Kalshi F1 Points Model Trader

Trades F1 Drivers Championship winner markets on Kalshi using current points standings and Monte Carlo simulation to compute win probabilities. Requires SIMM...

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

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	5	Rate limiting
Max slippage	15%	Skip if slippage exceeds
Min liquidity	$0	Disabled by default

Installation & Setup

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.

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

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

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)

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

Variable	Default	Purpose
SIMMER_F1_PTS_ENTRY_EDGE	0.10	Min divergence between model and market to trigger trade
SIMMER_F1_PTS_EXIT_THRESHOLD	0.45	Sell position when price reaches this level
SIMMER_F1_PTS_MAX_POSITION_USD	5.00	Max USDC per trade
SIMMER_F1_PTS_MAX_TRADES_PER_RUN	5	Max trades per execution cycle
SIMMER_F1_PTS_SLIPPAGE_MAX	0.15	Max slippage before skipping (0.15 = 15%)
SIMMER_F1_PTS_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]

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