符文市值新高,但为什么我和朋友们在亏钱?

Odaily星球日报Publicado a 2024-06-04Actualizado a 2024-06-04

Resumen

马太效应加剧,买新的不如买大的。

原创 | Odaily星球日报

作者 | Golem

符文市值新高,但为什么我和朋友们在亏钱?

今日符文总体市值创下新高,达到 19.2 亿美元,伴随着近期符文行情回暖,链上符文打新也越来越活跃,许多玩家开始关注一级市场以期打到符文金狗。但实际上的情况可能会与想象中的不同,纵使一级打新市场繁荣,但二级市场却少有人接盘新资产,而大部分资金都集中在头部几个符文项目。

下面,Odaily 星球日报将从数据角度揭示符文赛道目前的总体状况、打新资产的表现及由此得出的策略建议。

马太效应,强者愈强

根据 Geniidata 数据显示,从市值角度,符文市值前三的 DOG•GO•TO•THE•MOON、RSIC•GENESIS•RUNE 和 PUPS•WORLD•PEACE 市值分别约为 9.2 亿美元、 2.3 亿美元和 1.6 亿美元,共占符文赛道总市值的 70% 。

从 24 H 交易量角度来说,截止目前符文 24 H 总交易量约为 1458 万美元,而市值第一排名的符文 DOG•GO•TO•THE•MOON 24 H 总交易量约为 764 万美元,占符文 24 H 总交易量的 50% 以上。

符文市值新高,但为什么我和朋友们在亏钱?

综上,尽管符文市值已有 19.2 亿美元,已部署的符文数量达到 72764 个,但并没有出现赛道全面开花的盛况,反而是马太效应加剧——头部的符文项目愈来愈强,占据着场内大部分资金和流动性,并没有新的符文资产有比龙头们表现更为亮眼,来打破格局。

符文打新呢?表现也不尽人意

当比特币生态某协议龙头代币领涨时,必然会增加用户链上打新的热情,例如去年 6 月份 ORDI 领涨开启了 BRC 20 热,并且因为行情复苏新用户与资金进场,其中也不乏出现许多打完不久就翻上几倍的情况。

Runestone 空投的符文 DOG•GO•TO•THE•MOON 目前无疑是 Runes 协议中的龙头代币,近期代币价格的不断上涨自然激发了用户链上打新符文资产的热情。但本轮符文的打新热潮却与 BRC 20 不太一样,链上的打新资产表现不太尽人意?下表中记录了过去 7 天中按铸造费用排名的前 10 个符文的收益情况。

符文市值新高,但为什么我和朋友们在亏钱?

参考制图:@satosea_xyz

综合可以看出,在过去 7 天中铸造费用前 10 的符文只有 3 个具有较不错的打新回报,而其他 7 个表现均不佳,甚至出现超 70% 的跌幅。

再者,从资产流动性的角度来说,如下图 Geniidata 数据所示,过去 30 天比特币链上铸造符文的交易都远超买卖和转移符文的交易,这进一步说明大部分的符文新资产在铸造完成后实际上都面临着流动性困境,二级没有资产来接盘,场内资金 PVP 的现象很严重。

符文市值新高,但为什么我和朋友们在亏钱?

策略:买新的还不如买大的

在过去一周中,虽然头部符文的市值已经过亿,但是也有不错的涨幅,根据 CoinGecko 数据显示,过去 7 天 DOG•GO•TO•THE•MOON 涨幅为 118.6% ,RSIC•GENESIS•RUNE 涨幅为 55.9% 。

符文市值新高,但为什么我和朋友们在亏钱?

虽然在符文链上一级打新的赔率可能会很高,但是如果没有敏锐的洞察力、强大的分析能力和一直紧盯链上动态的精力是很难从众多新项目中选出那 30% 的金狗。况且正如前文分析,符文赛道目前还是场内资金 PVP 居多,即使新玩家和资金入场优先考虑的也可能是符文头部项目,而不是链上体量小、社区发展时间短的新资产。

综上所述,与其耗尽心力打新资产,还不如直接买市值大、共识强的头部资产。

Lecturas Relacionadas

Just Now, Anthropic Unveils Physical MCP: Claude Begins Controlling the Real World

Anthropic has announced the Model Hardware Standard (MHS), a new standard enabling AI agents like Claude to safely control physical devices. Building on the Model Context Protocol (MCP), MHS standardizes communication between AI agents and hardware such as microscopes, robotic arms, and lasers, marking a significant step for AI from the digital into the physical world. Developed in collaboration with HHMI Janelia Research Campus, MHS uses standardized drivers to translate basic commands (e.g., read, write) into a format any programmable device can understand. This drastically reduces integration time from weeks to hours or minutes and allows agents to discover and operate new devices using natural language tags that describe machine properties and safety limits. Agents can control devices via MCP, command-line interfaces, or APIs. They can sequence operations, monitor results, adjust parameters in real-time, and generate deterministic scripts for long-running tasks. Early tests show Claude interacting with hardware exploratively, like a scientist, learning to calibrate a laser and scripting the process. Early adopters and partners include AWS, Automata, Danaher, Doosan Robotics, and Tecan, who are integrating MHS support into their platforms. While promising, challenges remain: Claude's physical reasoning is limited, requiring expert oversight, and MHS currently only works with programmable hardware. Anthropic plans further refinements and broader device support before open-sourcing the standard.

marsbitHace 2 min(s)

Just Now, Anthropic Unveils Physical MCP: Claude Begins Controlling the Real World

marsbitHace 2 min(s)

History's Only Asset with a 100% Win Rate After 4 Years of Holding

**Title: The Only Asset with a 100% Win Rate Over Any 4-Year Holding Period** This article analyzes which major, freely-tradable assets have historically never produced a nominal loss over any rolling 4-year holding window. It concludes that only two distinct categories achieve this: ultra-low-risk contractual assets and Bitcoin. Among traditional risk assets, none maintain a perfect 4-year record. The S&P 500 had negative 4-year periods (e.g., 1929-1932: -64.8%). The Nasdaq 100 fell roughly 60% from 2000-2003. Gold saw a ~47.7% loss from 1981-1984. US real estate declined about 23.3% from 2007-2010. Even long-term US Treasury bonds (e.g., 2021-2024: -19.8%) and corporate bonds can produce 4-year losses due to interest rate and market price risks. In contrast, the first category achieving 100% nominal success includes assets like rolling 3-month US Treasury Bills, 4-year certificates of deposit (CDs), and US Treasuries held to maturity within 4 years. Their "guarantee" stems from contractual obligations and credit backing (e.g., FDIC insurance, US sovereign promise), not price appreciation. The sole exception in the high-risk category is Bitcoin. Analysis of daily data from 2010-2026 across 4,419 rolling 4-year windows shows a 100% positive return rate. The worst 4-year period (April 2021 to April 2025) still yielded a +32.6% total return (~7.3% CAGR). This record is unique because Bitcoin has no issuer, promises no cash flows, and has endured severe drawdowns (70-90%), yet its market price has always recovered within a 4-year span. The key distinction is the source of the "100%": contractual assets offer known, low nominal returns, while Bitcoin's record stems purely from historical price appreciation despite extreme volatility. The article suggests that for Bitcoin, the ability to hold for 4+ years is more critical than active trading strategies.

marsbitHace 9 min(s)

History's Only Asset with a 100% Win Rate After 4 Years of Holding

marsbitHace 9 min(s)

How One Article Moved 45 Billion: The Collapse of a 25-Year-Old 'AI Stock Guru'

This article details the dramatic rise and near-collapse of a hedge fund built by Leopold Aschenbrenner, a 24-year-old former OpenAI researcher. The fund, named Situational Awareness, amassed $45 billion in assets within two years. Its explosive growth stemmed from Aschenbrenner's influential 165-page manifesto predicting AGI's arrival by 2027 and his high-profile Silicon Valley connections. The fund employed an extremely aggressive strategy: high concentration and 400% leverage to bet long on AI infrastructure stocks while shorting legacy software firms. In July, this structure backfired when both sides of the trade reversed simultaneously—AI stocks plunged while shorted stocks rallied—triggering massive losses that nearly wiped out all equity. Major player Jane Street reportedly lost billions. The fund's leveraged public portfolio was ultimately sold at a discount to Citadel. The SEC is now investigating banks like Goldman Sachs for their role in facilitating the fund's high-leverage trades. The article compares this to past blow-ups like Archegos, highlighting systemic failures in risk management where the pursuit of short-term profits overrode due diligence. It questions whether such risky leverage concentrated in the AI sector, currently at record highs, poses a broader systemic threat. Ironically, Aschenbrenner, who studied AI safety at OpenAI, designed a fund structure prone to uncontrolled failure. Days after the crisis, he reportedly raised another $400 million for new investments.

marsbitHace 23 min(s)

How One Article Moved 45 Billion: The Collapse of a 25-Year-Old 'AI Stock Guru'

marsbitHace 23 min(s)

AI Accelerates Everything: Mathematics's Line of Defense Has Fallen, Physics is Already in AI's Crosshairs

This summer, the mathematics community was shaken as OpenAI's Astra model reportedly solved 10 long-standing open problems, and Claude Fable 5 found a potential counterexample to the Jacobian conjecture. This was followed by a major shift in physics: renowned physicist Gavin E. Crooks presented an open problem in stochastic thermodynamics to Claude, which the AI solved completely in days—a task that might take a skilled graduate student months. The problem concerned constraints on entropy production statistics under the Detailed Fluctuation Theorem (DFT), a core concept in non-equilibrium physics. Claude provided a unifying geometric answer: all possible DFT-compatible distributions correspond to a convex "moment body." Its key insight was that for a fixed "gap," the distribution is uniquely determined, making any general DFT distribution a mixture of these basic two-outcome distributions. This structure implies that for given lower-order moments, the nth moment only has a sharp lower bound, with no upper bound. Claude demonstrated that numerous previously published bounds on entropy production are merely low-dimensional projections or "shadows" of this single, unified convex body. The AI-authored paper offers a complete hierarchical characterization of the moment constraints. This case signifies a potential paradigm shift: AI is progressing from solving known problems to aiding in the exploration of fundamental, unsolved scientific questions, heralding an AI-accelerated era for physics.

marsbitHace 23 min(s)

AI Accelerates Everything: Mathematics's Line of Defense Has Fallen, Physics is Already in AI's Crosshairs

marsbitHace 23 min(s)

Trading

Spot
活动图片