A two-person trading team says it generated $10 million in profit over 10 months by arbitraging stock-linked perpetual contracts on Hyperliquid’s HIP-3 against prices in traditional markets, reporting $32 billion in combined turnover across the two venues. The account offers a detailed view of how stock-basis trading on decentralized perpetual platforms has evolved from a niche opportunity into a speed- and infrastructure-intensive strategy.
The team said it began operating shortly after Hyperliquid launched HIP-3 on Oct. 13, 2025, followed by trade.xyz’s introduction of its first stock perpetual market, XYZ100, three days later. It reported that its activity represented 1.5% of trade.xyz’s total volume during the period.
Rather than making directional bets on stocks, metals or oil, the traders sought to capture temporary differences between perpetual-contract prices and a reference price in the underlying traditional market. The approach required the team to trade the price gap quickly, hedge exposure after each fill, and continually manage the risk that the two sides of the trade could move before positions were balanced.
A volume-first strategy becomes a large arbitrage operation
The team said Hyperliquid’s token distribution influenced its initial approach. More than 40% of the token supply had yet to be distributed when it started, according to the traders, leading them to prioritize volume generation alongside trading returns.
That decision helped shape a strategy that depended on repeated, relatively small pricing discrepancies rather than a handful of high-conviction trades. In practice, the system watched the price of a stock-linked perpetual contract on HIP-3 and compared it with a traditional-market reference.
When a perpetual traded below that reference, the team said it would buy the contract on HIP-3 and sell the corresponding instrument in the traditional market. A premium would reverse the sequence: sell the perpetual, then buy the hedge. This is a form of delta arbitrage, designed to keep the overall position relatively insensitive to the asset’s direction while capturing convergence between the two prices.
The traders said they had no previous stock-trading experience before building the system. One person focused on understanding the trading interface and operational mechanics of the traditional-market side, while the other concentrated on API-based execution. They said the division of work eventually allowed new strategies to be deployed within roughly 48 hours.
Their early months show how rapidly activity grew. After testing in late October, the team reported about $850 million in HIP-3 volume during November and profit exceeding $500,000. It reported a further $550 million in volume in December, then $1.7 billion on Hyperliquid in January.
Metals created large funding and hedging demands
January brought a different source of returns as gold and silver trading became more active. The team reported earning more than $600,000 in funding fees that month, as traders on the perpetual side leaned heavily toward long commodity positions.
Funding is the recurring payment exchanged between long and short holders of perpetual contracts to keep the contract price close to its reference market. Heavy demand to hold long positions can push funding payments toward shorts, creating income for market-neutral traders able to hedge their exposure elsewhere.
That opportunity also increased the capital needed outside Hyperliquid. The team said each new short perpetual position required a corresponding hedge in the traditional market, while bank-transfer limits and settlement delays constrained how quickly capital could move between venues.
It responded by building a liquidity-sensitive execution system. When capital on the traditional side was scarce, the system would close positions even at a cost and require wider price spreads before opening new ones. When liquidity was available, it accepted narrower spreads and deployed funds more aggressively. The design reflects a practical constraint of cross-market arbitrage: a profitable price discrepancy can become unusable if the hedge cannot be placed or funded in time.
A $1.1 million error exposed the risk behind the strategy
The largest reported setback came on Jan. 27, when an API data-refresh failure caused the system’s risk checks to malfunction. According to the team, the error allowed it to accumulate an unintended net short position in gold futures with $120 million in notional exposure.
The traders said they manually closed the position over the following 15 to 30 minutes, eventually recording a $1.1 million loss. A notional figure describes the full face value of a derivatives position, rather than the cash posted as margin, but the episode shows how an arbitrage system can become a large directional trade when market data or risk controls fail.
Afterward, the team said it added checks designed to verify that market data was current before allowing positions to expand. It also introduced additional controls around position growth. The response was soon tested by a silver decline following an all-time high, when the team reported that the gap between the two markets briefly reached about 3%. It said the move produced roughly $600,000 in profit.
The January incident also helps explain why the group placed increasing emphasis on data speed. It initially used broker-provided price feeds, then moved toward a direct feed from Databento after applying for a Nasdaq data license in January and receiving approval by the end of that month. Faster and more reliable market data would reduce the time between identifying a discrepancy and placing the hedge, a critical variable as more participants target the same spreads.
Semiconductor stocks replaced commodities as volatility cooled
The team reported another $1.5 billion in trading volume in February, when metals remained highly active and late-month geopolitical developments pushed oil above $100. It said daily revenue from spreads and funding generally ranged from $60,000 to $120,000 on days when traditional markets were open.
Days producing around $40,000 were treated as warnings that required a review of execution quality and system parameters, according to the account. The group said it regularly analyzed trading data with Anthropic’s Claude to identify where execution was losing money, then adjusted code and trading settings.
By late April, it said volatility linked to tensions involving Iran had eased. Trading activity then shifted toward semiconductor-linked contracts, including SNDK and MU. The team reported monthly volume of $1.5 billion to $2.5 billion across May, June and July, with weekly profit of roughly $400,000 to $500,000.
By early September, the team said its annualized return on deployed capital ranged from about 35% to 45%, depending on the period. It also said institutions were moving into stock-basis strategies, citing Ethena’s announced plans to enter the segment.
The team’s results, if sustained, point to a market where the edge increasingly rests on hedging capacity, data quality and risk controls rather than simply identifying a visible price gap. Its $120 million gold error shows the same infrastructure that can process billions in turnover can also magnify a stale-data failure within minutes.
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