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Polymarket Esports Trader — 技能工具

v1.0.3

Trades esports tournament, game release, and streaming milestone prediction markets on Polymarket. Exploits three stacked edges — game data richness (HLTV El...

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by @diagnostikon·MIT-0
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License
MIT-0
最后更新
2026/4/5
安全扫描
VirusTotal
Pending
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OpenClaw
可疑
high confidence
The skill's documentation claims 'no external API' and lists no required credentials, but the included manifest and code require a Simmer SDK and a SIMMER_API_KEY and can execute real trades — the pieces are inconsistent and deserve attention before installing.
评估建议
Do not install blindly. Before proceeding: 1) Confirm you are willing to supply a SIMMER_API_KEY (this is a sensitive API credential that the skill uses to connect to a third-party trading service). 2) Inspect and vet the simmer-sdk package/source (pip package) to ensure you trust it. 3) Be aware the skill can place real trades if invoked with --live — test only in paper/sim mode first and verify that paper mode truly has no external financial side effects. 4) Ask the author to fix inconsistenci...
详细分析 ▾
用途与能力
SKILL.md repeatedly states the default signal needs 'no external API' and presents the skill as an instruction/template, but clawhub.json declares a pip dependency (simmer-sdk) and a required env SIMMER_API_KEY, and trader.py instantiates SimmerClient — so the skill clearly depends on an external trading API. The registry top-level metadata also omitted the required env var, creating an internal mismatch.
指令范围
SKILL.md describes market discovery and trade execution but omits the requirement to supply a Simmer API key or the runtime behavior of the Simmer client. The runtime instructions (trader.py) will call external APIs to find markets and (optionally, with --live) place real trades. The instructions do not direct access to unrelated system files, but they fail to disclose the external networked dependency and live-trading capability.
安装机制
There is no explicit install spec in the top-level metadata, but clawhub.json lists a pip dependency ('simmer-sdk'), which is a normal package-install pattern. This is not as risky as arbitrary downloads, but the missing/contradictory install information increases the chance of runtime failures and surprises (i.e., the skill may require pip installing simmer-sdk before it runs).
凭证需求
The code requires SIMMER_API_KEY (sensitive credential) to construct a SimmerClient, yet the public metadata claimed no required env vars. Requesting one API key for a trading SDK is proportionate to a trading skill, but the omission in the SKILL.md/registry is a red flag. Numerous non-secret tunables (SIMMER_* numeric settings) are exposed for configuration — expected — but the missing disclosure about the API key and its privileges is problematic.
持久化与权限
always:false and autostart:false; the automaton is managed with an entrypoint but default behavior is paper trading (venue='sim'). The skill can perform real trades only if launched with an explicit --live flag. No evidence it modifies other skills or requests permanent global privileges.
安全有层次,运行前请审查代码。

License

MIT-0

可自由使用、修改和再分发,无需署名。

运行时依赖

无特殊依赖

版本

latestv1.0.32026/3/15

Republish to refresh registry metadata

● Pending

安装命令 点击复制

官方npx clawhub@latest install polymarket-esports-trader
镜像加速npx clawhub@latest install polymarket-esports-trader --registry https://cn.clawhub-mirror.com

技能文档

This is a template.
The default signal is keyword-based market discovery combined with conviction-based sizing and esports_bias() — three stacked structural edges, no external API required.
The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Esports markets are mispriced in two directions simultaneously. Data-rich titles (CS2, LoL, Dota 2) have published Elo models, map win rates, and patch-level performance metrics that retail ignores entirely. At the same time, fan-favourite teams (T1/Faker) are systematically overcrowded by fanbases trading loyalty rather than skill assessment. Three structural edges compound cleanly without any API.

Signal Logic

Default Signal: Conviction-Based Sizing with Esports Bias

  • Discover active esports and gaming markets on Polymarket
  • Compute base conviction from distance to threshold (0% at boundary → 100% at p=0/p=1)
  • Apply esports_bias() — three layers: game data quality × series format × Asian session timing
  • Size = max(MIN_TRADE, conviction × bias × MAX_POSITION) — capped at MAX_POSITION
  • Skip markets with spread > MAX_SPREAD or fewer than MIN_DAYS to resolution

Esports Bias (built-in, no API required)

Layer 1 — Game / Market Type

Game / market typeMultiplierKey data source retail ignores
T1 / Faker markets0.75xFandom overcrowds YES by 10–20% vs Elo model — documented 2023–2025
CS2 / Counter-Strike1.20xHLTV.org Elo ratings, map win rates, head-to-head history
League of Legends (non-T1)1.15xOracle's Elixir patch-level stats — meta shifts change team win rates ±15%
Dota 2 / The International1.15xOpenDota comprehensive match stats — consistency rewarded in long series
Valorant / VCT1.10xVLR.gg agent win rates, map pools — growing and increasingly accurate
Mobile esports (HoK, PUBG Mobile, MLBB)1.15xDeep Asian stats with Western info lag
Game release date milestone1.10xPublisher delay history documented — ~70% re-delay rate for prior delayers
Twitch / streaming peak viewership1.10xTwitchTracker daily historical peaks — viewership growth curves trackable
Steam concurrent player milestone1.10xSteamCharts real-time — launch peaks predictable from pre-order velocity
The T1 / Faker Rule — The most precisely documented single-team overcrowding in all of esports. Faker's global fandom spans every region, every language, every platform. The result is systematic YES overpricing on T1 outcomes by 10–20% relative to what HLTV/Oracle's Elixir Elo models imply. T1 are genuinely elite — but the market price of T1 wins is almost always too high because the fan base is the dominant pricing force, not analysts. This is not a bet against T1 — it is a sizing discipline: trade T1 markets very conservatively.

Layer 2 — Series Format: Variance Reduction by Match Length

This is the cleanest mechanic in the entire trader — no data needed, just understanding how best-of series work:

FormatMultiplierStatistical reality
Bo5 / Grand Final / Championship1.20xStronger team wins ~72–78% — retail says "anything can happen" which is statistically false
Bo3 / Playoff / Semifinal / Elimination1.10xStronger team wins ~65–70% — meaningful variance reduction
Bo1 / Group Stage / Swiss / Round Robin0.90x~40% upset rate — genuine uncertainty, reduce conviction
The Grand Final insight: retail treats championship matches as the most uncertain because "the stakes are highest." The opposite is true statistically. Teams playing Bo5 Grand Finals have survived multiple elimination rounds — they are the two best teams in the tournament, playing the format that most reliably selects the winner. This is maximum-edge territory, not minimum.

Layer 3 — Asian Session Timing

LoL LCK/LPL, mobile esports, and Dota 2 SEA feature Korean, Chinese, and Southeast Asian teams competing at 01:00–09:00 UTC. Polymarket is US-dominated — match results in these regions take 30–90 minutes to fully reprice when US retail is asleep.

ConditionMultiplier
Asian-dominant game + 01:00–09:00 UTC1.15x — lag window open
All other times1.00x

Combined Examples

MarketTypeFormatTimingFinal bias
CS2 Bo5 Grand Final1.20x1.20x1.0x1.35x cap
T1 Bo3 match0.75x1.10x1.0x0.83x
LoL LCK Bo5 at 04:00 UTC1.15x1.20x1.15x1.35x cap
Dota 2 Bo1 group stage1.15x0.90x1.0x1.04x
Any Bo1 group matchtype_mult0.90x1.0xEdge compressed

Keywords Monitored

esports, League of Legends, CS2, Counter-Strike, Dota 2, Valorant, Fortnite,
World Championship, tournament, Steam, Twitch, game release, PlayStation,
Xbox, Nintendo, gaming revenue, Riot Games, Blizzard, grand final, bracket,
LCK, LPL, LEC, BLAST, ESL, VCT, The International, HLTV, peak viewers,
concurrent players, T1, Faker, NaVi, Vitality, patch

Remix Signal Ideas

  • HLTV.org Elo ratings: Compare published Elo-implied win probability to Polymarket price for CS2 matchup markets — the gap is consistently 8–15% for non-marquee matches
  • Oracle's Elixir: LoL team stats by patch — when a meta patch hits 2 days before a tournament, markets haven't adjusted; the data has
  • Liquipedia API: Real-time bracket data, match results, team stats for 30+ esports titles — feed bracket position into p to trade next-round markets
  • TwitchTracker: Daily peak viewer history for "will X reach Y viewers" markets — compare trajectory to market price

Safety & Execution Mode

The skill defaults to paper trading (venue="sim"). Real trades only with --live flag.

ScenarioModeFinancial risk
python trader.pyPaper (sim)None
Cron / automatonPaper (sim)None
python trader.py --liveLive (polymarket)Real USDC
autostart: false and cron: null — nothing runs automatically until you configure it in Simmer UI.

Required Credentials

VariableRequiredNotes
SIMMER_API_KEYYesTrading authority. Treat as high-value credential.

Tunables (Risk Parameters)

All declared as tunables in clawhub.json and adjustable from the Simmer UI.

VariableDefaultPurpose
SIMMER_MAX_POSITION20Max USDC per trade — reflects esports market liquidity
SIMMER_MIN_VOLUME3000Min market volume filter (USD)
SIMMER_MAX_SPREAD0.10Max bid-ask spread (10%)
SIMMER_MIN_DAYS2Min days until resolution — tournaments move fast
SIMMER_MAX_POSITIONS10Max concurrent open positions
SIMMER_YES_THRESHOLD0.38Buy YES if market price ≤ this value
SIMMER_NO_THRESHOLD0.62Sell NO if market price ≥ this value
SIMMER_MIN_TRADE5Floor for any trade (min USDC regardless of conviction)

Dependency

simmer-sdk by Simmer Markets (SpartanLabsXyz)

  • PyPI: https://pypi.org/project/simmer-sdk/
  • GitHub: https://github.com/SpartanLabsXyz/simmer-sdk
数据来源:ClawHub ↗ · 中文优化:龙虾技能库
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