Ladder -- F1 Championship Distribution Trader

This is a template. The default signal exploits distribution-sum violations in F1 championship winner markets. No external API required beyond simmer-sdk. The skill discovers F1 driver championship markets, groups them by championship, checks whether the implied probability distribution sums to ~100%, and trades the correction when it drifts.

Strategy Overview

In a winner-takes-all market like "Who will be the 2026 F1 Drivers' Champion?", the individual driver probabilities must sum to approximately 100%. On Polymarket, each driver is listed as a separate binary market. Because each market has its own bid-ask spread, retail flow, and narrative momentum, the implied probabilities frequently drift apart:

• Sum > 105%: The field is collectively overpriced. Each driver's YES price is inflated relative to the fair share of 100%. We sell the most overpriced drivers (buy NO on the highest-probability ones).
• Sum < 95%: The field is collectively underpriced. Each driver's YES price is too low. We buy the most underpriced drivers (buy YES on the lowest-probability ones).

This is a structural arbitrage on probability distributions, not a directional bet on any single driver winning or losing.

The Core Insight: Why Distribution Violations Exist

Three mechanisms create and sustain distribution-sum violations:

1. Independent bid-ask spreads Each driver market has its own spread. When you sum 20 drivers' mid prices, the individual spreads compound. A 2-3 cent spread on each of 20 markets can push the total sum 10-15% above or below 100%.

2. Narrative-driven retail flow When news breaks (e.g., Verstappen dominates a qualifying session), retail piles into that driver's YES market, pushing the entire field's sum above 100%. The other drivers' prices don't drop fast enough to compensate because there's no automated arbitrage mechanism across separate binary markets.

3. Stale pricing on low-liquidity drivers The top 3-5 drivers have active order books. The remaining 15+ drivers may have stale prices that don't adjust when the favorites move, creating persistent sum violations.

Signal Logic

Step 1 -- Market Discovery

Keyword search across F1-specific terms plus a get_markets(limit=200) fallback. All candidates are filtered by a regex that matches F1/motorsport market questions.

Step 2 -- Championship Grouping

Markets are parsed to extract (championship_name, driver_name) tuples using regex patterns that match:

• "Will Verstappen be the 2026 F1 Drivers' Champion?"
• "Will Gasly win the 2026 Formula 1 championship?"

Markets are grouped by championship name.

Step 3 -- Distribution Sum Check

For each championship group, sum all driver probabilities. If |sum - 1.0| < MIN_VIOLATION (default 5%), the distribution is within tolerance and we skip the group.

Step 4 -- Signal Direction

• Overpriced field (sum > 105%): Sort drivers by probability descending. Buy NO on drivers above NO_THRESHOLD (most overpriced first).
• Underpriced field (sum < 95%): Sort drivers by probability ascending. Buy YES on drivers below YES_THRESHOLD (most underpriced first).

Step 5 -- Conviction-Based Sizing

For NO (overpriced field):

conviction = (p - NO_THRESHOLD) / (1 - NO_THRESHOLD)
violation_boost = min(1.5, 1.0 + violation_magnitude)
size = max(MIN_TRADE, conviction * violation_boost * MAX_POSITION)

For YES (underpriced field):

conviction = (YES_THRESHOLD - p) / YES_THRESHOLD
violation_boost = min(1.5, 1.0 + violation_magnitude)
size = max(MIN_TRADE, conviction * violation_boost * MAX_POSITION)

The violation boost increases position size when the distribution-sum violation is larger (more edge).

How Sizing Works

With defaults (YES_THRESHOLD=38%, NO_THRESHOLD=62%, MIN_TRADE=$5, MAX_POSITION=$40, violation=10%):

Overpriced field (buy NO on high-p drivers):

Underpriced field (buy YES on low-p drivers):

Keywords Monitored

F1, Formula 1, champion, Drivers Champion, Grand Prix,
Verstappen, Hamilton, Norris, Leclerc, Piastri,
Russell, Gasly, Albon, Bottas

Remix Signal Ideas

• Cross-championship arbitrage: Compare distribution-sum violations across multiple championships (F1, MotoGP, IndyCar) and prioritize the most violated distribution
• Historical sum tracking: Track the distribution sum over time and trade mean-reversion when the sum spikes or dips
• News-driven violation detection: Wire in an F1 news feed and trigger scans immediately after major race results or qualifying sessions, when retail flow is most likely to create violations
• Team-pair arbitrage: Within a team, the two drivers' probabilities should roughly reflect their relative performance -- trade when team-pair ratios diverge from season data
• Volume-weighted conviction: Weight conviction by each driver market's volume -- low-volume markets have staler prices and potentially more edge

Safety & Execution Mode

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

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

Required Credentials

Tunables (Risk Parameters)

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

Edge Thesis

F1 driver championship winner probabilities on Polymarket systematically deviate from a 100% total because of three structural mechanisms:

1. Retail trades drivers individually -- users buy YES on their favorite driver without checking whether the field total already exceeds 100%. A wave of enthusiasm after a race result inflates the winner without deflating the rest.
2. No cross-market consistency enforcement -- unlike a traditional bookmaker who adjusts the entire field when one runner's odds move, Polymarket has no market maker enforcing that the sum of all driver probabilities stays at 100%. Each driver is a separate order book with independent liquidity.
3. Driver entry and exit creates transient imbalances -- when a new driver is added to the championship market (mid-season replacement, rookie promotion) or a driver is removed (retirement, disqualification), the existing prices do not instantly re-normalize. The sum drifts until enough traders notice and correct it.

These three forces guarantee recurring distribution-sum violations that revert as resolution approaches and the market must price a single winner at 100%.

Dependency

simmer-sdk by Simmer Markets (SpartanLabsXyz)

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