Celebrity & Social Media Trader
This is a template.
The default signal is keyword-based market discovery combined with conviction-based sizing and celebrity_bias() — remix it with the data sources listed below.
The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.
Strategy Overview
Celebrity markets look chaotic but split cleanly into two camps: data-trackable (subscriber growth rates, tweet cadence, athletic records) and emotionally-driven (fan loyalty, feuds, relationships). The data-trackable ones have genuine structural edges. The emotional ones are traps for anyone who doesn't understand that retail here is trading feelings, not probability.
The single most important insight in this domain: megastar fan bases are the worst market makers on Polymarket. They overcrowd YES on anything involving their idol, creating systematic NO edges that the bias function captures.
Signal Logic
Default Signal: Conviction-Based Sizing with Celebrity Bias
- 1. Discover active celebrity and social media markets on Polymarket
- Compute base conviction from distance to threshold (0% at boundary → 100% at p=0/p=1)
- Apply
celebrity_bias() — combines market type predictability with weekend repricing lag - Size =
max(MIN_TRADE, conviction × bias × MAX_POSITION) — capped at MAXPOSITION - Skip markets with spread > MAXSPREAD or fewer than MIN_DAYS to resolution
Celebrity Bias (built-in, no API required)
Two structural edges:
Factor 1 — Market Type Predictability
Celebrity markets are NOT uniformly unpredictable. The key is distinguishing what has data behind it:
| Market type | Multiplier | Why |
|---|
| Subscriber / follower / view milestones | 1.25x | Social Blade publishes daily growth data — "will MrBeast reach 400M by X" is calculable from the trend; retail never checks |
| Elon Musk / high-volume poster tweet count |
1.20x | Documented consistent cadence (~350–400 posts/week); markets misprice on vibes not data |
| Boxing / MMA / combat sport outcome |
1.15x | Athletic records, training camp signals, fight history exist; retail prices fame not skill |
| Reality TV voting outcome |
1.10x | Nielsen Social Content Ratings track engagement correlating with votes |
| Awards (Oscars, Grammys, Emmy, BAFTA) |
0.85x | Efficiently priced by dedicated awards-circuit followers — less edge |
| Celebrity relationship / breakup / divorce |
0.80x | Tabloid-narrative noise — rival fanbases overprice both directions |
| Celebrity beef / feud reconciliation |
0.75x | Fans desperately want YES; historical reconciliation base rate is low — fade hard |
| Megastar fan-favourite markets (Taylor Swift, Beyoncé, BTS, etc.) |
0.75x | Fan loyalty is the dominant pricing force — not information. This is the most dangerous overcrowded trade in the category |
Factor 2 — Weekend Repricing Lag
Social media metrics (subscribers, streams, tweet counts, chart positions) update continuously. But Polymarket market makers are least active Friday evening through Sunday. Social media activity peaks on weekends. The gap between real-world metric movement and market repricing is widest on weekends.
| Condition | Multiplier |
|---|
| Metric-based question (subscribers, streams, tweets, charts) + Friday–Sunday | 1.15x |
| All other combinations |
1.00x |
Combined and capped at 1.35x — intentionally lower cap than data-driven domains (crypto capped at 1.40x) because celebrity markets have inherently higher narrative noise. A subscriber milestone question on a Saturday → 1.25 × 1.15 = 1.35x cap. A Taylor Swift fan-favourite market at any time → 0.75x — trade near the MIN_TRADE floor.
The Fan Loyalty Trap — Why 0.75x for Megastars
This deserves its own section. Markets involving Taylor Swift, Beyoncé, BTS, and similar artists are structurally mispriced by their fanbases in a predictable direction: always too bullish on positive outcomes. "Will Taylor Swift announce a world tour?" gets bid to 70% when the base rate for any artist is 20%. "Will BTS reunite before X?" gets bid to 65% by fans who are pricing their wish, not the probability.
The 0.75x dampener does not mean avoid these markets — it means size down and be especially alert to NO signals when p ≥ NO_THRESHOLD, because that is where the fan-bid overcrowding creates the most exploitable gap.
Keywords Monitored
CODEBLOCK0
Remix Signal Ideas
- - Social Blade API: Daily subscriber growth rates for YouTube/TikTok/Twitch — compare published trajectory to Polymarket milestone probability, trade the divergence
- Spotify Charts API: Real-time streaming data for "will artist reach X streams" markets
- Nielsen Social Content Ratings: Social engagement scores correlating with reality TV votes
- X/Twitter API v2: Elon's actual posting cadence — compare to market's implied tweet count probability
Safety & Execution Mode
The skill defaults to paper trading (venue="sim"). Real trades only with --live flag.
| Scenario | Mode | Financial risk |
|---|
| INLINECODE5 | Paper (sim) | None |
| Cron / automaton |
Paper (sim) | None |
|
python trader.py --live | Live (polymarket) | Real USDC |
INLINECODE7 and cron: null — nothing runs automatically until you configure it in Simmer UI.
Required Credentials
| Variable | Required | Notes |
|---|
| INLINECODE9 | Yes | Trading authority. Treat as high-value credential. |
Tunables (Risk Parameters)
All declared as tunables in clawhub.json and adjustable from the Simmer UI.
| Variable | Default | Purpose |
|---|
| INLINECODE12 | INLINECODE13 | Max USDC per trade — lower ceiling reflects domain noise |
| INLINECODE14 |
3000 | Min market volume filter (USD) |
|
SIMMER_MAX_SPREAD |
0.12 | Max bid-ask spread (12%) — wider tolerance for niche markets |
|
SIMMER_MIN_DAYS |
3 | Min days until resolution |
|
SIMMER_MAX_POSITIONS |
10 | Max concurrent open positions |
|
SIMMER_YES_THRESHOLD |
0.38 | Buy YES if market price ≤ this value |
|
SIMMER_NO_THRESHOLD |
0.62 | Sell NO if market price ≥ this value |
|
SIMMER_MIN_TRADE |
5 | Floor for any trade (min USDC regardless of conviction) |
Dependency
INLINECODE28 by Simmer Markets (SpartanLabsXyz)
- - PyPI: https://pypi.org/project/simmer-sdk/
- GitHub: https://github.com/SpartanLabsXyz/simmer-sdk
名人及社交媒体交易员
这是一个模板。
默认信号是基于关键词的市场发现,结合基于信念的仓位规模和celebrity_bias()——请使用下方列出的数据源对其进行重新组合。
该技能处理所有底层工作(市场发现、交易执行、安全防护)。您的智能体提供阿尔法收益。
策略概述
名人市场看似混乱,但可以清晰地分为两类:数据可追踪型(订阅者增长率、推文节奏、运动记录)和情感驱动型(粉丝忠诚度、恩怨、恋情)。数据可追踪型具有真正的结构性优势。情感驱动型则是为那些不理解散户在此交易的是感受而非概率的人设下的陷阱。
该领域最重要的见解是:巨星粉丝群是Polymarket上最差劲的做市商。他们会过度买入任何涉及偶像的YES选项,从而创造出偏差函数能够捕捉到的系统性NO优势。
信号逻辑
默认信号:基于信念的仓位规模与名人偏差
- 1. 发现Polymarket上活跃的名人和社交媒体市场
- 根据与阈值的距离计算基础信念(边界处为0% → p=0/p=1时为100%)
- 应用celebritybias()——结合市场类型可预测性与周末重新定价滞后
- 仓位规模 = max(MINTRADE, conviction × bias × MAXPOSITION)——上限为MAXPOSITION
- 跳过价差大于MAXSPREAD或距离结算少于MINDAYS的市场
名人偏差(内置,无需API)
两种结构性优势:
因素1——市场类型可预测性
名人市场并非全都不可预测。关键在于区分哪些有数据支撑:
| 市场类型 | 乘数 | 原因 |
|---|
| 订阅者/粉丝/观看量里程碑 | 1.25倍 | Social Blade发布每日增长数据——MrBeast能否在X日期前达到4亿订阅可根据趋势计算;散户从不查看 |
| 埃隆·马斯克/高发帖量用户的推文数量 |
1.20倍 | 有记录的稳定节奏(约每周350-400条帖子);市场凭感觉而非数据错误定价 |
| 拳击/MMA/格斗运动结果 |
1.15倍 | 运动记录、训练营信号、比赛历史存在;散户为名气而非技术定价 |
| 真人秀投票结果 |
1.10倍 | Nielsen社交内容评级追踪与投票相关的互动 |
| 奖项(奥斯卡、格莱美、艾美奖、BAFTA) |
0.85倍 | 由专门的奖项追踪者高效定价——优势较小 |
| 名人恋情/分手/离婚 |
0.80倍 | 小报叙事噪音——对立粉丝群双向过度定价 |
| 名人间恩怨/和解 |
0.75倍 | 粉丝迫切希望YES;历史和解基础率低——强烈看空 |
| 巨星粉丝最爱市场(泰勒·斯威夫特、碧昂丝、防弹少年团等) |
0.75倍 | 粉丝忠诚度是主导定价力量——而非信息。这是该类别中最危险的过度拥挤交易 |
因素2——周末重新定价滞后
社交媒体指标(订阅者、流媒体播放量、推文数量、榜单排名)持续更新。但Polymarket做市商在周五晚上到周日期间最不活跃。社交媒体活动在周末达到高峰。现实世界指标变动与市场重新定价之间的差距在周末最大。
| 条件 | 乘数 |
|---|
| 基于指标的问题(订阅者、流媒体、推文、榜单)+ 周五至周日 | 1.15倍 |
| 所有其他组合 |
1.00倍 |
合并后上限为1.35倍——故意低于数据驱动领域(加密货币上限为1.40倍),因为名人市场固有更高的叙事噪音。周六的一个订阅者里程碑问题 → 1.25 × 1.15 = 1.35倍上限。任何时候的泰勒·斯威夫特粉丝最爱市场 → 0.75倍——接近MIN_TRADE下限进行交易。
粉丝忠诚度陷阱——为何巨星为0.75倍
这值得单独说明。涉及泰勒·斯威夫特、碧昂丝、防弹少年团及类似艺人的市场,其粉丝群会以一种可预测的方式结构性错误定价:总是对积极结果过于乐观。泰勒·斯威夫特会宣布世界巡演吗?被竞标到70%,而任何艺人的基础率仅为20%。防弹少年团会在X之前重聚吗?被渴望定价而非概率的粉丝竞标到65%。
0.75倍的抑制因子并不意味着避开这些市场——而是意味着缩小仓位,并在p ≥ NO_THRESHOLD时特别警惕NO信号,因为那里是粉丝竞标过度拥挤创造最可利用差距的地方。
监控的关键词
埃隆·马斯克, 推文, X帖子, YouTube, 订阅者, 病毒式传播, TikTok观看量,
名人, 离婚, 恋情, 恩怨, 真人秀, 单身汉,
奥斯卡, 金球奖, 社交媒体, 粉丝, Instagram, MrBeast,
洛根·保罗, 拳击, 杰克·保罗, 网红, 泰勒·斯威夫特, 碧昂丝,
格莱美, 艾美奖, 流媒体, Spotify流媒体, 榜单, Billboard, 恩怨,
和解, 分手
重新组合信号思路
- - Social Blade API:YouTube/TikTok/Twitch的每日订阅者增长率——将已发布的轨迹与Polymarket里程碑概率进行比较,交易背离
- Spotify Charts API:艺人能否达到X流媒体播放量市场的实时流媒体数据
- Nielsen社交内容评级:与真人秀投票相关的社交互动得分
- X/Twitter API v2:埃隆的实际发帖节奏——与市场隐含的推文数量概率进行比较
安全与执行模式
该技能默认为模拟交易(venue=sim)。仅在使用--live标志时进行真实交易。
| 场景 | 模式 | 财务风险 |
|---|
| python trader.py | 模拟 | 无 |
| Cron / 自动化 |
模拟 | 无 |
| python trader.py --live | 实盘 | 真实USDC |
autostart: false 和 cron: null——在Simmer UI中配置之前,不会自动运行任何内容。
所需凭证
| 变量 | 必需 | 备注 |
|---|
| SIMMERAPIKEY | 是 | 交易权限。视为高价值凭证。 |
可调参数(风险参数)
全部在clawhub.json中声明为tunables,并可在Simmer UI中调整。
| 变量 | 默认值 | 用途 |
|---|
| SIMMERMAXPOSITION | 20 | 每笔交易最大USDC——较低上限反映领域噪音 |
| SIMMERMINVOLUME |
3000 | 最小市场成交量过滤(美元) |
| SIMMER
MAXSPREAD | 0.12 | 最大买卖价差(12%)——对利基市场容忍度更宽 |
| SIMMER
MINDAYS | 3 | 距结算最少天数 |
| SIMMER
MAXPOSITIONS | 10 | 最大同时持仓数量 |
| SIMMER
YESTHRESHOLD | 0.38 | 如果市场价格≤此值则买入YES |
| SIMMER
NOTHRESHOLD | 0.62 | 如果市场价格≥此值则卖出NO |
| SIMMER
MINTRADE | 5 | 任何交易的下限(无论信念如何,最小USDC) |
依赖项
simmer-sdk by Simmer Markets (SpartanLabsXyz)
- - PyPI: https://pypi.org/project/simmer-sdk/
- GitHub: https://github.com/SpartanLabsXyz/simmer-sdk