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diagnostikon

Kalshi F1 Teammate Anti Trader

by diagnostikon · GitHub ↗ · v1.0.4 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install kalshi-f1-teammate-anti-trader
Description
Trades F1 Drivers Championship markets on Kalshi using teammate anti-correlation. Teammates share the same car so their probabilities are structurally linked...
README (SKILL.md)

\r \r

Kalshi F1 Teammate Anti-Correlation Trader\r

\r

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

Strategy Overview\r

\r 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.\r \r Key advantages:\r

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

Signal Logic\r

\r

Teammate Anti-Correlation Model\r

\r

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

Teammate Pairs (2025)\r

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

Conviction-Based Sizing\r

\r

  • conviction = min(|edge| / entry_edge, 2.0) / 2.0\r
  • size = max($1.00, conviction * MAX_POSITION_USD)\r
  • Larger edge = larger position, capped at MAX_POSITION_USD\r \r

Risk Parameters\r

\r | Parameter | Default | Notes |\r |-----------|---------|-------|\r | Entry edge | 8% | Min fair-vs-market divergence to trade |\r | Exit threshold | 45% | Sell when position price reaches this |\r | Max position size | $5.00 USDC | Per market |\r | Max trades per run | 4 | Rate limiting |\r | Max slippage | 15% | Skip if slippage exceeds |\r | Min liquidity | $0 | Disabled by default |\r \r

Installation & Setup\r

\r

clawhub install kalshi-f1-teammate-anti-trader\r
```\r
\r
Requires: `SIMMER_API_KEY` and `SOLANA_PRIVATE_KEY` environment variables.\r
\r
## Cron Schedule\r
\r
Cron is set to `null` -- the skill does not run on a schedule until you configure it in the Simmer UI.\r
\r
## Safety & Execution Mode\r
\r
**The skill defaults to dry-run mode. Real trades only execute when `--live` is passed explicitly.**\r
\r
| Scenario | Mode | Financial risk |\r
|----------|------|----------------|\r
| `python trader.py` | Dry run | None |\r
| Cron / automaton | Dry run | None |\r
| `python trader.py --live` | Live (Kalshi via DFlow) | Real USDC |\r
\r
## Required Credentials\r
\r
| Variable | Required | Notes |\r
|----------|----------|-------|\r
| `SIMMER_API_KEY` | Yes | Trading authority. Treat as a high-value credential. |\r
| `SOLANA_PRIVATE_KEY` | Yes | Base58-encoded Solana private key for live trading. |\r
\r
## Tunables (Risk Parameters)\r
\r
| Variable | Default | Purpose |\r
|----------|---------|---------|\r
| `SIMMER_F1_TEAM_ENTRY_EDGE` | `0.08` | Min divergence to trigger trade |\r
| `SIMMER_F1_TEAM_EXIT_THRESHOLD` | `0.45` | Sell position when price reaches this level |\r
| `SIMMER_F1_TEAM_MAX_POSITION_USD` | `5.00` | Max USDC per trade |\r
| `SIMMER_F1_TEAM_MAX_TRADES_PER_RUN` | `4` | Max trades per execution cycle |\r
| `SIMMER_F1_TEAM_SLIPPAGE_MAX` | `0.15` | Max slippage before skipping trade |\r
| `SIMMER_F1_TEAM_MIN_LIQUIDITY` | `0` | Min market liquidity USD (0 = disabled) |\r
\r
## Dependency\r
\r
`simmer-sdk` is published on PyPI by Simmer Markets.\r
- PyPI: https://pypi.org/project/simmer-sdk/\r
- GitHub: https://github.com/SpartanLabsXyz/simmer-sdk\r
- Publisher: [email protected]\r
Usage Guidance
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.
Capability Analysis
Type: OpenClaw Skill Name: kalshi-f1-teammate-anti-trader Version: 1.0.4 The skill implements an automated trading strategy for Kalshi F1 markets, which is a high-risk financial activity. It requires the user to provide highly sensitive credentials, specifically a 'SOLANA_PRIVATE_KEY' and 'SIMMER_API_KEY', via environment variables (trader.py, SKILL.md). While the code logic appears consistent with its stated purpose and includes basic safeguards like a dry-run mode and slippage limits, the handling of raw private keys and the reliance on an external dependency (simmer-sdk) for trade execution constitute significant security and financial risks.
Capability Tags
cryptorequires-wallet
Capability Assessment
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.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install kalshi-f1-teammate-anti-trader
  3. After installation, invoke the skill by name or use /kalshi-f1-teammate-anti-trader
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.4
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v1.0.3
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v1.0.2
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v1.0.1
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v1.0.0
Initial release
Metadata
Slug kalshi-f1-teammate-anti-trader
Version 1.0.4
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 5
Frequently Asked Questions

What is 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... It is an AI Agent Skill for Claude Code / OpenClaw, with 91 downloads so far.

How do I install Kalshi F1 Teammate Anti Trader?

Run "/install kalshi-f1-teammate-anti-trader" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Kalshi F1 Teammate Anti Trader free?

Yes, Kalshi F1 Teammate Anti Trader is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Kalshi F1 Teammate Anti Trader support?

Kalshi F1 Teammate Anti Trader is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Kalshi F1 Teammate Anti Trader?

It is built and maintained by diagnostikon (@diagnostikon); the current version is v1.0.4.

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