Kalshi F1 Points Model Trader#

This is a template.

The default signal uses static points standings and driver ratings to Monte Carlo simulate the remaining season -- remix it with live F1 API data, qualifying pace analysis, or weather-adjusted race models.

The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview#

F1 Drivers Championship markets on Kalshi price each driver's chance of winning the title. This skill runs Monte Carlo simulations using current points standings and driver skill ratings to compute fair win probabilities, then trades when the market diverges from the model.

Key advantages:

• Points standings are public -- no proprietary data needed
• Monte Carlo captures non-linear dynamics -- points gaps, remaining races, and driver variance
• Driver ratings provide edge -- market often misprices mid-field drivers

Signal Logic#

Points-Based Monte Carlo Model#

1. Load current F1 Drivers Championship standings
2. Assign driver skill ratings (affects finishing position distribution)
3. Simulate remaining races 10,000 times with weighted random finishes
4. Count championship wins per driver to get win probabilities
5. Compare model probability to Kalshi market price
6. Trade when |model - market| >= entry_edge

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

Remix Ideas#

• Live F1 API: Replace static standings with real-time Ergast/OpenF1 API
• Qualifying pace model: Weight recent qualifying gaps for more accurate finishing distributions
• Constructor performance: Factor in car development trajectory
• Weather/track type: Adjust driver ratings for rain races or street circuits

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 run5Rate limitingMax slippage15%Skip if slippage exceedsMin liquidity$0Disabled by default

Installation & Setup#

bash

clawhub install kalshi-f1-points-model-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

The automaton cron is set to null -- it does not run on a schedule until you configure it in the Simmer UI. autostart: false means it won't start automatically on install.

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)#

All risk parameters are declared in clawhub.json as tunables and adjustable from the Simmer UI without code changes.

VariableDefaultPurposeSIMMER_F1_PTS_ENTRY_EDGE``0.10Min divergence between model and market to trigger tradeSIMMER_F1_PTS_EXIT_THRESHOLD``0.45Sell position when price reaches this levelSIMMER_F1_PTS_MAX_POSITION_USD``5.00Max USDC per tradeSIMMER_F1_PTS_MAX_TRADES_PER_RUN``5Max trades per execution cycleSIMMER_F1_PTS_SLIPPAGE_MAX``0.15Max slippage before skipping (0.15 = 15%)SIMMER_F1_PTS_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

Review the source before providing live credentials if you require full auditability.