曾红极一时的TG Trading Bot赛道如今表现如何？

Odaily星球日报Published on 2023-11-09Last updated on 2023-11-09

Abstract

原先占据领先地位的 Maestro、Unibot 和 Banana Gun 各自发展如何？

原文作者：dt，DODO Research

原文编辑：Lisa

作为今年度少数热点赛道之一，曾经红极一时的 TG Trading BOT 赛道如今表现如何呢？今天 Dr. DODO 将带大家一探究竟，让我们以 Binance Research 研报 的发表日 8 月 17 日作为一个时间点，比较下至今这两个半月内 TG Trading Bot 赛道版图有什么变化？原先占据领先地位的 Maestro、Unibot 和 Banana Gun 各自发展如何？

数据比较

就整个赛道整体数据而言，不管是在交易量、Tx 数或是使用者方面，TG Trading Bot 赛道相较于暑假的数据有明显的回落。交易量从最顶峰每日接近 30 M 美金的交易量跌落至今大约 10 M，Tx 数则从每日最高 80 K 下调至 40 K，而使用者数则是回调最少的数据，每日平均用户仍维持在 10 K 左右。

曾红极一时的TG Trading Bot赛道如今表现如何？

每日交易量数据 Source: https://dune.com/whale_hunter/dex-trading-bot-wars

曾红极一时的TG Trading Bot赛道如今表现如何？

每日 Tx 数数据 Source: https://dune.com/whale_hunter/dex-trading-bot-wars

曾红极一时的TG Trading Bot赛道如今表现如何？

每日使用者数据 Source: https://dune.com/whale_hunter/dex-trading-bot-wars

接着就 TG Trading Bot 市场版图来看，Maestro 仍然稳居第一，占据超过一半的使用者数。Banana Gun 则是不断稳定成长，在发币后数据更是有着明显的提升，在九月初便成功反超 Unibot 登上第二名，目前使用者数约占 35 ％。而 Unibot 在币价 ATH 之后数据不断下跌，使用者数更是跌破 10% 。

曾红极一时的TG Trading Bot赛道如今表现如何？

Source: https://dune.com/whale_hunter/dex-trading-bot-wars

Maestro 凭藉着长久以来的社区支持仍然是这个赛道的领先者，不过已经从市场上绝对的领先者接近 70% 市占率逐渐下滑了至快跌破 50% 。

Banana Gun 前期靠着空投预期吸引到不少新用户，并靠着优质的 MEV Bribe Snipe 代币狙击功能以及比其他竞品更低的 0.4% 服务手续费巩固用户群。产品也不断推陈出新，最近推出的具有 Bribe 功能的限价单也让许多用户获利颇丰，备受好评。Banana Gun 在 MEV Bribe 狙击新币的功能上占据领先地位，过去十天 Banana Gun 用户贿赂 MEV Builder 的数据已超过 1700 E，远高于 Maestro 的 65 E，Unibot 则迟迟没有这项功能。

曾红极一时的TG Trading Bot赛道如今表现如何？

Source: https://dune.com/queries/3056051

相较于前两者，Unibot 则是在产品上改进不多，随着代币下跌广告效应也不在，产品更新上踏足 FriendTech Bot 更是一大败笔，日均使用者人数不到 10 位。在主要业务技术落后的情况下去分新增加业务，让人有一种项目方炒币为主产品其次的感觉。

曾红极一时的TG Trading Bot赛道如今表现如何？

Source: https://dune.com/whale_hunter/unibot-revenue

安全性

尽管 TG Trading Bot 赛道的项目从来不以安全性吹嘘，其目标用户本就是风险系数较高的 Degen 族群，但近来发生的两起黑客攻击事件同样带来不少打击。

10 月 24 日，Maestro 更新了其路由合约，但此合约因智能合约代码漏洞被黑客攻击导致用户损失超过 280 ETH。但后续，Maestro 项目方展现高度诚意，在修复漏洞后总偿还将近 610 ETH，获得了社区的高度称赞。

而不到一周过后， 10 月 31 日，Unibot 同样因更新路由合约出现代码漏洞导致黑客攻击，用户损失约 355 ETH。其代币 $Unibot 也应声下跌 50% ，目前据 Unibot 称已全数赔偿用户损失，代币价格也回升至事件前水平。

TG Trading Bot 向来是以牺牲安全度获得便捷度为主的一种替代方案，Not Your Key Not Your Money 用户在使用这类应用时切记注意风险，以小额投入为主。

后起之秀

管目前 TG Trading Bot 赛道市场上前三的项目占据接近 90% 的份额，但仍有不少后起之秀项目积极挑战，其中更是不乏有知名背景的项目方。

PepeBoost：Pepeboost 以中文社区为主要对象，社区带单炒币的形式吸引到不少使用者，目前在市占排行上暂居第四名，据项目方宣称其跟单交易功能更是超越 Maestro。

Shuriken：Shuriken 的特色在于其除了 TG Bot 之外，网页端的 Dapp 提供用户许多便捷的看板功能，提供用户监控聪明地址或是热门代币等等功能，并且推出积分徽章吸引不少空投猎人前来使用。

Alfred：Alfred 则是目前最多人提及的 TG Bot 项目，原因并不是因为其产品有特别突出的特色，而是其背景惊人。Alfred 团队由 Flashbots 联创 Stephane Gosselin 实名创办，近来发生的多起 TG Bot 项目被黑客攻击事件，让人对于安全性的疑虑大大提高。实名且具有高评价背景的 Alfred 便获得许多人亲睐，而其也同样推出积分机制，让不少空投猎人也来参与使用。

笔者观点

TG BOT 领域一直都是笔者重点关注且非常感兴趣的一个赛道。在笔者看来，区块链要普及化这种方便使用的应用是必然的过程之一。

尽管目前观察下来，数据相较于热度当头时有相当幅度的下跌，但整体而言仍然相当健康。数据回调很可能与市场走势有关，整体大盘回暖导致链上 Gas Fee 提高，并且对应到的是链上 Memecoin 发财效应不在，链上 Degen 用户相当程度转移 BTC 衍生的应用。而回到 TG BOT 赛道，不仅龙头项目仍在不断更新，且还有几个新项目的加入，其中更是有 Flashbot 创办人的参与，代表着不仅仅 Degen 用户在注意正规具有背景的开发者们也积极的重视。

Related Reads

Bitcoin Liquidation Shakeout Leaves Traders Watching $66K Resistance And $61K Support

Bitcoin's recent volatility triggered a major liquidation event, wiping out roughly $980 million in leveraged positions within 24 hours as price swung from ~$64,100 down to $60,700 and back above $63,000. Traders are now watching key technical levels. Major pivot resistance is identified at $66,247, with reclaimed highs near $64,234 and next resistance at $65,890. Critical support zones lie between $63,127-$63,354, then $62,459, with broader downside targets around $59,150-$60,171. Liquidation heatmaps show significant liquidity clusters above ($63.5K-$66K) and below ($58.5K-$61K) spot price, creating a volatile corridor. The market is in a corrective phase; a clean break above resistance is needed for a bullish shift, while failure to hold low-$63K support risks a move toward lower liquidity zones.

bitcoinist5m ago

Bitcoin Liquidation Shakeout Leaves Traders Watching $66K Resistance And $61K Support

bitcoinist5m ago

U.S. Government Bans Foreign Nationals from Using Fable 5, Anthropic Issues Rebuttal

U.S. Government Bans Foreign Access to Fable 5, Anthropic Issues Rebuttal On June 12th, the U.S. government ordered AI company Anthropic to immediately suspend all foreign access—including foreign nationals within the U.S. and Anthropic's own foreign employees—to its newly released Fable 5 and Mythos 5 AI models, citing national security concerns. This forced Anthropic to temporarily disable access to both models for all users globally, as it cannot technically differentiate user nationality at scale. The models, released just three days prior, represent Anthropic's highest public capability tier. Fable 5 is the first publicly available model from the advanced "Mythos" family, while Mythos 5 is a less-restricted version for approved cybersecurity and critical infrastructure partners. The government's directive was reportedly triggered by claims from another company that it could "jailbreak" Mythos 5, raising alarm within the Trump administration. Anthropic, in a detailed public statement, strongly challenged this rationale. The company argues the demonstrated "jailbreak" is a narrow, non-generalized technique that merely involves identifying minor, known software vulnerabilities—a capability common to other publicly available models like OpenAI's GPT-5.5 and routinely used by cybersecurity defenders. Anthropic stated it has complied with the order but disagrees with the government's standard, warning that applying it industry-wide would halt all new frontier model deployments. The company criticized the lack of a transparent, fact-based legal process and expressed confidence the situation stems from a misunderstanding. It is working to restore access and will release more technical details within 24 hours. Other Anthropic models remain unaffected.

链捕手7m ago

U.S. Government Bans Foreign Nationals from Using Fable 5, Anthropic Issues Rebuttal

链捕手7m ago

The Revelation from the Raydium Theft Incident: New DeFi Vulnerabilities Lurking in Forgotten Old Contracts

**Raydium Exploit Reveals DeFi's Hidden Risk: Forgotten "Zombie" Contracts** A recent attack on Raydium's deprecated V3 AMM pools resulted in a loss of approximately $1.34 million. The hacker exploited pools that were no longer supported by Raydium's current UI or SDK but remained fully functional and accessible on-chain. This incident highlights a critical, often overlooked category of risk in DeFi: inactive or legacy smart contracts that projects fail to properly decommission. Since March 2025, there have been at least 8 publicly reported attacks targeting such abandoned contracts, with total losses around $10.8 million. Including older pools and deprecated features, the count rises to 10 incidents with roughly $22.5 million in losses. These "zombie contracts" represent a lifecycle management failure rather than a code vulnerability, yet they are typically misclassified under general "code bug" categories in security reports, masking the true scale of the problem. The root cause is that projects often merely document a contract as "deprecated" without taking essential technical steps to secure it: withdrawing remaining assets, disabling external call functions, and implementing ongoing monitoring. These forgotten, under-monitored components become prime targets for attackers. To address this, the industry needs to recognize "zombie contracts" as a distinct risk category and establish standardized decommissioning protocols. Essential steps should include: 1) a formal retirement announcement, 2) removal of all front-end integrations, 3) withdrawal of locked assets, 4) disabling key contract functions, 5) ongoing security monitoring, 6) clear user communication, and 7) a post-mortem analysis. The value of a DeFi project lies not only in its current TVL but also in the security of its historical codebase, which has now become a new attack surface.

Foresight News1h ago

The Revelation from the Raydium Theft Incident: New DeFi Vulnerabilities Lurking in Forgotten Old Contracts

Foresight News1h ago

Solayer Launches Margin Trade to Consolidate Perpetual Multi-Asset Trading on Mainnet

Solayer, a high-performance SVM-compatible Layer 1 blockchain, has launched its Margin Trade platform on mainnet. This unified cross-asset perpetual trading platform merges traditional and digital finance, developed with input from institutional and crypto trading experts. It offers trading for a diverse range of assets, including top cryptocurrencies, commodities like gold and oil, and equities such as the synthetic MT500 index. Key features include cross-margin trading with a unified collateral pool for improved capital efficiency, real-time execution, and full on-chain transparency with non-custodial asset ownership. The launch includes the first perpetual market for Pearl Research ($PRL) with up to 3x leverage. Future plans involve expanding asset listings and adding isolated margin functionality. Built on the high-throughput Solayer network capable of 330,000 TPS, the platform aims to provide the speed required for serious trading. Following successful testnet phases, Margin Trade's mainnet debut marks a significant step in Solayer's goal to unify traditional and crypto markets on-chain with institutional-grade decentralized finance infrastructure.

TheNewsCrypto2h ago

Solayer Launches Margin Trade to Consolidate Perpetual Multi-Asset Trading on Mainnet

TheNewsCrypto2h ago

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

Robots have started to 'consume data,' driving the formation of a new industrial supply chain focused on producing training data for embodied AI. Unlike large language models, which are trained on vast internet text corpora, embodied AI models face a 'data desert' in the physical world. This has created a massive demand for first-person perspective video data (Ego Data), captured by workers wearing cameras in places like Indian garment factories. Companies like Neocambrian AI are establishing 'data factories' where workers perform standardized tasks (e.g., sorting clothes, kitchen organization) to generate thousands of hours of video. Research, such as NVIDIA's EgoScale, demonstrates that scaling this human demonstration data predictably improves robot performance, particularly for dexterous manipulation. This has validated a training path combining large-scale human data for pre-training with smaller amounts of robot-specific data for fine-tuning. The value of different data types varies significantly, forming a 'data pyramid.' The base consists of low-cost, large-scale internet and Ego Data. Higher layers include more expensive motion-capture data (e.g., from data gloves), simulation/synthetic data, and the most costly and scarce layer: real robot teleoperation data. This demand has spawned a layered ecosystem of data suppliers: low-cost data factories, motion capture and alignment specialists, robot-native teleoperation service providers, simulation data companies, and platforms aiming for data standardization. Robot companies themselves are adopting a 'layered procurement' strategy: outsourcing generic Ego Data while building in-house capabilities for robot-specific adaptation data and the critical deployment/failure data generated in real-world applications. The industry is shifting focus from hardware and basic mobility to the data pipelines required for general-purpose capability. While parallels exist to data labeling companies like Scale AI in the LLM boom, the physical complexity of robot data—involving action success ambiguity and sim-to-real gaps—requires more integrated solutions for data collection, annotation, and a continuous feedback loop. The race is on to build the data engines that will teach robots to operate reliably in the unstructured real world.

marsbit4h ago

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

marsbit4h ago

Trading

Spot
Futures
活动图片

Hot Categoriesright-arrow

Hot Tagsright-arrow