如何用 Hyperliquid 做市机器人实现 220 倍收益?

深潮Published on 2025-09-15Last updated on 2025-09-16

🤔两周内将 6800 美元增至 150 万美元。

撰文:The Smart Ape

编译:Saoirse,Foresight News

这是一个绝佳案例,足以说明「学习编程」的重要性 —— 借助编程,你只需两周时间,就能在加密货币交易所 Hyperliquid 平台上,将 6800 美元增值至 150 万美元。

不久前,一位 Hyperliquid 交易者就做到了这一点。

更令人惊叹的是,该交易者几乎没有承担任何风险。他既没有押注市场走向,也没有跟风炒作热门资产,仅仅依靠一套精密的做市策略 —— 核心逻辑围绕「做市商返利」展开,并结合自动化操作与严格的风险管控。

Hyperliquid 平台的做市机制

在深入解析该策略前,我们需要先理解 Hyperliquid 平台的做市逻辑。Hyperliquid 是一家订单簿模式交易所,用户可在平台上下达两类订单:

  • 买单:即「买入订单」(例如,「我想以 100 美元的价格买入 SOL 代币」)

  • 卖单:即「卖出订单」(例如,「我想以 101 美元的价格卖出 SOL 代币」)

这些待成交的订单共同构成了「订单簿」。而下达买单或卖单的交易者,被称为「做市商」(Makers)。

  • 做市商的核心作用是「提供流动性」:通过提前下达限价订单,为市场补充可交易的订单量。

  • 与之相对的是「吃单者」(Takers):这类交易者会直接成交订单簿中已有的订单(例如,以当前最优卖价「市价买入」某代币)。

做市商对市场至关重要:正是因为有他们提供流动性,市场买卖价差才能维持在较低水平;若缺乏做市商,交易者可能面临「定价不合理」「滑点损失大」等问题。

核心关键:做市商返利

交易所的核心是「流动性」—— 为鼓励用户成为做市商、补充市场流动性,Hyperliquid 会为做市商提供「成交返利」:每当做市商的订单被成交时,平台会返还一小笔返利。

在 Hyperliquid 平台,每笔交易的返利比例约为 0.0030% —— 也就是说,每交易 1000 美元,就能获得 0.03 美元的返利。

而正是这看似微薄的返利,让那位交易者实现了「6800 美元到 150 万美元」的跨越。他的策略核心是「单边报价」:只在订单簿的某一侧(要么只下买单,要么只下卖单)下达限价订单;一旦市场价格变动,就迅速撤销原有订单或切换到另一侧报价。

简单来说,他的操作逻辑是:只在单侧提供流动性以赚取返利,同时通过机器人实时调整订单方向,避免因持仓暴露而承担风险。最终,依靠「自动化高频交易」带来的巨大交易量,微薄的单笔返利积少成多,最终转化为巨额利润。

传统做市商的核心痛点

大多数做市商会同时在订单簿的「买侧」和「卖侧」下达订单。

举个例子:你同时下达两笔订单 —— 以 100 美元买入 1 枚 SOL 的买单,和以 101 美元卖出 1 枚 SOL 的卖单。

若两笔订单都能成交,你就通过「低买高卖」赚取了 1 美元的价差利润。

但这种模式存在一个关键问题:持仓风险。

  • 若买单成交、卖单未成交:你会被动持有 SOL 代币;

  • 若卖单成交、买单未成交:你会被动持有稳定币(如 USDT)。

一旦市场价格向不利于你的方向波动,这些被动持有的资产就会面临大幅亏损。

这也是那位 Hyperliquid 交易者选择「单边报价」的原因:通过单侧下单,他能更严格地控制持仓,避免被动持有不必要的资产。不过,这种模式面临的代价是,更高的「被套利」风险。

「被套利」是什么意思?

举个具体场景:你在订单簿上下达了「以 100 美元买入 SOL」的买单。此时,突发利空消息导致 SOL 价格瞬间跌至 90 美元。

  • 你那笔「100 美元买入」的订单仍在订单簿中未被撤销;

  • 速度更快的交易者会立即以「100 美元」的价格向你卖出 SOL(即与你的买单成交);

  • 最终结果:你多花了 10% 的成本买入 SOL,即便能获得平台返利,仍会承受巨额亏损。

这种情况被称为「逆向选择」,也就是我们常说的「被套利」。

因此,在采用「单边报价」策略时,「精准度」和「速度」是成败关键 —— 整个策略的有效性,完全取决于机器人的反应效率和操作准确性。

高频交易基础设施

为避免「被套利」,那位交易者搭建了一套「超高速执行系统」,核心包括:

  • 托管服务:将交易服务器物理部署在靠近 Hyperliquid 平台服务器的位置,最大限度降低网络延迟;

  • 自动化操作:机器人每秒可调整数千次报价,实现「实时跟价」;

  • 实时风险控制:在持仓风险失控前,自动平仓或调整仓位。

这类基础设施的搭建既需要高额成本,技术复杂度也极高 —— 这也是为什么只有少数专业做市商能部署此类系统。

从技术细节来看,他的交易机器人很可能是用 C++ 或 Rust 语言编写的(这两种语言以「运行速度快」「延迟低」著称);服务器则托管在靠近 Hyperliquid「订单匹配引擎」的位置,确保他的订单能优先被匹配。

机器人通过 WebSocket 或 gRPC 协议获取实时订单簿数据,在毫秒级时间内完成「下单 - 撤单 - 切换报价方向」的操作 —— 既保证能持续赚取返利,又避免订单因价格变动而「失效」。

如何保持「Delta 中性」?

最令人印象深刻的是,那位交易者始终保持「Delta 中性」状态:尽管他的总交易量高达数十亿美元,但净持仓风险始终控制在 10 万美元以内。

他是如何做到的?

  1. 机器人实时追踪 SOL 代币的持仓量变化;

  2. 设置严格的风险上限(净持仓风险绝不超过 10 万美元);

  3. 一旦持仓风险接近上限,机器人会立即停止当前侧的交易,并切换到对侧报价,通过反向交易实现仓位再平衡。

他没有采用「现货与期货套利」的模式,而是完全在「永续合约」市场操作 —— 由于所有交易都在同一市场完成,仓位对冲和风险控制会更简单。

不过,这种策略对「纪律性」和「精准度」要求极高:哪怕是最小的操作失误,都可能导致巨额亏损。

背后的数学逻辑

整个策略的收益计算逻辑其实很清晰:

  • 两周内,该交易者的总交易量达到 14 亿美元;

  • 做市商返利比例为 0.003%/ 笔;

  • 仅通过返利获得的利润 = 14 亿美元 × 0.003% ≈ 42 万美元。

在此基础上,他还采用了「利润再投资」策略 —— 将每笔返利立即重新投入交易,通过「复利效应」放大收益。最终,总利润达到了 150 万美元。

而这一切的起点,仅仅是 6800 美元的初始交易资金。

为什么你无法直接复制这个策略?

你可能会想:「既然如此,我直接复制他的交易不就能赚同样多的钱吗?」但现实是,这种策略几乎无法复制,核心原因包括:

  1. 你没有他那样的「执行速度」:专业托管服务器 + 低延迟代码的组合,是普通交易者难以企及的;

  2. 你没有他那样的「资金规模」:虽然初始资金只有 6800 美元,但随着利润复利,后期交易规模已达到专业级别;

  3. 你没有「精准的代码和机器人」:他的机器人经过反复调试,能适配订单簿的每一次微小波动,普通开发者难以复刻;

  4. 你没有「24 小时不间断的基础设施和监控」:加密货币市场是 7×24 小时交易的,需要实时监控系统以应对突发风险。

简言之,这是一套「专业级高频交易系统」,并非普通散户能轻易复制。

该策略的潜在风险

即便对于这种高度精密的机器人,仍存在不可忽视的风险:

  1. 服务器故障:若服务器崩溃,可能导致机器人无法及时撤单,被动持有大量风险仓位;

  2. 交易所故障:虽然罕见,但 Hyperliquid 平台若出现宕机或故障,可能在几秒内打乱机器人的交易逻辑;

  3. 极端市场波动:剧烈行情可能打破「单边报价」的平衡,导致策略失效并产生亏损;

  4. 费率结构变更:若 Hyperliquid 调整做市商返利比例或交易手续费,可能会立即让该策略的盈利能力大幅下降。

这套策略虽然精妙,但并非「无懈可击」。

结论

两周内将 6800 美元增值至 150 万美元,听起来可能像「靠 Meme 币碰运气」,但实际上,这背后是扎实的技术能力、严格的纪律性和精密的系统设计。

这是一个绝佳的案例研究, 展示了如何「规模化利用做市商返利」「保持 Delta 中性」,并将「方向性风险」降到最低。

这个案例带来的核心启示是:交易不只是「预测价格」。有时候,最赚钱的策略,是吃透市场结构规则,并搭建一套能在「别人忽视的角落」创造价值的系统。

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Below is a suggested timeline mapping significant events in the evolution of SPERO,$$s$: Conceptualisation and Ideation Phase: The initial ideas forming the basis of SPERO,$$s$ were conceived, aligning closely with the principles of decentralisation and community focus within the blockchain industry. Launch of Project Whitepaper: Following the conceptual phase, a comprehensive whitepaper detailing the vision, goals, and technological infrastructure of SPERO,$$s$ was released to garner community interest and feedback. Community Building and Early Engagements: Active outreach efforts were made to build a community of early adopters and potential investors, facilitating discussions around the project’s goals and garnering support. Token Generation Event: SPERO,$$s$ conducted a token generation event (TGE) to distribute its native tokens to early supporters and establish initial liquidity within the ecosystem. Launch of Initial dApp: The first decentralised application (dApp) associated with SPERO,$$s$ went live, allowing users to engage with the platform's core functionalities. Ongoing Development and Partnerships: Continuous updates and enhancements to the project's offerings, including strategic partnerships with other players in the blockchain space, have shaped SPERO,$$s$ into a competitive and evolving player in the crypto market. Conclusion SPERO,$$s$ stands as a testament to the potential of web3 and cryptocurrency to revolutionise financial systems and empower individuals. With a commitment to decentralised governance, community engagement, and innovatively designed functionalities, it paves the way toward a more inclusive financial landscape. As with any investment in the rapidly evolving crypto space, potential investors and users are encouraged to research thoroughly and engage thoughtfully with the ongoing developments within SPERO,$$s$. The project showcases the innovative spirit of the crypto industry, inviting further exploration into its myriad possibilities. While the journey of SPERO,$$s$ is still unfolding, its foundational principles may indeed influence the future of how we interact with technology, finance, and each other in interconnected digital ecosystems.

413 Total ViewsPublished 2024.12.17Updated 2024.12.17

What is $S$

What is AGENT S

Agent S: The Future of Autonomous Interaction in Web3 Introduction In the ever-evolving landscape of Web3 and cryptocurrency, innovations are constantly redefining how individuals interact with digital platforms. One such pioneering project, Agent S, promises to revolutionise human-computer interaction through its open agentic framework. By paving the way for autonomous interactions, Agent S aims to simplify complex tasks, offering transformative applications in artificial intelligence (AI). This detailed exploration will delve into the project's intricacies, its unique features, and the implications for the cryptocurrency domain. What is Agent S? Agent S stands as a groundbreaking open agentic framework, specifically designed to tackle three fundamental challenges in the automation of computer tasks: Acquiring Domain-Specific Knowledge: The framework intelligently learns from various external knowledge sources and internal experiences. This dual approach empowers it to build a rich repository of domain-specific knowledge, enhancing its performance in task execution. Planning Over Long Task Horizons: Agent S employs experience-augmented hierarchical planning, a strategic approach that facilitates efficient breakdown and execution of intricate tasks. This feature significantly enhances its ability to manage multiple subtasks efficiently and effectively. Handling Dynamic, Non-Uniform Interfaces: The project introduces the Agent-Computer Interface (ACI), an innovative solution that enhances the interaction between agents and users. Utilizing Multimodal Large Language Models (MLLMs), Agent S can navigate and manipulate diverse graphical user interfaces seamlessly. Through these pioneering features, Agent S provides a robust framework that addresses the complexities involved in automating human interaction with machines, setting the stage for myriad applications in AI and beyond. Who is the Creator of Agent S? While the concept of Agent S is fundamentally innovative, specific information about its creator remains elusive. The creator is currently unknown, which highlights either the nascent stage of the project or the strategic choice to keep founding members under wraps. Regardless of anonymity, the focus remains on the framework's capabilities and potential. Who are the Investors of Agent S? As Agent S is relatively new in the cryptographic ecosystem, detailed information regarding its investors and financial backers is not explicitly documented. The lack of publicly available insights into the investment foundations or organisations supporting the project raises questions about its funding structure and development roadmap. Understanding the backing is crucial for gauging the project's sustainability and potential market impact. How Does Agent S Work? At the core of Agent S lies cutting-edge technology that enables it to function effectively in diverse settings. Its operational model is built around several key features: Human-like Computer Interaction: The framework offers advanced AI planning, striving to make interactions with computers more intuitive. By mimicking human behaviour in tasks execution, it promises to elevate user experiences. Narrative Memory: Employed to leverage high-level experiences, Agent S utilises narrative memory to keep track of task histories, thereby enhancing its decision-making processes. Episodic Memory: This feature provides users with step-by-step guidance, allowing the framework to offer contextual support as tasks unfold. Support for OpenACI: With the ability to run locally, Agent S allows users to maintain control over their interactions and workflows, aligning with the decentralised ethos of Web3. Easy Integration with External APIs: Its versatility and compatibility with various AI platforms ensure that Agent S can fit seamlessly into existing technological ecosystems, making it an appealing choice for developers and organisations. These functionalities collectively contribute to Agent S's unique position within the crypto space, as it automates complex, multi-step tasks with minimal human intervention. As the project evolves, its potential applications in Web3 could redefine how digital interactions unfold. Timeline of Agent S The development and milestones of Agent S can be encapsulated in a timeline that highlights its significant events: September 27, 2024: The concept of Agent S was launched in a comprehensive research paper titled “An Open Agentic Framework that Uses Computers Like a Human,” showcasing the groundwork for the project. October 10, 2024: The research paper was made publicly available on arXiv, offering an in-depth exploration of the framework and its performance evaluation based on the OSWorld benchmark. October 12, 2024: A video presentation was released, providing a visual insight into the capabilities and features of Agent S, further engaging potential users and investors. These markers in the timeline not only illustrate the progress of Agent S but also indicate its commitment to transparency and community engagement. Key Points About Agent S As the Agent S framework continues to evolve, several key attributes stand out, underscoring its innovative nature and potential: Innovative Framework: Designed to provide an intuitive use of computers akin to human interaction, Agent S brings a novel approach to task automation. Autonomous Interaction: The ability to interact autonomously with computers through GUI signifies a leap towards more intelligent and efficient computing solutions. Complex Task Automation: With its robust methodology, it can automate complex, multi-step tasks, making processes faster and less error-prone. Continuous Improvement: The learning mechanisms enable Agent S to improve from past experiences, continually enhancing its performance and efficacy. Versatility: Its adaptability across different operating environments like OSWorld and WindowsAgentArena ensures that it can serve a broad range of applications. As Agent S positions itself in the Web3 and crypto landscape, its potential to enhance interaction capabilities and automate processes signifies a significant advancement in AI technologies. Through its innovative framework, Agent S exemplifies the future of digital interactions, promising a more seamless and efficient experience for users across various industries. Conclusion Agent S represents a bold leap forward in the marriage of AI and Web3, with the capacity to redefine how we interact with technology. While still in its early stages, the possibilities for its application are vast and compelling. Through its comprehensive framework addressing critical challenges, Agent S aims to bring autonomous interactions to the forefront of the digital experience. As we move deeper into the realms of cryptocurrency and decentralisation, projects like Agent S will undoubtedly play a crucial role in shaping the future of technology and human-computer collaboration.

1.1k Total ViewsPublished 2025.01.14Updated 2025.01.14

What is AGENT S

Discussions

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