Polymarket Sports Live Trader

Trades Polymarket prediction markets on sports championships, tournament outcomes, MVP awards, transfer windows, and season milestones. Use when you want to...

Sports & Championships Trader

This is a template. The default signal is keyword-based market discovery combined with probability-extreme detection — remix it with the data sources listed in the Edge Thesis below. The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

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

1. Fan loyalty dampening — Popular clubs (Real Madrid, Man City, Lakers) are systematically overpriced by emotional retail traders
2. Sports calendar timing — Each sport has a defined peak season; trading in-season means better signal density

Signal Logic

Default Signal: Conviction-Based Sizing with Fan Bias + Calendar

1. Discover active sports markets on Polymarket
2. Compute base conviction from distance to threshold (0% at boundary → 100% at p=0/p=1)
3. Apply sport_bias() — combines fan loyalty adjustment with sports calendar timing
4. Size = max(MIN_TRADE, conviction × bias × MAX_POSITION) — capped at MAX_POSITION
5. 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 events (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	Journalist sources trackable 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 run-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 capped at 1.35x. Example: Transfer market in July → 1.20 × 1.20 = 1.35x (capped).

Remix Signal Ideas

• Club Elo: Replace market.current_probability with Elo-implied win probability — trade divergence vs market
• FiveThirtyEight NBA/NFL models: Same divergence approach for American sports
• Transfermarkt API: Player valuations and injury status as signal inputs
• ESPN hidden API: https://site.api.espn.com/apis/site/v2/sports/{sport}/{league}/scoreboard for live scores/injury data

Safety & Execution Mode

The skill 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

autostart: false and cron: null — nothing runs automatically until you configure it in Simmer UI.

Required Credentials

Variable	Required	Notes
SIMMER_API_KEY	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
SIMMER_MAX_POSITION	25	Max USDC per trade (reached at 100% conviction)
SIMMER_MIN_VOLUME	5000	Min market volume filter (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