Polymarket Sports Live Trader — Polymarket 体育直播交易员

Sports & Championships Trader

This is a template. The default 签名al is keyword-based market discovery combined with probability-extreme 检测ion — remix it with the data sources 列出ed in the Edge Thesis below. The 技能 handles all the plumbing (market discovery, trade execution, safe防护s). Your 代理 provides the alpha.

Strategy Overview

Sports prediction markets are dominated by passionate fans who bet emotionally. This 创建s two structural edges this 技能 exploits without any external API:

Fan loyalty dampening — Popular clubs (Real Madrid, Man City, Lakers) are 系统atically overpriced by emotional retAIl traders Sports calendar timing — Each sport has a defined peak season; trading in-season means better 签名al density 签名al 记录ic Default 签名al: Conviction-Based Sizing with Fan Bias + Calendar Discover active sports markets on Polymarket Compute base conviction from distance to threshold (0% at boundary → 100% at p=0/p=1) 应用ly sport_bias() — combines fan loyalty adjustment with sports calendar timing Size = max(MIN_TRADE, conviction × bias × MAX_POSITION) — c应用ed at MAX_POSITION Skip markets with spread > MAX_SPREAD or fewer than MIN_DAYS to resolution Sport Bias (built-in, no API required)

Factor 1 — Fan Loyalty Adjustment

Market type Multiplier Why Fan-favorite clubs (Real Madrid, Man City, Lakers) 0.75x Fan loyalty inflates YES — high noise, trade cautiously Peak fan 事件 (Super Bowl, UCL final, World Cup final) 0.80x Maximum emotional retAIl attention = maximum mispricing Individual sports (tennis, F1, golf) 1.15x Individual performance is more data-driven than team sports Transfer / contract markets 1.20x Journa列出 sources 追踪able before market reprices Award markets (MVP, Ballon d'Or, Golden Boot) 1.10x Stats-driven — quantifiable advantage

Factor 2 — Sports Calendar Timing

Sport / Event Active season In-season multiplier Football title 运行-in (UCL, PL, Liga) Mar–May 1.15x Transfer windows Jan + Jun–Sep 1.20x NBA playoffs Apr–Jun 1.15x NFL season Sep–Feb 1.10x Tennis / Wimbledon Jun–Sep 1.15x

Combined and c应用ed at 1.35x. Example: Transfer market in July → 1.20 × 1.20 = 1.35x (c应用ed).

Remix 签名al Ideas Club Elo: Replace market.current_probability with Elo-implied win probability — trade divergence vs market FiveThirtyEight NBA/NFL 模型s: Same divergence 应用roach for American sports Transfermarkt API: Player valuations and injury 状态 as 签名al 输入s ESPN hidden API: https://site.API.espn.com/APIs/site/v2/sports/{sport}/{league}/scoreboard for live scores/injury data Safety & Execution Mode

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

Scenario Mode Financial risk python trader.py Paper (sim) None Cron / automaton Paper (sim) None python trader.py --live Live (polymarket) Real USDC

auto启动: false and cron: null — nothing 运行s automatically until you 配置 it in Simmer UI.

Required 凭证s Variable Required Notes SIMMER_API_KEY Yes Trading authority. Treat as high-value 凭证. Tunables (Risk Parameters)

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

Variable Default Purpose SIMMER_MAX_POSITION 25 Max USDC per trade (reached at 100% conviction) SIMMER_MIN_VOLUME 5000 Min market volume 过滤器 (USD) SIMMER_MAX_SPREAD 0.08 Max bid-ask spread (8%) SIMMER_MIN_DAYS 2 Min days until resolution SIMMER_MAX_POSITIONS 8 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

simmer-sdk by Simmer Markets (SpartanLabsXyz)

PyPI: https://pypi.org/project/simmer-sdk/ GitHub: https://github.com/SpartanLabsXyz/simmer-sdk