Kalshi F1 Race Momentum Trader

> 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: hello@simmer.markets