Introduction
The next structural opportunity in crypto may not emerge from another new token narrative, but from a fundamental shift in what crypto’s trading infrastructure is built to carry. As on-chain tools mature while high-quality crypto-native assets remain relatively scarce, market attention is turning toward assets with genuine fundamentals and event density — and U.S. equities, particularly AI-related names, are emerging as the most direct beneficiary of that pivot.
Concurrently, the rise of on-chain U.S. equity perpetuals, the growing disruption that Pre-IPO Perps pose to private secondary market pricing, and the gap between on-chain expectations and traditional underwriter guidance around the Cerebras listing all point to the same observation: U.S. equity price discovery is developing a parallel track, populated by crypto users and supported by on-chain liquidity.
This report by HTX Research addresses three critical dimensions of this transformation:
- The true Product-Market Fit (PMF) driving on-chain U.S. equity perpetuals.
- Reframing the investment logic of AI equities through a crypto-native lens.
- The evolving role of crypto exchanges as gateways within this paradigm shift.
1. Core Thesis: Smart Money Is Moving from “Trading Crypto” to “Trading Global Assets”
Over the past decade, the most profound innovations in crypto was not any single token, but the creation of a highly globalized, always-on, low-barrier, high-leverage, and composable trading infrastructure. Perpetual futures, USDT/USDC margin, on-chain wallets, CLOB and AMM liquidity pools, automated liquidations, funding rates, points incentives, and airdrop expectations have collectively pushed the barrier to financial market participation to a historical low.
However, the crypto market is also entering an awkward stage: infrastructure is becoming increasingly mature, trading tools are becoming increasingly powerful, yet the number of high-quality tradable assets is becoming increasingly limited. Many altcoins, meme coins, and narrative tokens are still essentially attention assets rather than cash-flow assets. They can generate spectacular short-term wealth effects, but they are difficult to use as long-term vehicles for large-scale capital.
By contrast, U.S. equities—specifically AI-driven tech names—remain the definitive epicenter of global capital. In Q1 2026, SIFMA data indicated that the total market capitalization of U.S.-listed companies hovered around $66 trillion, eclipsing the digital asset market by orders of magnitude.
Key Structural Shift: If the previous crypto cycles were defined by trading BTC, ETH, SOL, and altcoins, the next structural phase of crypto trading infrastructure will likely absorb U.S. equities, pre-IPO equity derivatives, tokenized Real-World Assets (RWAs), and novel financial contracts built directly on AI infrastructure.
The long-term opportunity for the crypto industry is expanding beyond the mere issuance of native tokens toward repackaging the world’s most liquid, narrative-driven, and revenue-generating legacy assets into on-chain formats that are globally accessible, leveragable, and fully composable.
1.1 Crypto’s Structural Bottleneck: High-Efficiency Infrastructure, Low-Quality Trading Assets
Over the past few years, crypto infrastructure has improved rapidly. Centralized exchanges offer extremely high-performance matching engines, while on-chain perp DEXs such as Hyperliquid, Lighter, Paradex, Aevo, Drift, and Ostium have continuously improved the trading experience.
The problem is that as infrastructure becomes stronger, the shortage of high-quality trading assets becomes more obvious. After volatility in BTC and ETH declined as core assets, the market kept searching for new beta. Solana memes, AI agents, RWA, DePIN, restaking, BTC L2s, Monad, Berachain, and the Base ecosystem have all become temporary narratives. But most crypto narratives have short life cycles and are easily consumed by token emissions, points fatigue, low-float high-FDV structures, and market-making mechanics.
As a result, a structural contradiction has emerged: traders need volatility, but much of the volatility provided by projects does not come from real fundamentals. Instead, it comes from liquidity design, KOL distribution, and unlock schedules. The market is becoming increasingly tired of assets that exist purely “for trading,” and is turning toward assets with real revenue, real events, and real fundamental elasticity.
U.S. equities fill this gap perfectly. U.S. stocks, especially AI stocks, not only have fundamentals, but also have high event density. Earnings, orders, supply chains, CapEx, export controls, model releases, data center construction, power agreements, M&A rumors, and pre-IPO roadshows can all be converted into on-chain trading opportunities.
Therefore, the core value of on-chain U.S. equity AI products is not simply to copy Robinhood or Interactive Brokers. It is to repackage U.S. equities into products that are more suitable for crypto users: always-on, USDC-denominated, high-leverage, shortable, composable, DeFi-accessible, incentivized by points systems, and distributable through KOLs and communities.
2. The PMF of On-Chain U.S. Equity Perpetuals: From RWA Narrative to Real Trading Demand
Real-World Assets (RWAs) were initially popularized as the simple tokenization of static yield, such as on-chain Treasuries or real estate. However, from a trading architecture perspective, the true paradigm shift lies in secondary-market liquidity.
What truly changes user behavior is not telling users that there is a token representing Treasury yield on-chain. It is letting users realize that they can use USDC at 3 a.m. to go long Nvidia, short Tesla, trade OpenAI listing expectations, bet on the Cerebras IPO, hedge an AI bubble, or trade volatility around U.S. stock earnings.
According to RWA.xyz, the total value of tokenized stocks is approximately $1.08 billion, with monthly transfer volume of around $2.3 billion and roughly 190,000 holders. This shows that tokenized stocks are no longer just a concept, but have already formed an early on-chain circulation market.
But tokenized stocks are only the first step. Spot tokenized stocks solve the problem of “holding,” while perpetual futures solve the problem of “trading.” For crypto users, trading demand is much larger than holding demand. Most users do not truly want to hold a stock for the long term. They want to express a view during a specific event window.
For example, a user may not want to become an Nvidia shareholder, but they may want to trade NVDA volatility around earnings. They may not want to own Cerebras equity, but they may want to bet on its IPO opening price. They may not want to buy private OpenAI shares, but they may want to trade OpenAI valuation expectations. They may not want to open an overseas brokerage account, but they may want to use USDC to trade global technology stocks on-chain.
This is the PMF of U.S. equity perpetuals: they are not replacing traditional stock accounts. They are serving a group of users that traditional finance does not serve well. These users are used to 24-hour trading, leverage, USDT/USDC denomination, wallet login, points and airdrops, and real-time trading signals from X, Telegram, Discord, Binance Square, and Chinese communities.
2.1 TradeXYZ, Ostium, and Lighter: Three Different Models for On-Chain U.S. Equity Trading
At present, on-chain U.S. equity perpetuals can roughly be divided into three routes.
The first route is represented by TradeXYZ and the Hyperliquid HIP-3 model. Its core advantage is inheriting Hyperliquid’s trading habits, order book depth, user mindshare, and asset issuance capabilities. TradeXYZ’s pre-IPO perp product is especially important because it does not only trade already-listed stocks. It attempts to establish a public, continuous, two-sided, and leveragable price discovery market before IPO.
The second route is represented by Ostium, a professional RWA trading venue. Built on Arbitrum, Ostium uses a peer-to-pool or pool-to-pool structure and focuses on real-world assets, including U.S. stocks, indices, commodities, and FX. Its features include broader asset coverage, more aggressive leverage, and oracle and rollover fee mechanisms that are closer to traditional financial cost structures. According to Ostium’s official documentation, its fees include opening fees, oracle fees, rollover fees, and liquidation fees. Opening fees are around 3–5 bps, oracle fees are 0.10 USDC, rollover fees vary based on the underlying carry cost, and there are no closing fees.
The third route is represented by Lighter’s high-performance ZK-rollup CLOB model. Lighter emphasizes zero trading fees, provable fairness, and a high-performance order book experience. In November 2025, Lighter completed a $68 million financing round at a valuation of approximately $1.5 billion, with investors including Founders Fund, Ribbit Capital, Haun Ventures, and Robinhood. Later, Lighter also launched 24/5 equity perps, showing that it is expanding from a pure crypto perpetual trading platform into the perpetual market for traditional assets.
The difference between these three routes is not just technical, but also reflects different market philosophies.
TradeXYZ is more like a crypto-native asset issuance and price discovery layer. Ostium is more like a professional leveraged RWA trading market. Lighter is more like high-performance on-chain exchange infrastructure. Together, they point to the same trend: U.S. equities are no longer just assets inside brokerage accounts. They are becoming fundamental trading pairs inside crypto trading systems.
2.2 Why Are On-Chain U.S. Equity Perpetuals More Suitable for Global Crypto Users Than Traditional Brokerages?
Traditional brokerages have advantages in compliance, custody, and real equity ownership. But their trading experience is not friendly to crypto users.
First, onboarding is difficult. Cross-border users often need to submit identity documents, tax forms, proof of address, bank account information, and other materials. Second, trading hours are restricted. Even with pre-market and after-hours trading, depth and available tools are limited. Third, leverage is restricted. Ordinary users find it difficult to access high leverage, and margin rules are complex. Fourth, deposits and withdrawals are inefficient. Fiat deposits, cross-border wires, settlement cycles, and withdrawal reviews do not match crypto users’ preference for instant trading.
The advantages of on-chain U.S. equity perpetuals are the exact opposite. Users can trade by connecting a wallet, use USDC/USDT as margin, settle almost globally in real time, trade 24 hours or 24/5, go long or short, use high leverage, and connect trading with DeFi, lending, points, airdrops, strategy bots, and social trading.
This is why “U.S. equity perpetuals” are not a niche product. They may become the next-generation traffic entry point contested by both CEXs and DEXs. CEXs have users and fiat channels. DEXs have transparency, composability, and airdrop incentives. Both are looking for larger trading assets, and U.S. equities, especially AI stocks, are currently the most suitable answer.
3. The Cerebras Event: Pre-IPO Perps’ First Real Demonstration of Pricing Power
The recent listing of Cerebras Systems served as a landmark validation for on-chain pre-IPO perpetuals. While Cerebras priced its traditional IPO at $185 before opening at $350 on the Nasdaq, TradeXYZ’s pre-IPO perp contract had actively indexed this demand well ahead of public order-book opening.
On-chain pricing surged from the $290 range to $380, with hourly volumes approaching $100 million, a 24-hour aggregate volume of $280 million, and open interest scaling to $57.77 million. This made it the fourth most liquid asset contract on the venue.
The significance of this event is not simply that “TradeXYZ guessed the Cerebras price correctly.” More importantly, it proved that on-chain markets can form a new price discovery mechanism outside the traditional IPO pricing system.
Traditional IPO pricing relies on investment banks, roadshows, bookbuilding, and institutional allocation. Information flows among underwriters, institutional investors, and company management. Ordinary investors usually can only passively accept the result after the stock opens for trading. Investment banks control the full order book, and therefore control pricing power.
Pre-IPO perps break this structure. They allow global users to express views with real capital before an IPO. They allow both long and short positions. Prices update continuously, and positions can be publicly observed. They do not require users to actually own the stock, nor do they promise stock delivery. Instead, they trade “the market’s expectation of future public equity prices” through cash settlement.
This is a new species that sits somewhere between prediction markets, perpetual futures, and private secondary markets.
3.1 Why Might Pre-IPO Perps Be More Accurate Than Traditional Private Secondary Markets?
Private secondary markets such as Forge and Hiive are closer to real equity transactions, but they naturally have several problems.
First, participation barriers are high. Usually, only accredited investors, high-net-worth individuals, VCs, funds, and institutions can participate. Second, liquidity is fragmented. Private equity does not trade through a continuous order book, and transactions may take days or even weeks to complete. Third, short-selling mechanisms are missing. The market can express buy-side demand, but it is difficult to publicly express bearish views. Fourth, information updates slowly. There are delays between valuations, financing rounds, listings, and transaction prices.
On-chain pre-IPO perps have a completely different structure. They do not require actual equity transfer, so trading can be more continuous. They use margin and perpetual contracts to express views, so users can short. They are open to global wallet users, so information sources are broader. They aggregate expectations in real time through public order books and transaction prices, making it easier to capture breaking news and market sentiment.
This leads to an important change: prices in traditional private markets come from quotes by a small number of people, while prices in on-chain pre-IPO perps come from the game among global traders.
This is especially important in the era of AI mega-IPOs. Cerebras may only be the beginning. If companies such as SpaceX, OpenAI, Anthropic, Databricks, Perplexity, and xAI enter listing windows, pre-IPO perps around these companies may become the new markets that global traders pay the most attention to.
3.2 What Pre-IPO Perps Truly Disrupt Is “Who Owns Pricing Power”
In traditional finance, pricing power is often held by investment banks, exchanges, funds, brokerages, market makers, and data providers. Ordinary users can only consume prices after they have already been produced.
Crypto markets open up the process of price production. Every wallet address, every order, every liquidation, and every funding rate change becomes part of the price formation process.
The revolutionary nature of pre-IPO perps lies in turning the originally closed late-stage private market and IPO bookbuilding process into a publicly tradable market.
This logic is similar to prediction markets: price is not only the result of trading, but also information itself. When TradeXYZ’s CBRS price continued to trade before the IPO, it essentially became a public information source. There were even community rumors that traditional finance traders were watching the on-chain CBRS chart. This phenomenon sends an important signal: on-chain markets are no longer merely following TradFi. They are beginning to export price signals back to TradFi.
If this trend continues, on-chain markets may gain pricing power in three areas.
First, valuations of pre-IPO technology companies, especially in AI, space, robotics, semiconductors, defense technology, and energy infrastructure.
Second, event-driven assets, such as earnings, M&A, regulatory approvals, major contracts, model releases, and export controls.
Third, long-tail global assets that traditional exchanges cannot efficiently cover, but for which global users have strong trading demand. These assets can all be perpetualized on-chain.
4. The AI U.S. Equity Thesis: From GPUs to “Cheaper, More Abundant, and More Efficient Tokens”
The foundational phase of the AI thesis was training. The market focused on GPUs, compute, model parameters, training clusters, and cloud CapEx. Nvidia became the core asset because GPUs were the scarcest means of production in the AI training era.
The second phase is inference. As AI applications scale, the real bottleneck is no longer only whether larger models can be trained, but whether more tokens can be generated at lower cost, lower latency, and higher throughput.
Therefore, the AI investment thesis for 2026–2028 can be summarized in one sentence: providing cheaper, more abundant, and more efficient tokens.
Behind this sentence is the repricing of the entire AI infrastructure stack. In the past, the market focused most on GPUs. Going forward, the market will gradually focus on bottlenecks beyond GPUs: memory, storage, I/O, networking, optical communications, power, cooling, ASICs, data center construction, cloud platform scheduling, and inference optimization.
This also explains why AI equities are not a single-stock Nvidia story, but a supply-chain diffusion story.
GPUs drive HBM. HBM drives DRAM, NAND, and SSDs. Large-scale clusters drive power equipment, liquid cooling, UPS, power distribution, and data center delivery. Multi-machine interconnect drives 800G, 1.6T, silicon photonics, CPO, DSP, SerDes, retimers, and switches. Inference demand drives CPUs, ASICs, networking, storage, and scheduling systems.
From a trading perspective, opportunities in AI equities will gradually spread from individual leaders to bottleneck segments. The market is not only trading “who is the strongest,” but also “where the next bottleneck is.”
4.1 Five Core Investment Lines
The first line is memory and storage.
Both AI training and inference require massive data movement. HBM is the most visible bottleneck, but it is not the only one. DRAM, NAND, SSDs, CXL, memory controllers, memory interfaces, and storage arrays will all benefit. Relevant names include MU, SNDK, WDC, STX, RMBS, SK Hynix, and Samsung. For on-chain U.S. equity perpetuals, these stocks are highly suitable for trading around earnings, capacity, pricing cycles, and supply disruptions.
The second line is I/O and high-speed interconnect.
As the number of GPUs increases, the problem shifts from “whether there are enough GPUs” to “how GPUs communicate efficiently with each other.” PCIe, CXL, SerDes, retimers, switches, DPUs, and NICs will become data center performance bottlenecks. Relevant companies include ALAB, CRDO, MRVL, and AVGO. Their volatility may not be as widely discussed as NVDA’s, but for professional traders, their upside elasticity is not low.
The third line is networking and optical communications.
AI data centers are increasingly becoming giant distributed computing networks. 800G, 1.6T, silicon photonics, CPO, InP, DSP, optical modules, switches, and data center networking will become important directions for the next phase of capital expenditure. Relevant companies include ANET, AVGO, MRVL, COHR, LITE, CIEN, and AAOI.
The fourth line is power and cooling.
AI is not a pure software revolution. It is also an energy-density revolution. Data center constraints are increasingly coming from power access, transformers, UPS, power distribution, liquid cooling, heat dissipation, data center construction, and grid expansion. Relevant companies include VRT, ETN, GEV, PWR, and EME. Future AI trading will not only look at chips, but also power.
The fifth line is custom ASICs.
As inference cost becomes the key issue, large technology companies will increasingly use in-house chips or semi-custom chips. Google TPU, Amazon Trainium/Inferentia, Meta MTIA, Microsoft Maia, OpenAI’s potential in-house chips, and ASIC service providers such as AVGO and MRVL may all become market focuses.
Together, these five lines form the core trading map for AI equities from 2026 to 2028.
4.2 Reinterpreting AI Equities from a Crypto Perspective
Traditional equity research focuses on revenue, gross margin, orders, capacity, valuation, and cash flow. Crypto traders care more about narrative diffusion, capital flows, volatility windows, leverage efficiency, and liquidity depth.
Combining these two perspectives, trading opportunities in U.S. AI equity perpetuals can be divided into three categories.
The first category is fundamental event trading. This includes earnings, CapEx guidance, new product releases, order disclosures, supply chain shortages, export controls, and M&A news.
The second category is narrative rotation trading. For example, the market may rotate from GPUs to HBM, from HBM to optical modules, from optical modules to power, from power to ASICs, and from ASICs to cloud providers and AI applications.
The third category is on-chain structural trading. This includes funding rate arbitrage, cross-platform spreads, convergence between pre-IPO perps and post-listing stock prices, CEX-DEX liquidity spreads, and price discovery during weekends or market closures.
This is where crypto markets and U.S. AI equities converge: not simply buying stocks, but turning the AI supply chain into a 24-hour, global, leveragable, composable, and hedgeable trading network.
5. Core Mechanisms of the U.S. Equity Perpetuals: Price Sources, Funding Rates, and Market Closure Risk
The biggest challenge for U.S. equity perpetuals is not matching orders, but pricing.
Crypto assets trade 24/7. BTC and ETH always have a price. But U.S. equities have trading hours, and liquidity varies greatly across regular sessions, pre-market, after-hours, weekends, and holidays. If an on-chain contract trades 24 hours a day while the external spot market stops, one question emerges: how should the oracle quote prices?
Different platforms provide different answers.
TradeXYZ handles indices and individual stocks differently. For indices, it can reference futures prices that are closer to round-the-clock trading, such as CME-related futures, and then infer the corresponding spot index price through a cost-of-carry model. For individual stocks, it relies more on external price sources such as Pyth to continue obtaining data during pre-market, after-hours, and extended sessions. When there is no external price input at all, the system needs to rely on internal order books and EMA mechanisms to smooth prices.
Ostium’s approach is closer to traditional financial market hours. Through its RWA oracle system, it handles different asset trading hours, opening gaps, futures rollovers, holidays, and other issues. During market closures, limit orders and stop-loss orders can be submitted in advance, but they are only executed after the market reopens and the price conditions are met. Market orders usually cannot be submitted during market closures. This mechanism sacrifices some of the excitement of 24-hour trading, but emphasizes closer alignment with real market prices.
Lighter’s strategy is more risk-freezing oriented. During market closures, markets enter reduce-only mode, meaning users can only reduce positions and cannot increase exposure or expand risk. Funding rates can still be calculated, but active trading is restricted.
These three solutions essentially represent three different risk preferences. TradeXYZ is more aggressive and emphasizes autonomous price discovery through on-chain order books. Ostium is more conservative and emphasizes alignment with real market rhythms. Lighter is more platform risk-control oriented and emphasizes preventing risk from expanding indefinitely during market closures.
5.1 Dividends, Corporate Actions, and Funding Rates: The Most Overlooked Problem in the U.S. Equity Perpetuals
The biggest difference between U.S. equity perpetuals and crypto perpetuals is that stocks have dividends and corporate actions.
BTC does not pay dividends. ETH does not distribute dividends like a stock. But Apple, Microsoft, Nvidia, and other listed companies may have dividends, stock splits, ex-dividend adjustments, and other corporate actions. Traditional stocks naturally adjust downward on ex-dividend dates. If perpetual contracts do not handle this properly, risk-free arbitrage can appear.
For example, if a stock trades at $100 and pays a $2 dividend, its theoretical price becomes $98 after going ex-dividend. If the perpetual contract does not adjust for this, short sellers can short before the ex-dividend date and profit from the price drop afterward, while long holders suffer losses without compensation.
TradeXYZ’s approach is more like smoothing the dividend impact through funding rates. Before the ex-dividend date, the mark price gradually reflects the discount caused by the future dividend, funding rates turn negative, and shorts pay longs, thereby offsetting the arbitrage opportunity created by the ex-dividend price gap.
Ostium’s treatment is more like “the contract only tracks price changes.” It does not directly compensate longs for dividends, but instead reflects holding costs through rollover fees. Ostium’s documentation explicitly states that it does not use the zero-sum funding rates between longs and shorts commonly found in crypto perps. Instead, it applies rollover fees based on real-world carry costs across all trading pairs.
Lighter’s public documentation provides relatively limited details on dividend handling. But mechanically, if the oracle price adjusts downward on the ex-dividend date and the mark price deviates from the index price, funding rates may play part of the balancing role. It is worth noting that Lighter sets caps on funding rates, which helps prevent traders from being overwhelmed by fees in extreme scenarios.
5.2 The Higher the Leverage, the More Risk Becomes Exponential Rather Than Linear
Ostium supports relatively high leverage, and leverage on some stocks can be very aggressive. TradeXYZ and Lighter are more restrained by comparison. High leverage appears to improve capital efficiency, but for U.S. equity perpetuals, the risk is more complex than in crypto perpetuals.
First, U.S. equities have gap risk. Crypto markets are volatile, but continuous trading means prices tend to move more gradually. U.S. stocks can gap at the open and jump directly across stop-loss or liquidation levels.
Second, U.S. equities have market closures. During weekends and holidays, external prices may be unavailable, but news continues to happen. AI companies may release major news over the weekend, regulators may announce policies outside trading hours, and geopolitical events may affect semiconductor and energy stocks.
Third, U.S. equities have corporate actions. Dividends, splits, M&A, regulatory investigations, and earnings revisions can all affect contract pricing.
Fourth, AI stocks are crowded trades. The more popular an asset is, the more likely leverage becomes concentrated in one direction. Once the market reverses, liquidations can amplify volatility.
Therefore, U.S. equity perpetuals are not simply “U.S. stocks plus leverage.” They are a combination of U.S. equity event risk, crypto leverage structures, oracle risk, and market-closure gap risk.
6. HTX’s Position in This Trend: From Exchange to an “AI × RWA × Derivatives” Gateway
HTX has established an institutional footprint at the intersection of equity derivatives and AI-native trading infrastructure through two primary product initiatives:.
The first is a rapid expansion in TradFi perpetuals. As of May 21, 2026, the platform has listed 66 TradFi perpetuals, forming a full-spectrum asset matrix — U.S. equity exposure spans three core segments (unicorn Pre-IPO names, AI compute and tech mega-caps, and traditional Wall Street blue chips), complemented by two adjacent categories: precious metals and commodities, and global indices and sector ETFs. This allows crypto users to trade the event-driven volatility of individual U.S. stocks and the beta of macro assets and sectors within a single environment. It’s worth noting that HTX supports Pre-IPO perps — SpaceX, OpenAI, and Anthropic, currently among the most closely watched private assets. This indicates that HTX is not simply migrating listed U.S. equities into a crypto exchange environment, but also introducing the price discovery demand of the private secondary market into crypto trading infrastructure, which represents one concrete CEX-side manifestation of the “pricing power migration” central to this report.
The second is the launch of HTX AI Skills, a trading capability protocol opened by HTX for the AI Agent ecosystem. It allows AI to understand and execute trading operations through natural language. According to official descriptions, HTX AI Skills initially supports two major capabilities: spot trading and futures trading. These include market orders, limit orders, order placement, order cancellation, order status queries, as well as opening long and short futures positions, adjusting leverage, and setting take-profit and stop-loss orders. It is also compatible with mainstream AI tools such as OpenClaw, Claude Code, Codex, and Cursor.
Overall, the U.S. equity perpetuals solve the problem of “expanding tradable assets,” allowing HTX to move beyond pure crypto asset trading and extend into the volatility of traditional financial assets. AI Skills solves the problem of “upgrading the trading interaction layer,” gradually transforming actions that previously required manual execution, such as checking market conditions, placing orders, and managing futures positions, into capabilities that can be called and executed by AI Agents.
Together, these two product directions show that HTX is evolving from a single crypto trading platform into a broader trading infrastructure that covers more asset classes and provides an AI-native trading gateway.
Reference
https://oldcoinbad.com/p/non-arbitrage-conditions-for-perpetual
https://www.theblockbeats.info/news/60366
The post first appeared on HTX Square.
