Polymarket Ladder F1 Championship Trader

Trades distribution-sum violations in F1 championship winner markets on Polymarket. Driver winner probabilities form a distribution that must sum to ~100% — when the field is collectively overpriced or underpriced, individual driver markets are structurally mispriced.

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):

text

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):

text

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):

Driver p	Conviction	Violation boost	Size
62% (at threshold)	0%	1.10x	$5 (floor)
70%	21%	1.10x	$9
80%	47%	1.10x	$21
90%	74%	1.10x	$33

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

Driver p	Conviction	Violation boost	Size
38% (at threshold)	0%	1.10x	$5 (floor)
30%	21%	1.10x	$9
20%	47%	1.10x	$21
10%	74%	1.10x	$33

Keywords Monitored

text

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.

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	40	Max USDC per trade (reached at 100% conviction)
SIMMER_MIN_VOLUME	5000	Min market volume filter
SIMMER_MAX_SPREAD	0.06	Max bid-ask spread (6%)
SIMMER_MIN_DAYS	7	Min days until resolution
SIMMER_MAX_POSITIONS	8	Max concurrent open positions
SIMMER_YES_THRESHOLD	0.38	Buy YES when driver probability <= this in underpriced field
SIMMER_NO_THRESHOLD	0.62	Buy NO when driver probability >= this in overpriced field
SIMMER_MIN_TRADE	5	Floor for any trade (min USDC regardless of conviction)
SIMMER_MIN_VIOLATION	0.05	Min distribution-sum violation to trigger trades (5%)

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