24h NBA Game Structure Trader

This is a template. The default signal detects structural inconsistencies across correlated NBA game markets -- remix it with additional sports, league-specific heuristics, or live odds feeds. The skill handles all the plumbing (market discovery, game grouping, cross-market checks, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Polymarket lists multiple correlated markets for each NBA game:

• Moneyline: "Clippers vs. Pacers" = 79.7%
• 1H Moneyline: "Clippers vs. Pacers: 1H Moneyline" = 50.5%
• Full-game O/U: "Clippers vs. Pacers: O/U 235.5" = 56.7%, O/U 236.5 = 55.2%, O/U 237.5 = 52%
• 1H O/U: "Clippers vs. Pacers: 1H O/U 114.5" = 50.5%
• Spreads: "Spread: Clippers (-8.5)" = 41%, "1H Spread: Clippers (-5.5)" = 50.5%

Retail trades each market as an isolated bet. But together, these markets form a structural web that must be internally consistent.

This skill reconstructs the full game structure and finds where it is mathematically broken.

The Edge: NBA Game Structure Arbitrage

Inconsistency Type 1: Moneyline vs 1H Moneyline

If the full-game moneyline says a team is an 80% favorite, the 1H moneyline cannot be a coin-flip. The expected 1H advantage is dampened but must exist:

If ML_full > 70%, then ML_1h > 50% + (ML_full - 50%) * dampening

When the 1H moneyline diverges too far from what the full-game moneyline implies, the 1H market is mispriced.

Inconsistency Type 2: O/U Monotonicity

Within the same game, the probability of going OVER must decrease as the line increases:

P(O/U 235.5 OVER) >= P(O/U 236.5 OVER) >= P(O/U 237.5 OVER)

If a higher line is priced above a lower line, the curve is broken -- pure structural arbitrage.

Inconsistency Type 3: 1H O/U vs Full-Game O/U

At the same line value, the full-game total always exceeds the 1H total, so:

P(Full O/U X OVER) >= P(1H O/U X OVER)

If a 1H O/U market is priced higher than the equivalent full-game O/U market, the relationship is violated.

Inconsistency Type 4: Spread vs Moneyline Direction

The spread favorite (negative line) must be the moneyline favorite. If the spread says Clippers (-8.5) but the moneyline says Pacers are favored, the markets contradict each other.

Why This Works

1. Retail trades in silos -- most users view each market independently and do not cross-reference the full game structure
2. No market maker enforcement -- unlike sportsbooks, there is no central entity maintaining consistency across related markets
3. Mathematical, not opinion -- the violations are provable inconsistencies in the implied game model
4. NBA-specific density -- NBA games generate 5-15+ correlated markets per game, creating more surface area for inconsistencies
5. Rapid line movement -- NBA spreads and totals move on injury news, creating temporary cross-market divergences

Signal Logic

1. Discover all NBA-related markets via keyword and team name search
2. Parse each question: extract teams, market type (moneyline/spread/O/U), scope (full/1H), line value
3. Group ALL markets for the same game into a GameGroup
4. For each game with 2+ markets:
   • Check moneyline vs 1H moneyline consistency
   • Check O/U monotonicity within full-game and 1H lines
   • Check 1H O/U vs full-game O/U at matching lines
   • Check spread direction vs moneyline direction
5. Rank inconsistencies by magnitude
6. Trade only inconsistencies that also pass threshold gates (YES_THRESHOLD / NO_THRESHOLD)
7. Size by conviction, not flat amount

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 have professional line-setters who enforce consistency across all markets for the same game. Polymarket has no such mechanism -- each market (moneyline, spread, O/U, 1H variants) is priced by its own order book with its own liquidity pool. This creates systematic micro-inconsistencies in the implied game model, especially when:

• Injury news moves the moneyline but not the 1H moneyline
• O/U lines are added at new values without re-pricing existing ones
• Large directional flow on spreads does not propagate to correlated markets
• 1H markets diverge from full-game markets during low-liquidity hours

This skill treats the full game structure as a consistency web and trades the repair.

Dependency

simmer-sdk by Simmer Markets (SpartanLabsXyz)

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