Kalshi F1 Teammate Anti Trader

Trades F1 Drivers Championship markets on Kalshi using teammate anti-correlation. Teammates share the same car so their probabilities are structurally linked...

Security Scan

The code and README describe a legitimate Kalshi/Simmer trading bot that reasonably needs an API key and a Solana private key for live trading, but the package/registry metadata is inconsistent (it omits required envs and install instructions) and thus the bundle contains mismatches you should resolve before trusting sensitive credentials.

Purpose & Capability

The skill's stated purpose (automated trading on Kalshi using teammate anti-correlation) aligns with the code and SKILL.md: the script calls into simmer_sdk, constructs trade signals, and can execute live trades via Solana/DFlow. However the registry metadata at the top of the report lists no required environment variables or primary credential, while SKILL.md and clawhub.json both require SIMMER_API_KEY and SOLANA_PRIVATE_KEY. That metadata omission is an inconsistency (likely an authoring error) but it is material: a trading skill legitimately needs those secrets.

Instruction Scope

SKILL.md and trader.py limit actions to market discovery, signal generation, and (optionally) executing trades. The README emphasizes dry-run by default and requires an explicit --live flag to perform real trades. The instructions do not request unrelated system files or credentials beyond the trading API key and Solana private key, nor do they instruct exfiltration to unexpected endpoints.

Install Mechanism

There is no formal 'install spec' in the top-level registry, but SKILL.md and clawhub.json both declare a pip dependency on 'simmer-sdk' and the code imports simmer_sdk. Using pip to install simmer-sdk is a reasonable mechanism. The inconsistency is that the registry initially reported 'No install spec' and 'Required env vars: none' while the included files do declare pip and env requirements — this mismatch should be fixed. No downloads from unknown URLs or archive extraction are present.

Credentials

The skill requires SIMMER_API_KEY and SOLANA_PRIVATE_KEY (base58 private key) for live trading, which is proportionate to its purpose but represents high-value secrets. The registry metadata failing to advertise these required variables is concerning because a user could install the skill without realizing it requests private keys. The script also reads optional envs like TRADING_VENUE and AUTOMATON_MAX_BET; those are plausible but should be documented consistently. Treat the SOLANA_PRIVATE_KEY as highly sensitive.

Persistence & Privilege

The skill is not marked always:true and autostart is false in clawhub.json. It can be invoked autonomously (model invocation is not disabled), which is normal for skills. The skill does not request to modify other skills' configurations or system-wide settings. The automaton entrypoint is trader.py (managed), which is expected for an agent-run trading skill.

What to consider before installing

This skill is plausibly a real trading bot and requires two high-value secrets (SIMMER_API_KEY and SOLANA_PRIVATE_KEY) to execute live trades. Before installing or providing credentials: 1) Confirm you trust the publisher and the simmer-sdk package (review its PyPI page and GitHub repo referenced in SKILL.md). 2) Fix or ask the publisher to fix the registry metadata so required envs and pip dependency are declared consistently (current top-level metadata omitted them). 3) Only run in dry-run mode initially (default) and never pass --live until you have tested behavior and audited simmer-sdk. 4) If you must provide a private key, prefer using a dedicated/truncated test account with minimal funds and revoke/regenerate keys if anything seems off. 5) Optionally inspect simmer-sdk source and run trader.py in an isolated environment to observe network activity. The main red flag here is the inconsistency between the registry summary and the included files (environment and install requirements); that should be resolved before trusting sensitive credentials.

Kalshi F1 Teammate Anti-Correlation Trader

This is a template. The default signal uses static dominance ratios -- remix it with live head-to-head qualifying/race data for dynamic dominance estimation. The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

F1 teammates share the same car, meaning their championship probabilities are structurally anti-correlated. If Verstappen's probability rises, Lawson's should fall (they share Red Bull's probability budget). Markets are slow to adjust both sides of teammate pairs simultaneously, creating exploitable relative mispricings.

Key advantages:

• Structural constraint -- teammate probabilities must sum to roughly the team's total share
• Dominance ratios are stable -- intra-team hierarchy rarely changes mid-season
• Markets adjust asymmetrically -- one driver's price moves but teammate's lags
• Pairs trading -- natural hedge structure reduces directional risk

Signal Logic

Teammate Anti-Correlation Model

1. Define teammate pairs (same constructor)
2. Set dominance ratios (who captures what % of team total)
3. For each pair, sum their market prices = team_total
4. Compute fair split: fair_A = team_total * dominance, fair_B = team_total * (1-dominance)
5. Trade when |fair - market| >= entry_edge for either driver

Teammate Pairs (2025)

Pair	Constructor	Dominance	Team Total
Verstappen / Lawson	Red Bull	85%/15%	~36%
Leclerc / Hamilton	Ferrari	50%/50%	~20%
Russell / Antonelli	Mercedes	70%/30%	~8%
Piastri / Norris	McLaren	40%/60%	~36%

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	8%	Min fair-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-teammate-anti-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_TEAM_ENTRY_EDGE	0.08	Min divergence to trigger trade
SIMMER_F1_TEAM_EXIT_THRESHOLD	0.45	Sell position when price reaches this level
SIMMER_F1_TEAM_MAX_POSITION_USD	5.00	Max USDC per trade
SIMMER_F1_TEAM_MAX_TRADES_PER_RUN	4	Max trades per execution cycle
SIMMER_F1_TEAM_SLIPPAGE_MAX	0.15	Max slippage before skipping trade
SIMMER_F1_TEAM_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]