Bundle -- Tennis Set-Match Constraint Trader

This is a template. The default signal detects cross-market constraint violations between tennis Set 1 O/U, Match O/U, Total Sets O/U, and Set Handicap props -- remix it with live score feeds, surface-specific models, or player fatigue data. The skill handles all the plumbing (market discovery, bundle construction, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Tennis matches on Polymarket spawn multiple correlated prop markets: Set 1 Games O/U, Match Games O/U, Total Sets O/U, Set Handicap, and Set/Match Winner. These props are mathematically constrained -- match total games must always exceed Set 1 total games, and Total Sets O/U 2.5 directly implies the probability of a straight-sets finish which constrains set handicap pricing. When Polymarket prices violate these structural constraints, it is pure arbitrage.

Edge

Each tennis match prop is priced by its own order book with independent liquidity. Retail traders view each prop in isolation -- they price "Set 1 Games O/U 9.5" and "Match Games O/U 9.5" independently, ignoring the mathematical certainty that Match total >= Set 1 total. Similarly, "Total Sets O/U 2.5 OVER" at 60% means there is a 40% chance of a straight-sets finish, which directly bounds what "Set Handicap -1.5" should price at. No sportsbook would allow these constraints to be violated, but Polymarket has no central line-setter enforcing cross-prop consistency.

Signal Logic

1. Discover active tennis markets via keyword search + get_markets(limit=200) fallback
2. Parse each question: extract match_key, prop_type (set1_ou, match_ou, total_sets, set_handicap, set_winner), line_value
3. Filter non-tennis markets with regex
4. Group into bundles by match_key (normalized player names)
5. Check constraint 1: P(Match O/U X OVER) >= P(Set 1 O/U X OVER) for same line X
6. Check constraint 2: if Total Sets O/U 2.5 > 50% (likely 3 sets), Set Handicap -1.5 should be < P(straight sets)
7. Rank violations by magnitude, trade the most mispriced prop
8. Size by conviction (violation magnitude), not flat amount

Remix Signal Ideas

• Live score feeds: During a match, if Set 1 goes to a tiebreak (7-6), the remaining sets likely follow similar tempo -- adjust Match O/U expectations in real-time
• Surface-specific models: Clay court matches average more games per set than grass -- weight violations differently by surface
• Head-to-head data: Some player matchups consistently produce straight-set results -- use H2H history to sharpen Total Sets O/U constraint bounds
• Player fatigue tracking: Late-round tournament matches after 5-setters create fatigue patterns that shift set handicap probabilities

Safety & Execution Mode

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

autostart: false and cron: null mean nothing runs automatically until configured in Simmer UI.

Required Credentials

Tunables (Risk Parameters)

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

Edge Thesis

Traditional sportsbooks employ professional line-setters who enforce mathematical consistency across all props within the same match. Polymarket has no such mechanism -- each prop is priced by its own order book. Tennis is especially vulnerable because:

• Set 1 and Match totals have a strict mathematical relationship (match total >= set 1 total)
• Total Sets O/U 2.5 directly implies the probability distribution of set scores, constraining set handicap
• Tennis prop markets are thinner than mainstream match winner markets, widening violations
• Retail traders price each prop independently without cross-checking the bundle
• New props added mid-tournament inherit no pricing from related markets

This skill treats the tennis prop bundle as a system of constraints and trades the repair when any constraint is violated.

Dependency

simmer-sdk by Simmer Markets (SpartanLabsXyz)

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