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)

| 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: hello@simmer.markets