Original Title: Hyperliquid Risk Dashboard
Original Author: asxn_r, Crypto Researcher
Original Translation: Duoduo Xia Deep

Editor's Note: The article analyzes the event where the price surge of JELLY led to the liquidation of a $4 million USD short position, exposing platform risks due to HLP's backstop design flaw. It introduces measures such as the HLP liquidation treasury, dynamic open interest contract limit, and asset delisting to optimize risk management. It also launches a multi-table dashboard to monitor real-time open interest, liquidity, funding rates, and other metrics to help validators identify manipulation risks and make decisions on asset delisting.

The following is the original content (lightly rephrased for better comprehension):

On March 26, a trader opened a $4 million USD USDC short position through self-trading when the JELLY price was $0.0095. Subsequently, the JELLY price surged over 4 times, leading to the liquidation of this position. The platform's backstop, HLP, took over the loss-making position, resulting in a depreciation of its account value.

Despite the platform using a dynamic open interest contract limit formula (based on global liquidity and open interest volume from major exchanges), this $4 million USD short was still within the limit and initially allowed. Once the limit was reached, the system automatically prevented new open interest contracts.

The core issue arose post-liquidation: the HLP treasury holding the short position shared collateral with other strategic treasuries. This design choice meant that the auto-deleveraging (ADL) was not triggered, leaving the platform exposed to further loss risks as the JELLY price continued to rise.

To mitigate this risk, Hyperliquid reinforced its risk management through the following key measures:

· HLP Liquidation Treasury: Strictly limiting the treasury's proportion to the total HLP value, reducing the rebalancing frequency, and employing more advanced logic in backstop liquidation. ADL will only be triggered when the liquidation treasury's losses exceed a set threshold, rather than drawing collateral from other treasuries. Under normal market conditions, ADL is not expected to activate.

· Dynamic Open Interest Contract Limit: The open interest contract limit will dynamically adjust based on market value.

· Asset Delisting: Validators will vote on-chain to delist assets based on a predefined threshold.

Following the recent events, ASXN has created the Hyperliquid Risk Metrics Dashboard to monitor position risks in real-time and help validators build consensus on asset delisting based on multiple indicators.

Dashboard Structure

Table 1: Perpetual Market Overview

This table provides an overview of key metrics for the Perpetual Futures Market on Hyperliquid:

· Open Interest (OI): The total USD value of all outstanding perpetual futures positions.

· Market Cap: The total circulating market capitalization of the base token (price × circulating supply).

· OI/Market Cap Ratio: The open interest divided by the market cap, represented as a percentage. This ratio helps identify markets on Hyperliquid that may have a disproportionately large open interest relative to the token's circulating supply, posing a manipulation risk. For example, the position inherited by the HLP liquidation treasury exceeds 40% of the JELLY circulation.

· Max Leverage: The maximum leverage allowed for perpetual futures trading.

· 24-Hour Trading Volume: The total trading volume over the past 24 hours, reflecting market activity.

Table 2: Assets Approaching the Hyperliquid Open Interest Cap

This table displays assets that have reached or are close to the maximum allowed open interest, along with the market cap and OI/Market Cap ratio percentage for these assets.

Markets that have reached the open interest limit no longer allow opening positions and only support closing positions. From a risk perspective, monitoring assets reaching this limit is crucial as it provides real-time feedback on which assets may face manipulation risks or have already been manipulated.

The JELLY position quickly reached its limit, but as HLP is the main counterparty of malicious traders (through liquidation positions), HLP has entered into a settlement deadlock with the traders.

Table 3: Centralized Exchange Liquidity (±2% Order Book Depth)

Measures the cost of moving the price up or down by 2% on major centralized exchanges:

· +2% Depth: The buy order volume required to push the price up by 2%.

· -2% Depth: The sell order volume required to push the price down by 2%.

· Data aggregated from Binance (Spot and Perpetual), Bybit (Spot and Perpetual), Kucoin, and OKX, selecting the market with the deepest liquidity for ±2% readings.

· The liquidity on centralized exchange order books beyond the best bid/ask price allows us to assess the risk of asset manipulation. Assets with poor liquidity enable attackers to move prices with lower capital, making the attack cost-effective.

Table 4: Decentralized Exchange Liquidity

Tracking liquidity metrics on various chains:

· Total Reserve: Available liquidity in the DEX pool (in USD), including the USD value of base and quote assets.

· 24-Hour Trading Volume: Overall trading activity.

· Buy/Sell Trades: Number of buy/sell trade transactions.

· Traders: Unique addresses engaging in trades.

· Display DEX market depth and liquidity.

· As of today, the DEX pools we track cover cosmos, evmos, canto, kava, binance-smart-chain, ethereum, moonriver, harmony-shard-0, moonbeam, energi, polygon-pos, optimistic-ethereum, arbitrum-one, arbitrum-nova, sui, fantom, near-protocol, xdai, milkomeda-cardano, avalanche, base, aptos, polygon-zkevm, solana, ronin, manta-pacific, tomochain, sonic, the-open-network, linea, neon-evm, celo, zksync, opbnb, starknet, and mantle.

· The DEX liquidity table allows us to quickly view on-chain liquidity for tokens and their manipulation risk, as well as identify signs of hoarding by holders.

Table 5: Price Impact

By examining the order book depth beyond the best bid/ask price on Hyperliquid, we can understand the price impact. The price impact indicates the nominal impact scale of market orders (2k USD for ETH and BTC, 6k USD for other assets) expected to cause a price effect.

For example, the Impact Sell Price displays the price the asset would reach if a nominal impact market sell is performed.

· Nominal Impact Size: ETH and BTC at $20,000, all other assets at $6,000, based on the Hyperliquid documentation.

· Impact Price (Buy/Sell): The expected price after executing a market order.

· Impact Percentage: The percentage change from the current price. This metric gives us an overview of the liquidity of assets on Hyperliquid, helping identify easily manipulated assets.

· Note: HPOS and RLB are isolated mode only, with lower risk, HLP will not liquidate, unrealized PnL (uPnL) cannot be used for cross-margin.

Table 6: Funding Rate Comparison (Annualized)

This table compares the perpetual futures funding rates on Hyperliquid, Bybit, and Binance:

· Nominal Open Interest: The total USD value of all open perpetual futures positions on Hyperliquid.

· Funding Rate (HL, Binance, Bybit): The annualized percentage rate traders holding positions pay/receive.

· Exchange-HL Arbitrage: The difference between the exchange (Binance/Bybit) and Hyperliquid funding rates.

· Comparing the funding rate with major centralized exchanges allows us to identify potentially manipulated assets. When large positions enter the illiquid open interest, the funding rate may deviate from other exchanges.

Table 7: HLP Financial Metrics

These metrics analyze the financial performance of HLP and its three sub-treasuries (HLP-A, HLP-B, and Liquidation Treasury), deriving the performance of HLP and each sub-treasury's TVL.

We estimate HLP returns, but due to data granularity limitations, they may slightly differ from actual performance. Based on these estimated returns, we calculate the following metrics:

· Maximum Drawdown: The maximum drawdown faced by depositors due to HLP strategy losses.

· Sharpe Ratio: A measure used to evaluate the risk-adjusted return of an investment, reflecting the excess return earned for taking on additional volatility (risk). A 4% risk-free rate is used here.

· Sortino Ratio: Similar to the Sharpe Ratio, a risk-adjusted metric that only penalizes downside volatility (negative returns), not overall volatility.

Table 8: HLP Treasury Positions

· Display the real-time positions of each HLP treasury as part of the strategy.

· The HLP parent treasury appears to hold idle USDC, while the HLP-A and HLP-B child treasuries have active market-making positions.

· The Liquidation Treasury currently has no positions, but if needed for backing a liquidation, active positions will occur.

· Real-time monitoring of each treasury position is crucial to act swiftly when certain positions approach the platform's risk limits.

Table 9: HLP Treasury Time Series

Return of account value and cumulative P&L for each HLP treasury.

· Note: "Total HLP" refers to the sum of HLP-A, HLP-B, Liquidation Treasury, and the parent treasury.

· We also provide an estimated weekly return for the Total HLP.

Table 10: HLP Net Positions

In the cumulative net positions table, we aggregate all HLP child treasury positions, showing HLP's net exposure to specific positions/assets. It can be sorted by each treasury's maximum nominal position.

Table 11: HLP's Share of Total Open Interest

· Based on HLP's cumulative net position, calculate the percentage of HLP's position to Hyperliquid's total open interest.

· Assets with low demand and external market-maker quoting may have a lower likelihood, with HLP having a disproportionately high share in providing liquidity, increasing the risk of manipulation by malicious market participants. For example, HLP holds nearly 50% of the open interest in the JELLY market, whereas manipulators hold the other half.

· Tracking the proportion of HLP in each asset's total open interest can help us identify manipulable assets.

A public dashboard will enable us and Hyperliquid mainnet validators to utilize data-driven metrics to assess asset offboarding and platform real-time risk.

「Original Article Link」

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