The recent rollercoaster ride of speculative cryptocurrencies like $MYX, $AIA, and $COAI has not only caused massive losses for countless traders but has also drawn criticism regarding the clearing mechanisms and risk control capabilities of exchanges. The drastic "pump and dump" tactics reveal a contradiction: exchanges rely on volatility to earn transaction fees, but uncontrolled volatility can deplete insurance funds and even undermine market confidence. This article attempts to speculate on the balance between maintaining market activity and system solvency from the "first-person perspective" of exchange risk control. We will analyze how exchanges use tiered monitoring systems and advanced quantitative algorithms, such as Open Interest Concentration Ratio (OICR) and Order Flow Toxicity Index (OTSI), to proactively isolate manipulative behavior. Secondly, for professional traders hoping to survive and profit within this framework, some self-avoidance guidelines are provided: how to monitor their ADL priority and individual position ratio in real time to avoid being flagged by the risk control system as a "potential liquidation victim" or "market manipulator" posing a threat to the exchange. In the wild west of the crypto derivatives market, the secret to survival lies in understanding the rules—and that inviolable bottom line. Note: This article only speculates on the exchange's algorithm from an external perspective. It contains no internal information, is for reference only, and is intended for academic exchange. We assume no responsibility for its content. Part 1: The Core Strategic Needs of Exchanges: Balancing Volatility and Solvency As a financial infrastructure that provides trading venues and clearing services, the core objective of an exchange is to seek a dynamic balance: while not strictly limiting market volatility to maximize fee income, it must never allow such volatility to threaten its solvency and market reputation. 1.1 The "Dual Constraints" of Exchanges and Commercial Demands 1.1.1 Maximizing fee income versus allowing volatility: Increased trading volume directly drives transaction fee revenue. Dramatic price fluctuations, even those caused by pump and dump (P&D), attract a large number of speculators, thus boosting trading volume. Therefore, exchanges do not reject all volatility; in fact, they need a certain level of speculative activity to maintain market activity. 1.1.2 Avoid personal losses and systemic risks: The exchange's Insurance Fund serves as a safety net for perpetual contract trading. This fund absorbs losses incurred due to margin calls resulting from high-leverage trading (i.e., liquidation prices falling below zero or below the counterparty's bankruptcy price). Once these losses deplete the Insurance Fund, the exchange is forced to activate the Auto Deleveraging (ADL) mechanism. The ADL mechanism, due to its characteristics of penalizing profitable traders and closing hedging positions, represents a reluctantly "democratized" profit-and-loss equilibrium model. It's worth noting that frequent activation of the ADL, besides impacting the exchange's reputation, also signifies that the Insurance Fund has been depleted, as it acts as a last line of defense. 1.1.3 Public opinion pressure and market integrity: P&D incidents, especially sharp crashes in illiquid assets, can cause severe losses for users, triggering immense public pressure and damaging the exchange's brand reputation. Therefore, exchanges need to proactively isolate manipulative activities that could lead to systemic failure, even if they allow a certain degree of speculative volatility. in conclusion The bottom line for exchanges is to allow the market to operate freely without incurring losses themselves. The goal of risk control systems is not to eliminate all P&D, but to proactively identify and intervene before P&D evolves into a systemic crisis that depletes insurance funds. Once risk control is triggered, the consequences range from minor inquiries and order restrictions to serious account bans, frozen funds, and even legal intervention. 1.2 Risk Classification and Monitoring Weights Following the model of traditional exchanges, we speculate that exchanges should also adopt a tiered governance model to ensure that risk control measures are commensurate with the inherent vulnerabilities of contracts. This involves managing contracts through risk tiers and prioritizing monitoring resources on "high-risk contracts" (Tier 1), as manipulators can exert disproportionate influence on prices with relatively little capital in these contracts. Hierarchical logic and monitoring weight allocation: (Example) Risk control logic: Contracts with higher risk levels (such as MYX, AIA, COAI, etc.) are more likely to be attacked by P&D strategies, and once liquidation occurs, the probability that the losses from margin calls will be absorbed by insurance funds due to lack of liquidity is also higher. Therefore, exchanges generally adopt a "high-pressure" monitoring mode for Tier 1 contracts, mitigating leverage risk by increasing margin requirements, reducing leverage, and reducing the position size of individual accounts, and using high-frequency algorithms and indicators (such as OTSI) to quickly identify manipulation, thereby triggering intervention during the risk accumulation period. II. Exchange Monitoring Indicators and Quantitative Algorithms (Risk Control Systems) To proactively intervene in and curb manipulation, exchanges deploy multi-layered, high-dimensional algorithms in their risk control systems to monitor market behavior. This article will explore this from three fundamental perspectives: position concentration (P&D accumulation phase), basis anomalies (structural pressure), and order flow toxicity (high-frequency manipulation). 2.1 Algorithm Metric 1: Position Concentration and Accumulation Detection (OICR) The core concern of exchanges is that "a single entity has disproportionate control over the market." Therefore, monitoring the concentration of open interest is crucial. Metric: Open Interest Concentration Ratio (OICR) OICR measures the proportion of the total open interest of the top trading entities (e.g., the top 5 or top 10 accounts) to the total open interest of the contract. Quantitative Alert Example (Tier 1 Contract): Scenario: A Tier 1 contract has a total open interest (OI) of 1 million contracts. After identifying related accounts, it was found that the top three accounts quietly accumulated 750,000 contracts in the past 24 hours. Calculation and Alerts: OICR = 75%. If the exchange sets an internal alert threshold of OICR > 60% for this contract, the system will immediately trigger a "Concentrated Accumulation" alert. This signifies the end of the P&D accumulation phase and the imminent start of potential price manipulation. It is worth noting that even diversified account holdings can be easily flagged by similar trading methods, sources of funds, and other similarities. 2.2 Algorithm Metric 2: Order Flow Toxicity Detection (OTSI) Spoofing is one of the core tactics in the execution phase of P&D (Pre-Drop) trading, which involves submitting large orders but intending to cancel them before execution, creating false liquidity and demand. Exchange systems identify this "toxicity" by analyzing the efficiency of order flow. Metric: Order to Trade Ratio (OTR) OTR measures the ratio of the total number of submitted and cancelled orders to the number of trades actually executed. An excessively high OTR is one of the key indicators of fraud. OTR = Total Order Submissions and Cancellations / Total Executed Trades It is worth noting that spoofing is often accompanied by a large number of wash trades, creating a trend of increased price and trading volume. Quantitative alert example (high-frequency account): Scenario: During a highly volatile period, a high-frequency trading account submitted and canceled 400,000 orders within one minute, but only executed 80 trades. Calculation and Alerts: OTR = 400,000 / 80 = 5,000. If the average OTR of the legitimate market makers for this contract is below 500, the system will trigger a "Toxic Order Flow" alert for that account because its OTR is far above the average. This may cause the system to immediately impose flow restrictions on the order submission rate of that account. (Data is for illustrative purposes only and should not be taken literally.) 2.3 Algorithm Indicator 3: Spot-Futures Basis Difference Anomaly Detector (SFBAD) Exchanges need to prevent extreme price misalignments from triggering large-scale liquidations. The basis (Futures Price - Spot Price) reflects market sentiment and arbitrage efficiency. Metric: Standardized Basis Bias (SBD) Calculate how many standard deviations the current basis deviates from its long-term (e.g., 30-day rolling) average. Example of a quantitative alert: Scenario: The average basis (premium) between the futures and spot prices of a certain Tier 1 contract is +0.2%. However, during a market pump, due to concentrated buying by manipulators in the futures market, the basis instantly surged to +6.0% (an extremely high premium). Calculation and Alerts: If the 6% basis is statistically equivalent to a deviation of 5 standard deviations from the mean (SBD > 3.0), and this deviation persists for 15 minutes, the system will issue a "Structural Stress" alert. This indicates that price misalignment (usually speculative or manipulation-driven) could lead to massive liquidations and foreshadows the risk of a market crash. (Data is for illustrative purposes only and should not be taken literally.) III. Project Operators' Self-Avoidance Strategies: Quantitative Indicators and Survival Methods For professional traders or project owners, the most important thing is to avoid being flagged by the exchange's risk control system as a threat to the system's solvency and market integrity. This requires traders to master a set of "anti-risk control" self-monitoring indicators. Below are some common indicators explained. 3.1 Core Risk 1: Systemic Solvency Risk (Insurance Fund and ADL) The exchange's insurance fund serves as a buffer to cover losses from margin calls. Traders must consider the health of the insurance fund as a systemic risk affecting the safety of their own trading. Quantitative risk avoidance strategies for traders: 3.1.1 Monitoring ADL Priority: This is the most direct risk indicator for traders. Exchanges usually provide a real-time level for this indicator (e.g., level 5). The higher the level, the greater the risk of positions being forcibly liquidated when ADL is triggered. From the perspective of whoever profits is the most likely suspect, this situation should be avoided. ADL Priority = Profit Percentage / Effective Leverage Avoidance strategy: Traders must proactively close out some positions when the ADL level reaches a high point (e.g., 4/5 or 5/5). This will reduce the "profit percentage," thereby lowering their ADL priority to a safe zone (e.g., 2/5). 3.1.2 Monitor Insurance Fund Dynamics: Monitor the balance of the insurance fund for this trading pair and exchange announcements for similar trading pairs to determine policy direction. Traders should consider this a macroeconomic indicator of systemic pressure. Any sharp decline in the fund balance should be seen as a systemic risk warning, indicating that ADL risk is increasing. 3.1.3 Avoid High Leverage: Exchanges have higher margin and risk control requirements for low-liquidity contracts (Tier 1). Traders should increase margin to dilute effective leverage to reduce the risk of being targeted by the system during periods of high market volatility. 3.2 Core Risk 2: Centralized Control and Manipulation Risk (IOIR) Traders must avoid allowing the positions of any single or linked account to have a dominant influence on the contract, especially in low-liquidity contracts. Quantitative risk avoidance strategies for traders: Self-calculated IOIR: Individual open interest ratio IOIR = Your Position Size / Total Contract Open Interest (OI) Mitigation Objective: In high-risk (Tier 1) contracts, strive to keep the account's IOIR below n% to avoid triggering the exchange's internal "large trader report/concentration alert." If the capital is large, positions should be diversified to avoid rapid and concentrated accumulation of OI within a short period. 3.3 Core Risk 3: Order Flow Toxicity (OTR) Traders must ensure that their algorithms and trading patterns are consistent with the behavior of legitimate market makers, rather than with the characteristics of deceptive manipulation. Quantitative risk avoidance strategies for traders: Monitoring OTR: Continuously monitor your account's OTR. While legitimate market makers (providing liquidity) may have higher OTRs, their order submission and cancellation patterns are often balanced and bidirectional. Evasion Mode: The following modes marked as manipulation are strictly prohibited: One-sided spike: OTR shows a one-sided, extreme spike, such as when a large number of orders are submitted on the buy side, but the buy orders are immediately cancelled after the sell side has traded. Liquidity vacuum: Avoid operations that cause the order book depth to collapse rapidly within seconds (depth collapse exceeding 70%). This will be flagged by the system as creating a "liquidity vacuum," a strong signal of manipulation. It should be noted that the above indicators are just some routine quantitative indicators. If you have not yet established the above self-monitoring, please think twice. I forgot where I heard the joke: Since we're engaging in something as blatant as snatching food from a tiger's mouth, we should be prepared to return it intact. ???????????? Finally, I suggest you take a look, but I don't recommend you do anything about it. May we always maintain a sense of awe and respect for the market.The recent rollercoaster ride of speculative cryptocurrencies like $MYX, $AIA, and $COAI has not only caused massive losses for countless traders but has also drawn criticism regarding the clearing mechanisms and risk control capabilities of exchanges. The drastic "pump and dump" tactics reveal a contradiction: exchanges rely on volatility to earn transaction fees, but uncontrolled volatility can deplete insurance funds and even undermine market confidence. This article attempts to speculate on the balance between maintaining market activity and system solvency from the "first-person perspective" of exchange risk control. We will analyze how exchanges use tiered monitoring systems and advanced quantitative algorithms, such as Open Interest Concentration Ratio (OICR) and Order Flow Toxicity Index (OTSI), to proactively isolate manipulative behavior. Secondly, for professional traders hoping to survive and profit within this framework, some self-avoidance guidelines are provided: how to monitor their ADL priority and individual position ratio in real time to avoid being flagged by the risk control system as a "potential liquidation victim" or "market manipulator" posing a threat to the exchange. In the wild west of the crypto derivatives market, the secret to survival lies in understanding the rules—and that inviolable bottom line. Note: This article only speculates on the exchange's algorithm from an external perspective. It contains no internal information, is for reference only, and is intended for academic exchange. We assume no responsibility for its content. Part 1: The Core Strategic Needs of Exchanges: Balancing Volatility and Solvency As a financial infrastructure that provides trading venues and clearing services, the core objective of an exchange is to seek a dynamic balance: while not strictly limiting market volatility to maximize fee income, it must never allow such volatility to threaten its solvency and market reputation. 1.1 The "Dual Constraints" of Exchanges and Commercial Demands 1.1.1 Maximizing fee income versus allowing volatility: Increased trading volume directly drives transaction fee revenue. Dramatic price fluctuations, even those caused by pump and dump (P&D), attract a large number of speculators, thus boosting trading volume. Therefore, exchanges do not reject all volatility; in fact, they need a certain level of speculative activity to maintain market activity. 1.1.2 Avoid personal losses and systemic risks: The exchange's Insurance Fund serves as a safety net for perpetual contract trading. This fund absorbs losses incurred due to margin calls resulting from high-leverage trading (i.e., liquidation prices falling below zero or below the counterparty's bankruptcy price). Once these losses deplete the Insurance Fund, the exchange is forced to activate the Auto Deleveraging (ADL) mechanism. The ADL mechanism, due to its characteristics of penalizing profitable traders and closing hedging positions, represents a reluctantly "democratized" profit-and-loss equilibrium model. It's worth noting that frequent activation of the ADL, besides impacting the exchange's reputation, also signifies that the Insurance Fund has been depleted, as it acts as a last line of defense. 1.1.3 Public opinion pressure and market integrity: P&D incidents, especially sharp crashes in illiquid assets, can cause severe losses for users, triggering immense public pressure and damaging the exchange's brand reputation. Therefore, exchanges need to proactively isolate manipulative activities that could lead to systemic failure, even if they allow a certain degree of speculative volatility. in conclusion The bottom line for exchanges is to allow the market to operate freely without incurring losses themselves. The goal of risk control systems is not to eliminate all P&D, but to proactively identify and intervene before P&D evolves into a systemic crisis that depletes insurance funds. Once risk control is triggered, the consequences range from minor inquiries and order restrictions to serious account bans, frozen funds, and even legal intervention. 1.2 Risk Classification and Monitoring Weights Following the model of traditional exchanges, we speculate that exchanges should also adopt a tiered governance model to ensure that risk control measures are commensurate with the inherent vulnerabilities of contracts. This involves managing contracts through risk tiers and prioritizing monitoring resources on "high-risk contracts" (Tier 1), as manipulators can exert disproportionate influence on prices with relatively little capital in these contracts. Hierarchical logic and monitoring weight allocation: (Example) Risk control logic: Contracts with higher risk levels (such as MYX, AIA, COAI, etc.) are more likely to be attacked by P&D strategies, and once liquidation occurs, the probability that the losses from margin calls will be absorbed by insurance funds due to lack of liquidity is also higher. Therefore, exchanges generally adopt a "high-pressure" monitoring mode for Tier 1 contracts, mitigating leverage risk by increasing margin requirements, reducing leverage, and reducing the position size of individual accounts, and using high-frequency algorithms and indicators (such as OTSI) to quickly identify manipulation, thereby triggering intervention during the risk accumulation period. II. Exchange Monitoring Indicators and Quantitative Algorithms (Risk Control Systems) To proactively intervene in and curb manipulation, exchanges deploy multi-layered, high-dimensional algorithms in their risk control systems to monitor market behavior. This article will explore this from three fundamental perspectives: position concentration (P&D accumulation phase), basis anomalies (structural pressure), and order flow toxicity (high-frequency manipulation). 2.1 Algorithm Metric 1: Position Concentration and Accumulation Detection (OICR) The core concern of exchanges is that "a single entity has disproportionate control over the market." Therefore, monitoring the concentration of open interest is crucial. Metric: Open Interest Concentration Ratio (OICR) OICR measures the proportion of the total open interest of the top trading entities (e.g., the top 5 or top 10 accounts) to the total open interest of the contract. Quantitative Alert Example (Tier 1 Contract): Scenario: A Tier 1 contract has a total open interest (OI) of 1 million contracts. After identifying related accounts, it was found that the top three accounts quietly accumulated 750,000 contracts in the past 24 hours. Calculation and Alerts: OICR = 75%. If the exchange sets an internal alert threshold of OICR > 60% for this contract, the system will immediately trigger a "Concentrated Accumulation" alert. This signifies the end of the P&D accumulation phase and the imminent start of potential price manipulation. It is worth noting that even diversified account holdings can be easily flagged by similar trading methods, sources of funds, and other similarities. 2.2 Algorithm Metric 2: Order Flow Toxicity Detection (OTSI) Spoofing is one of the core tactics in the execution phase of P&D (Pre-Drop) trading, which involves submitting large orders but intending to cancel them before execution, creating false liquidity and demand. Exchange systems identify this "toxicity" by analyzing the efficiency of order flow. Metric: Order to Trade Ratio (OTR) OTR measures the ratio of the total number of submitted and cancelled orders to the number of trades actually executed. An excessively high OTR is one of the key indicators of fraud. OTR = Total Order Submissions and Cancellations / Total Executed Trades It is worth noting that spoofing is often accompanied by a large number of wash trades, creating a trend of increased price and trading volume. Quantitative alert example (high-frequency account): Scenario: During a highly volatile period, a high-frequency trading account submitted and canceled 400,000 orders within one minute, but only executed 80 trades. Calculation and Alerts: OTR = 400,000 / 80 = 5,000. If the average OTR of the legitimate market makers for this contract is below 500, the system will trigger a "Toxic Order Flow" alert for that account because its OTR is far above the average. This may cause the system to immediately impose flow restrictions on the order submission rate of that account. (Data is for illustrative purposes only and should not be taken literally.) 2.3 Algorithm Indicator 3: Spot-Futures Basis Difference Anomaly Detector (SFBAD) Exchanges need to prevent extreme price misalignments from triggering large-scale liquidations. The basis (Futures Price - Spot Price) reflects market sentiment and arbitrage efficiency. Metric: Standardized Basis Bias (SBD) Calculate how many standard deviations the current basis deviates from its long-term (e.g., 30-day rolling) average. Example of a quantitative alert: Scenario: The average basis (premium) between the futures and spot prices of a certain Tier 1 contract is +0.2%. However, during a market pump, due to concentrated buying by manipulators in the futures market, the basis instantly surged to +6.0% (an extremely high premium). Calculation and Alerts: If the 6% basis is statistically equivalent to a deviation of 5 standard deviations from the mean (SBD > 3.0), and this deviation persists for 15 minutes, the system will issue a "Structural Stress" alert. This indicates that price misalignment (usually speculative or manipulation-driven) could lead to massive liquidations and foreshadows the risk of a market crash. (Data is for illustrative purposes only and should not be taken literally.) III. Project Operators' Self-Avoidance Strategies: Quantitative Indicators and Survival Methods For professional traders or project owners, the most important thing is to avoid being flagged by the exchange's risk control system as a threat to the system's solvency and market integrity. This requires traders to master a set of "anti-risk control" self-monitoring indicators. Below are some common indicators explained. 3.1 Core Risk 1: Systemic Solvency Risk (Insurance Fund and ADL) The exchange's insurance fund serves as a buffer to cover losses from margin calls. Traders must consider the health of the insurance fund as a systemic risk affecting the safety of their own trading. Quantitative risk avoidance strategies for traders: 3.1.1 Monitoring ADL Priority: This is the most direct risk indicator for traders. Exchanges usually provide a real-time level for this indicator (e.g., level 5). The higher the level, the greater the risk of positions being forcibly liquidated when ADL is triggered. From the perspective of whoever profits is the most likely suspect, this situation should be avoided. ADL Priority = Profit Percentage / Effective Leverage Avoidance strategy: Traders must proactively close out some positions when the ADL level reaches a high point (e.g., 4/5 or 5/5). This will reduce the "profit percentage," thereby lowering their ADL priority to a safe zone (e.g., 2/5). 3.1.2 Monitor Insurance Fund Dynamics: Monitor the balance of the insurance fund for this trading pair and exchange announcements for similar trading pairs to determine policy direction. Traders should consider this a macroeconomic indicator of systemic pressure. Any sharp decline in the fund balance should be seen as a systemic risk warning, indicating that ADL risk is increasing. 3.1.3 Avoid High Leverage: Exchanges have higher margin and risk control requirements for low-liquidity contracts (Tier 1). Traders should increase margin to dilute effective leverage to reduce the risk of being targeted by the system during periods of high market volatility. 3.2 Core Risk 2: Centralized Control and Manipulation Risk (IOIR) Traders must avoid allowing the positions of any single or linked account to have a dominant influence on the contract, especially in low-liquidity contracts. Quantitative risk avoidance strategies for traders: Self-calculated IOIR: Individual open interest ratio IOIR = Your Position Size / Total Contract Open Interest (OI) Mitigation Objective: In high-risk (Tier 1) contracts, strive to keep the account's IOIR below n% to avoid triggering the exchange's internal "large trader report/concentration alert." If the capital is large, positions should be diversified to avoid rapid and concentrated accumulation of OI within a short period. 3.3 Core Risk 3: Order Flow Toxicity (OTR) Traders must ensure that their algorithms and trading patterns are consistent with the behavior of legitimate market makers, rather than with the characteristics of deceptive manipulation. Quantitative risk avoidance strategies for traders: Monitoring OTR: Continuously monitor your account's OTR. While legitimate market makers (providing liquidity) may have higher OTRs, their order submission and cancellation patterns are often balanced and bidirectional. Evasion Mode: The following modes marked as manipulation are strictly prohibited: One-sided spike: OTR shows a one-sided, extreme spike, such as when a large number of orders are submitted on the buy side, but the buy orders are immediately cancelled after the sell side has traded. Liquidity vacuum: Avoid operations that cause the order book depth to collapse rapidly within seconds (depth collapse exceeding 70%). This will be flagged by the system as creating a "liquidity vacuum," a strong signal of manipulation. It should be noted that the above indicators are just some routine quantitative indicators. If you have not yet established the above self-monitoring, please think twice. I forgot where I heard the joke: Since we're engaging in something as blatant as snatching food from a tiger's mouth, we should be prepared to return it intact. ???????????? Finally, I suggest you take a look, but I don't recommend you do anything about it. May we always maintain a sense of awe and respect for the market.