Kalshi F1 Constructor Trader

Trades F1 Drivers Championship markets on Kalshi using constructor (team) car performance ratings. Drivers in faster cars have structurally higher championship probabilities. Requires SIMMER_API_KEY and simmer-sdk.

This is a template. The default signal uses static constructor ratings and driver skill modifiers -- remix it with live qualifying data, FP session lap times, or car development trajectory analysis. The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Constructor (team) car speed is the single strongest predictor of F1 championship outcomes. A driver in the fastest car wins the championship ~70% of the time historically. This skill rates each constructor's car performance, applies individual driver skill modifiers, and computes fair championship probabilities using a power-law model.

Key advantages:

• Car > driver -- constructor advantage explains most championship variance
• Stable signal -- car performance changes slowly (major upgrades every 3-5 races)
• Markets overweight narratives -- retail traders overreact to single race results while ignoring underlying car pace
• Power-law compounding -- small car advantages compound over a 24-race season

Signal Logic

Constructor Model

1. Rate each constructor's car performance (0-100 scale)
2. Apply individual driver skill modifiers (teammate comparison)
3. Compute effective driver rating = constructor_base + skill_modifier
4. Convert to probabilities: P(win) proportional to rating^3 (power law)
5. Compare model probability to Kalshi market price
6. Trade when |model - market| >= entry_edge

Constructor Ratings (2025)

Constructor	Rating	Notes
Red Bull	95	Top pace, dominant qualifying
McLaren	92	Strong race pace, consistent
Ferrari	90	Great qualifier, variable race pace
Mercedes	88	Improving development trajectory
Aston Martin	82	Best of the rest

Conviction-Based Sizing

• conviction = min(|edge| / entry_edge, 2.0) / 2.0
• size = max($1.00, conviction * MAX_POSITION_USD)
• Larger edge = larger position, capped at MAX_POSITION_USD

Risk Parameters

Parameter	Default	Notes
Entry edge	10%	Min model-vs-market divergence to trade
Exit threshold	45%	Sell when position price reaches this
Max position size	$5.00 USDC	Per market
Max trades per run	4	Rate limiting
Max slippage	15%	Skip if slippage exceeds
Min liquidity	$0	Disabled by default

Installation & Setup

clawhub install kalshi-f1-constructor-trader

Requires: SIMMER_API_KEY and SOLANA_PRIVATE_KEY environment variables.

Cron Schedule

Cron is set to null -- the skill does not run on a schedule until you configure it in the Simmer UI.

Safety & Execution Mode

The skill defaults to dry-run mode. Real trades only execute when --live is passed explicitly.

Scenario	Mode	Financial risk
python trader.py	Dry run	None
Cron / automaton	Dry run	None
python trader.py --live	Live (Kalshi via DFlow)	Real USDC

Required Credentials

Variable	Required	Notes
SIMMER_API_KEY	Yes	Trading authority. Treat as a high-value credential.
SOLANA_PRIVATE_KEY	Yes	Base58-encoded Solana private key for live trading.

Tunables (Risk Parameters)

Variable	Default	Purpose
SIMMER_F1_CONSTR_ENTRY_EDGE	0.10	Min divergence to trigger trade
SIMMER_F1_CONSTR_EXIT_THRESHOLD	0.45	Sell position when price reaches this level
SIMMER_F1_CONSTR_MAX_POSITION_USD	5.00	Max USDC per trade
SIMMER_F1_CONSTR_MAX_TRADES_PER_RUN	4	Max trades per execution cycle
SIMMER_F1_CONSTR_SLIPPAGE_MAX	0.15	Max slippage before skipping trade
SIMMER_F1_CONSTR_MIN_LIQUIDITY	0	Min market liquidity USD (0 = disabled)

Dependency

simmer-sdk is published on PyPI by Simmer Markets.

• PyPI: https://pypi.org/project/simmer-sdk/
• GitHub: https://github.com/SpartanLabsXyz/simmer-sdk
• Publisher: [e-mail]