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:

Fan loyalty dampening — Popular clubs (Real Madrid, Man City, Lakers) are systematically overpriced by emotional retail traders
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

Discover active sports markets on Polymarket
Compute base conviction from distance to threshold (0% at boundary → 100% at p=0/p=1)
Apply sport_bias() — combines fan loyalty adjustment with sports calendar 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

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

polymarket-sports-live-trader

Installation