数据透视六大做市商“85”暴跌前后操作

Odaily星球日报Publicado em 2024-08-07Última atualização em 2024-08-07

Resumo

表现不一：跟风减持、借机抄底BTC、手拿山寨纹丝不动。

原创 | Odaily星球日报（@OdailyChina）

作者｜Golem（@web3_golem）

数据透视六大做市商“85”暴跌前后操作除了传统金融市场等宏观因素影响外，头部做市商 Jump Trading 的抛售一度被认为是引起本次“ 85 ”下跌的重要因素。

那么，在 Jump 抛售市场下跌前后，其他加密头部做市商们都有哪些操作，是跟风抛售 ETH、持有大量稳定币避险，还是抄底优质资产？Odaily星球日报将从数据角度解析其他 6 大做市商的链上公开地址，分析他们在“ 85 ”暴跌前后的持仓异动并进行总结，希望为读者在风云诡谲的行情变化中提供些许参考。

GSR Markets：持续抛售 ETH

“ 85 ”暴跌前（8.1-8.5）

在“ 85 ”暴跌之前，根据 ARKHAM 数据，GSR Markets 链上公开地址在 8 月 1 日至 8 月 5 日总持仓量净值减少。主要变化为：ETH 持仓减少超 706 枚，STETH 持仓减少 300 枚，稳定币 USDC 持仓减少超 334 万枚，L3 持仓减少超 856 万枚。详细数据如下图所示。

数据透视六大做市商“85”暴跌前后操作

GSR Markets 8 月 1 日至 8 月 5 日持仓量变化（数据更新时间为 UTC 时间每日 0 点，来源：ARKHAM）

同时对 ETH 持仓进行单独分析发现，GSR Markets 在 8 月 1 日至 8 月 2 日之间将超 1000 枚 ETH 转入了币安等交易所。

数据透视六大做市商“85”暴跌前后操作

GSR Markets ETH 链上公开地址持仓量变化（来源：ARKHAM）

数据透视六大做市商“85”暴跌前后操作

GSR Markets 在 8 月 1 日至 8 月 2 日将 ETH 转移至交易所

“ 85 ” 暴跌后（8.5-8.6）

而在“ 85 ”暴跌发生后，根据 ARKHAM 数据，GSR Markets 链上公开地址仍然在减持超 100 多枚 ETH，但其他的山寨币减持并不明显。

数据透视六大做市商“85”暴跌前后操作

GSR Markets 8 月 5 日至 8 月 6 日持仓量变化（数据更新时间为 UTC 时间每日 0 点，来源：ARKHAM）

小结

从链上持仓变化数据可以看出，GSR Markets 在“ 85 ”暴跌前极可能跟随 Jump 进行了 ETH 的抛售，并且在暴跌后依然在进行减持。稳定币作为避险资产一直是 GSR Markets 最大仓位，可能表明其对后市仍然不太乐观。

Amber Group：暴跌时恐慌性抛售 ETH

“ 85 ” 暴跌前（8.1-8.5）

在“ 85 ”暴跌之前，根据 ARKHAM 数据，Amber Group 链上公开地址在 8 月 1 日至 8 月 5 日总持仓量净值在增加。主要变化为：ETH 持仓增加超 1.15 万枚，稳定币 USDC 持仓增加 50 万枚。详细数据如下图所示。

数据透视六大做市商“85”暴跌前后操作

Amber Group 8 月 1 日至 8 月 5 日持仓量变化（数据更新时间为 UTC 时间每日 0 点，来源：ARKHAM）

“ 85 ” 暴跌后（8.5-8.6）

而在“ 85 ”暴跌发生后，根据 ARKHAM 数据，Amber Group 链上公开地址对 ETH 进行了大量减持，共减持超 1.3 万枚 ETH，其中有将近一半转移进了交易所。

数据透视六大做市商“85”暴跌前后操作

Amber Group ETH 链上公开地址持仓量变化（来源：ARKHAM）

数据透视六大做市商“85”暴跌前后操作

Amber Group 在 8 月 5 日至 8 月 6 日将大量 ETH 转移至交易所

小结

从以上的持仓变化可以看出，Amber Group 在暴跌前并未有多少预感，也未跟随 Jump 进行抛售，而是大量增持了 ETH。但是当“ 85 ”暴跌发生时，Amber Group 将几天前增持的 ETH 都进行了转移，其中超一半直接进入了交易所。Amber Group 的持仓变化表明，与其他做市商相比，其并未预料到黑天鹅的来临，而更像普通交易者被市场左右。

B2C 2 Group：大规模减持所有代币

“ 85 ” 暴跌前（8.1-8.5）

在“ 85 ”暴跌之前，根据 ARKHAM 数据，B2C 2 Group 链上公开地址在 8 月 1 日至 8 月 5 日总持仓量净值在增长。主要变化为：ETH 持仓增长 4650 枚，BTC 持仓增长 531.52 枚，同时还增持了 21.65 万枚 UNI、 2.3 万枚 COMP、 3.2 万枚 DAI。详细数据如下图所示。

数据透视六大做市商“85”暴跌前后操作

B2C 2 Group 8 月 1 日至 8 月 5 日持仓量变化（数据更新时间为 UTC 时间每日 0 点，来源：ARKHAM）

“ 85 ” 暴跌后（8.5-8.6）

而在“ 85 ”暴跌发生后，根据 ARKHAM 数据，B2C 2 Group 几乎对所有代币都进行了减持，并且将其换仓了稳定币资产进行避险。其中 BTC 减持超 1000 枚，ETH 减持超 1.9 万枚，BNB 减持超 4000 枚，LINK 减持超 5.9 万枚，COMP 减持超 2.3 万枚。详细数据如下图所示。

数据透视六大做市商“85”暴跌前后操作

B2C 2 Group 8 月 5 日至 8 月 7 日持仓量变化（数据更新时间为 UTC 时间每日 0 点，来源：ARKHAM）

同时对 ETH 进行单独分析发现，B2C 2 Group 在 8 月 5 日至 8 月 6 日之间集中将大量 ETH 转入到了 Robinhood、Coinbase 及链上一些不明地址。

数据透视六大做市商“85”暴跌前后操作

B2C 2 Group ETH 链上公开地址持仓量变化（来源：ARKHAM）

数据透视六大做市商“85”暴跌前后操作

Amber Group 在 8 月 5 日至 8 月 6 日将大量 ETH 转移至 Robinhood 及其他不明地址

小结

从以上持仓数据可看出，B2C 2 Group 在“ 85 ”暴跌之前并未跟随 Jump 进行减持 ETH，反而在增持；但是在暴跌之后迅速减少持有的几乎所有代币，但大多数代币转入了未知地址。虽然没有明显证据证明其在抛售，但 B2C 2 Group 目前持有的最大仓位是避险资金稳定币，也能侧面反映出其对后市不怎么乐观。

数据透视六大做市商“85”暴跌前后操作

Amber Group 目前的持仓

Wintermute：暴跌前后都在增持

“ 85 ” 暴跌前（8.1-8.5）

在“ 85 ”暴跌之前，根据 ARKHAM 数据，Wintermute 链上公开地址在 8 月 1 日至 8 月 5 日总持仓量净值在增长。主要变化为：稳定币 USDT 和 USDC 持仓增长超 1.04 亿枚，ETH 持仓增长超 1.493 万枚，WBTC 持仓增长 104.74 枚，同时还增持了 400 万枚 PEPE。详细数据如下图所示。

数据透视六大做市商“85”暴跌前后操作

Wintermute 8 月 1 日至 8 月 5 日持仓量变化（数据更新时间为 UTC 时间每日 0 点，来源：ARKHAM）

“ 85 ” 暴跌后（8.5-8.6）

而在“ 85 ”暴跌发生后，根据 ARKHAM 数据，Wintermute 链上公开地址不仅没有大量减持，反而仍在增持。主要变化为：稳定币 USDT 和 USDC 持仓增长超 6200 万枚，WBTC 持仓增长 131.5 枚，同时还增持了 3298 千亿枚 PEPE 和 69 万枚 DAI。详细数据如下图所示。

数据透视六大做市商“85”暴跌前后操作

Wintermute 8 月 5 日至 8 月 6 日持仓量变化（数据更新时间为 UTC 时间每日 0 点，来源：ARKHAM）

同时对 ETH 进行单独分析发现，Wintermute 在“ 85 ”暴跌和 Jump 大量抛售 ETH 期间链上 ETH 持仓并未有明显变化，总持仓量依然超 1.6 万枚。

数据透视六大做市商“85”暴跌前后操作

Wintermute ETH 链上公开地址持仓量变化（来源：ARKHAM）

小结

Wintermute 在暴跌之前虽然大量增持了稳定币避险，但也增持了 ETH；同时在暴跌后除了调仓稳定币避险以外，又继续增持了 BTC，并且前先流入的 ETH 并未有大量流出。从链上的这些持仓变化数据来看，Wintermute 似乎对本次“ 85 ”大跌和 Jump 抛售并未有剧烈恐慌反应，对后市持较为乐观态度。

Flow Traders：抛售 ETH 但抄底 BTC

“ 85 ” 暴跌前（8.1-8.5）

在“ 85 ”暴跌之前，根据 ARKHAM 数据，Flow Traders 链上公开地址在 8 月 1 日至 8 月 5 日总持仓量净值在减少。主要变化为：ETH 持仓减少 633.93 枚，BTC 持仓减少 142.69 枚，稳定币持仓减少变化并被明显。详细数据如下图所示。

数据透视六大做市商“85”暴跌前后操作

Flow Traders 8 月 1 日至 8 月 5 日持仓量变化（数据更新时间为 UTC 时间每日 0 点，来源：ARKHAM）

同时对 ETH 进行单独分析发现，Flow Traders 链上公开地址在 7 月 31 日大量增持了 ETH，但在 8 月 1 日至 8 月 2 日又将超 7000 枚 ETH 转入了交易所。

数据透视六大做市商“85”暴跌前后操作

Flow Traders ETH 链上公开地址持仓量变化（来源：ARKHAM）

数据透视六大做市商“85”暴跌前后操作

Flow Traders 在 8 月 1 日至 8 月 2 日将大量 ETH 转移至交易所

“ 85 ” 暴跌后（8.5-8.6）

而在“ 85 ”暴跌发生后，根据 ARKHAM 数据，Flow Traders 链上公开地址又开始大量增持 BTC，由 797.31 枚增持到 1650 枚，共增持 852.69 枚；同时还增持超 750 枚 MKR，而稳定币持仓仍无明显变化。详细数据如下图所示。

数据透视六大做市商“85”暴跌前后操作

Flow Traders 8 月 5 日至 8 月 6 日持仓量变化（数据更新时间为 UTC 时间每日 0 点，来源：ARKHAM）

小结

Flow Traders 似乎对暴跌“早有预感”，链上公开地址提前进行减持，特别是 ETH 前一天大量转入链上但马上后一天又大量转入了交易所，如果是抛售则是一次完美逃顶；同时暴跌后 Flow Traders 对 BTC 仍有信心，共增持超 800 多枚。

从链上的这些持仓变化数据来看，Flow Traders 在“ 85 ”大跌前后和 Jump 抛售时进行了 ETH 逃顶和 BTC 抄底操作，出色的交易使其成为此次行情跌宕的受益者之一。

DWF Labs：手拿山寨纹丝不动

“ 85 ” 暴跌前（8.1-8.5）

在“ 85 ”暴跌发生前，根据 ARKHAM 数据，DWF Labs 公开链上地址持仓多为山寨币，持有仓位最大的代币为 TRADE、GALA、DEXE 等。虽然资产净值被动贬值减少，但代币并未有明显流动，详细数据如下图所示。

数据透视六大做市商“85”暴跌前后操作

DWF Labs 8 月 1 日至 8 月 5 日持仓量变化（数据更新时间为 UTC 时间每日 0 点，来源：ARKHAM）

“ 85 ” 暴跌后（8.5-8.6）

而即使是在“ 85 ”暴跌发生后，根据 ARKHAM 数据，DWF Labs 公开链上地址持仓变动或者换仓也不大，详细数据如下图所示。

数据透视六大做市商“85”暴跌前后操作

DWF Labs 8 月 5 日至 8 月 6 日持仓量变化（数据更新时间为 UTC 时间每日 0 点，来源：ARKHAM）

同时对 ETH 的持仓变化进行单独分析发现，DWF Labs 的公开链上地址持有的 ETH 在暴跌前后持有量都无明显变化。

数据透视六大做市商“85”暴跌前后操作

DWF Labs ETH 链上公开地址持仓量变化（来源：ARKHAM）

小结

从以上持仓数据可看出，虽然币圈经历了大幅下跌，且其他做市商都在“操作”，但 DWF Labs 属于躺平派，满手山寨也依然不换稳定币等资产避险，这有可能是因为其对后市持乐观态度或对持有的山寨有足够信心。

Leituras Relacionadas

GPT-5.6 Countdown: Abandon the Illusion of a Single API, Computational Iteration Can't Outpace a Single Page of Compliance

In mid-June, three seemingly independent industry events—the compliance-driven throttling of Fable 5, the open-sourcing of GLM-5.2, and the leaked release timeline for GPT-5.6—are pushing the global AI industry toward a watershed moment. These shifts signal a fundamental restructuring of the industry's underlying logic. First, **"usability" has substantially overtaken "advanced capabilities"** as the primary weight, pushing the global large language model (LLM) supply chain into a "dual-track" phase of controlled closed-source and local open-source coexistence. Second, **the competitive moats of closed-source giants are shifting**. Their technical focus is moving from "language intelligence" toward "spatial intelligence (world models)"—a domain heavily reliant on computing power. Third, faced with常态化 transnational compliance risks, **a "model-agnostic" decoupled design has become a survival necessity for application-layer developers to maintain business continuity.** The article details how Anthropic's Fable 5, despite its advanced engineering feats, was restricted for non-U.S. citizens within 72 hours of launch, highlighting how geopolitical compliance can instantly limit even the most advanced models. In response, the open-source camp, exemplified by Zhipu AI's MIT-licensed GLM-5.2, is gaining market share by offering stable performance improvements and significant cost advantages (up to 70% savings for enterprises), while achieving full adaptation with domestic semiconductor platforms. Meanwhile, closed-source leaders like OpenAI are pivoting. The anticipated GPT-5.6 reportedly shifts focus from language to spatial intelligence and world models, aiming to rebuild a generational gap in areas like 3D understanding, simulation, and industrial design that demand immense compute. The core conclusion is that the LLM supply chain's logic has changed. Enterprises must now evaluate infrastructure based on a composite of technical performance and policy compliance. For developers, complete reliance on a single closed-source API poses unacceptable risk. Implementing a truly model-agnostic architecture—enabling swift switches to compliant, locally deployable open-source alternatives—is no longer just good practice but a fundamental baseline for business continuity.

marsbitHá 23m

GPT-5.6 Countdown: Abandon the Illusion of a Single API, Computational Iteration Can't Outpace a Single Page of Compliance

marsbitHá 23m

Is the 'Token Subsidy War' Among AI Giants Almost Over?

The article discusses the ongoing "token subsidy war" among AI giants like OpenAI and Anthropic, questioning whether it's nearing its end. It reveals that current AI subscription prices are heavily subsidized, with some plans offering tokens at up to 70 times the actual cost to attract and retain heavy users, especially developers and enterprises. This strategy mirrors past internet-era subsidy battles, but with a key difference: AI tokens lack "lock-in" effects. Unlike ride-hailing or food delivery apps, users can easily switch between AI providers as APIs become standardized, making it difficult for companies to raise prices post-subsidy. The piece highlights a structural asymmetry in the competition. Giants like Google, with massive advertising revenue, can afford to subsidize tokens indefinitely, akin to using "tokens as a weapon." In contrast, venture-backed companies like OpenAI and Anthropic face pressure to become profitable, especially as they approach IPO. The article cites Google Ventures founder Bill Maris, who suggests Google could slash token prices by 80%, putting immense pressure on competitors. Two potential endgames are presented: the "internet service" model (subsidize, monopolize, then raise prices) and the "utility" model (tokens become a standardized, low-margin commodity like electricity). Given the low switching costs, the latter seems more likely. The competition may not have a single winner but could instead accelerate AI's evolution into a foundational, infrastructure-level technology, akin to a public utility. For now, users continue to benefit from heavily subsidized token costs.

marsbitHá 40m

Is the 'Token Subsidy War' Among AI Giants Almost Over?

marsbitHá 40m

Beyond the Stadium: The Profitable Games Surrounding the World Cup

"Beyond the Pitch: The Profit Game Around the World Cup" The FIFA World Cup transcends being a sporting spectacle, evolving into a massive global arena for speculation and profit-seeking. The 2026 tournament has amplified this dynamic, creating a multi-layered ecosystem of financial opportunism alongside the football. **Prediction markets** have surged into the mainstream. Platforms like Polymarket and Kalshi saw trading volumes for World Cup contracts soar, attracting new users with their financial trading model and high-profile, chain-based wealth stories that overshadow traditional sports betting in terms of growth and narrative. However, **traditional sportsbooks** remain the dominant force, leveraging established user habits, legal markets, and comprehensive product offerings to handle the vast majority of speculative wagers, with projections suggesting record-breaking betting volumes. Capital markets also react. **"Concept stocks"** in countries like South Korea and Japan experience volatile price swings based on team performance and anticipated fan spending on items like chicken, beer, and viewing parties, effectively becoming a stock market reflecting fan sentiment. The **ticket resale market** has become a sophisticated arena for arbitrage. Prices fluctuate wildly based on team draws and star power, with sellers sometimes listing tickets they don't yet own in a practice akin to short-selling, while FIFA's own "Right to Buy" tokens add another layer of speculative trading. **Collectibles and merchandise** offer another avenue. Panini sticker albums, with their inherent scarcity and nostalgic value, can become high-value collectibles. Limited-edition or locally themed jerseys command significant premiums on secondary markets, and even counterfeit vendors profit from fans' desire for affordable match-day identity. The **cryptocurrency** space has seen a frenzy of speculative, unauthorized World Cup-themed meme coins on chains like Solana. These tokens, often exploiting team names and player imagery, experience extreme pump-and-dump cycles, creating stories of massive gains for a few early entrants and steep losses for many others. Finally, an entire industry thrives on **providing information and tools** to other speculators. Developers create platforms like SeatSidekick to track ticket inventory and prices, while paid Telegram groups and subscriptions sell betting tips and predictions, monetizing the widespread desire for an informational edge. In essence, the World Cup has become a compressed, global laboratory for speculation. While the games determine champions on the field, a parallel, complex network of financial transactions—spanning prediction contracts, bets, stocks, tickets, collectibles, crypto, and information services—settles its own scores in the global market.

marsbitHá 1h

Beyond the Stadium: The Profitable Games Surrounding the World Cup

marsbitHá 1h

Hyperliquid ETF Claim Draws Attention As HYPE Narrative Builds On X

A social media claim has brought attention to Hyperliquid (HYPE) and the growth of its related ETF products. According to a June 20 post on X by AlphaOnChain, three Hyperliquid ETFs launched in May 2026 have collectively amassed $158 million in assets under management, led by a Bitwise fund with $88 million and a 21Shares fund with $66 million. The article notes this claim is from social media and not verified by official issuer data, advising caution. Nonetheless, the report highlights that HYPE is gaining traction as a notable altcoin narrative. Its focus on on-chain perpetual trading and exchange infrastructure places it at the intersection of DeFi and derivatives, attracting traders looking for high-conviction plays beyond Bitcoin and Ethereum. The key takeaway is that while social momentum can influence short-term markets, sustainable growth for HYPE would require confirmed demand, liquidity, and continued ecosystem development.

bitcoinistHá 2h

Hyperliquid ETF Claim Draws Attention As HYPE Narrative Builds On X

bitcoinistHá 2h

How Does Codex Use a Computer? Three Entry Points and Permission Boundaries

This article explains the three primary methods for Codex to interact with a computer, each with distinct use cases, permission boundaries, and trust levels. **1. Computer Use:** This offers the broadest access, allowing Codex to visually control and interact with the graphical user interface of authorized macOS/Windows apps, system settings, and even iOS simulators. It's ideal for tasks lacking APIs or structured tools, such as operating legacy software or multi-app workflows. However, it's the slowest method and has the widest permission scope, requiring careful supervision for sensitive actions. **2. Chrome Extension:** This grants Codex access to the user's logged-in Chrome browser state, including cookies, profiles, and open tabs. It's best for tasks requiring user identity across websites like Gmail, LinkedIn, Salesforce, or internal dashboards. Its key advantage is multi-tab control for complex workflows. While more powerful for browser-based tasks than Computer Use, it carries higher sensitivity as actions are performed under the user's identity. **3. In-App Browser:** This is a browser isolated within the Codex thread, separate from the user's personal browsing data. It excels in web development and debugging scenarios—previewing local servers, testing responsive layouts, or annotating designs directly on the page. Its isolation is a strength for development but a limitation for tasks requiring login sessions. The core principle is to choose the narrowest, safest, and most structured interface for the task. Use plugins or MCPs first, resort to visual control (Computer Use) only for GUI-dependent tasks, employ the Chrome extension for identity-reliant browser work, and prefer the In-App Browser for isolated development. **Appshots** are clarified as a fourth, complementary tool for *inputting* context—capturing a screenshot of a window to point Codex to something—rather than a method for Codex to *act*. Together, this layered approach highlights a key to AI agent productization: not granting unlimited permissions, but constraining them within clear boundaries for specific tasks while preserving user oversight.

marsbitHá 2h

How Does Codex Use a Computer? Three Entry Points and Permission Boundaries

marsbitHá 2h

Trading

Spot
Futuros
活动图片

Categorias popularesright-arrow

Etiquetas Popularesright-arrow