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. 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. 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 MAXPOSITION
5. Skip markets with spread > MAXSPREAD 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
INLINECODE6	Paper (sim)	None
Cron / automaton

Paper (sim) | None |
| python trader.py --live | Live (polymarket) | Real USDC |

INLINECODE8 and cron: null — nothing runs automatically until you configure it in Simmer UI.

Required Credentials

Variable	Required	Notes
INLINECODE10	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
INLINECODE13	INLINECODE14	Max USDC per trade (reached at 100% conviction)
INLINECODE15

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

INLINECODE29 by Simmer Markets (SpartanLabsXyz)

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