
Infrastructure failures exacerbated the situation. As one analyst pointed out: "Chain liquidations overwhelmed servers with millions of requests. Market makers could not post buy orders in time, creating a liquidity vacuum." The feedback loop between automatic deleveraging triggers and infrastructure failures caused unprecedented destructive power.
Better Design for Automatic Deleveraging Mechanisms
Based on the empirical evidence from this crash, the automatic deleveraging system should be improved in the following ways:
1. Tiered Delayed Automatic Deleveraging Mechanism: Replace immediate deleveraging with a progressive warning system that maintains the system's solvency while preserving traders' autonomy.
60-second countdown: Send current queue position warnings to high-risk positions.
30-second countdown: Provide an option for voluntary deleveraging at market price.
Zero hour: Only execute forced deleveraging on positions that have not voluntarily deleveraged.
2. Market Maker Obligation Mechanism: The voluntary liquidity provision model has been declared a failure. Future market structures need to establish:
Binding obligations to maintain minimum quotes during periods of stress.
Enhanced rebates and privileges linked to performance during crises.
Automatic deleveraging protection for market makers who maintain liquidity during crises.
A penalty mechanism for those who abandon their posts during system stress events.
3. Dynamic Insurance Fund Requirements: Static insurance fund ratios have proven insufficient. They should be dynamically adjusted based on the following factors:
Position concentration indicators (an 87% long preference is already a clear warning).
Cross-margin risk exposure amplification factors.
Encapsulated asset correlation risks.
Real-time market maker participation levels.
4. Portfolio-Aware ADL: The current automatic deleveraging mechanism ignores position relationships and indiscriminately destroys hedging strategies. Improved designs should:
Identify hedging relationships before deleveraging.
Provide portfolio-level deleveraging options to maintain hedging ratios.
Allow traders to specify protected hedging portfolios.
Implement intelligent deleveraging to minimize overall portfolio impact.
5. Hybrid Transparency Model: Combine the efficiency of CEX with the accountability of DEX:
Real-time disclosure of the total amount of the insurance fund.
Display the statistical distribution of the automatic deleveraging queue.
Provide on-chain verification of automatic deleveraging events after execution.
Allow traders to purchase automatic deleveraging insurance or priority protection.
ADL: A Mirror of Market Maturity
The crash in October indicates that automatic deleveraging is not black and white, but rather a reflection of the structural flaws in the cryptocurrency market. When market makers flee, infrastructure collapses, and 87% of positions lean in the same direction, automatic deleveraging becomes the only mechanism to prevent a complete system failure.
Automatic deleveraging helped most short positions by forcing optimal exit timing, and this surprising finding suggests that the mechanism itself is not flawed—the problem lies within the market system that relies on it. In an effective market with reliable liquidity providers, sound infrastructure, and balanced position distribution, automatic deleveraging should rarely be triggered.
The direction forward is not to eliminate automatic deleveraging, but to build a market system that no longer urgently depends on it. This requires fundamental changes:
Market makers must not abandon their posts during crises.
Infrastructure should be able to expand with pressure, not just trading volume.
Risk management must prevent extreme position imbalances.
Transparency that builds trust even during forced actions.
Until these reforms are realized, automatic deleveraging will remain a lesser of two evils, a "necessary evil"—this mechanism, which contradicts the principles of a free market, is essential for maintaining market existence. The "10.11" crash indicates that, in the face of a liquidation scale of $19-40 billion, the choice is not between automatic deleveraging and fairness, but between automatic deleveraging and total collapse.
Perhaps the real insight lies in the fact that in a leveraged market connected through shared margin pools, pure individual freedom is merely an illusion. When someone loses more than their principal, others inevitably have to bear the losses. Automatic deleveraging merely determines who that bearer is. The data from October suggests that we are slowly learning this lesson. However, based on historical experience, when greed once again overwhelms fear, whether we can remember this lesson remains questionable.
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