Kalshi F1 Constructor Trader#

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)#

ConstructorRatingNotesRed Bull95Top pace, dominant qualifyingMcLaren92Strong race pace, consistentFerrari90Great qualifier, variable race paceMercedes88Improving development trajectoryAston Martin82Best 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#

ParameterDefaultNotesEntry edge10%Min model-vs-market divergence to tradeExit threshold45%Sell when position price reaches thisMax position size$5.00 USDCPer marketMax trades per run4Rate limitingMax slippage15%Skip if slippage exceedsMin liquidity$0Disabled by default

Installation & Setup#

bash

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.

ScenarioModeFinancial riskpython trader.pyDry runNoneCron / automatonDry runNonepython trader.py --liveLive (Kalshi via DFlow)Real USDC

Required Credentials#

VariableRequiredNotesSIMMER_API_KEYYesTrading authority. Treat as a high-value credential.SOLANA_PRIVATE_KEYYesBase58-encoded Solana private key for live trading.

Tunables (Risk Parameters)#

VariableDefaultPurposeSIMMER_F1_CONSTR_ENTRY_EDGE``0.10Min divergence to trigger tradeSIMMER_F1_CONSTR_EXIT_THRESHOLD``0.45Sell position when price reaches this levelSIMMER_F1_CONSTR_MAX_POSITION_USD``5.00Max USDC per tradeSIMMER_F1_CONSTR_MAX_TRADES_PER_RUN``4Max trades per execution cycleSIMMER_F1_CONSTR_SLIPPAGE_MAX``0.15Max slippage before skipping tradeSIMMER_F1_CONSTR_MIN_LIQUIDITY``0Min 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: hello@simmer.markets