Kalshi F1 Race Momentum Trader

Trades F1 Drivers Championship markets on Kalshi using recent race results weighted by recency. Hot streaks boost championship probability, cold streaks redu...

This is a template. The default signal uses static recent results -- remix it with live F1 API data for automatic momentum recalculation after each race weekend. The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Recent form matters in F1. A driver on a hot streak (wins, podiums in the last 3 races) has demonstrably higher championship probability than their season-long average suggests. Markets are slow to fully price in momentum shifts -- they anchor to pre-season expectations and lag behind recent performance changes.

Key advantages:

• Recency bias is real and quantifiable -- last 3 races predict next-race performance better than season average
• Markets anchor to narratives -- "Verstappen always wins" persists even during cold streaks
• Weighted recency -- most recent race gets 3x weight, capturing acceleration/deceleration in form
• Hot/cold classification -- simple trend labels (HOT/COLD/FLAT) identify tradeable regimes

Signal Logic

Momentum Model

1. Collect last 3 race results per driver (position finished)
2. Convert positions to performance scores: P1=1.0, P20=0.0
3. Apply recency weights: [3x, 2x, 1x] for [most recent, second, third]
4. Compute momentum score: normalized to [-1.0, +1.0]
5. Adjust base probability: adjusted = base + momentum * SCALE * base
6. Compare adjusted probability to Kalshi market price
7. Trade when |adjusted - market| >= entry_edge

Momentum Categories

Momentum Score	Category	Meaning
> +0.3	HOT	Driver outperforming, probability should be higher
-0.3 to +0.3	FLAT	Stable form, no momentum edge
< -0.3	COLD	Driver underperforming, probability should be lower

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

Installation & Setup

clawhub install kalshi-f1-race-momentum-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_RACEMOM_ENTRY_EDGE	0.10	Min divergence to trigger trade
SIMMER_F1_RACEMOM_EXIT_THRESHOLD	0.45	Sell position when price reaches this level
SIMMER_F1_RACEMOM_MAX_POSITION_USD	5.00	Max USDC per trade
SIMMER_F1_RACEMOM_MAX_TRADES_PER_RUN	5	Max trades per execution cycle
SIMMER_F1_RACEMOM_SLIPPAGE_MAX	0.15	Max slippage before skipping trade
SIMMER_F1_RACEMOM_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]