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diagnostikon

Kalshi F1 Points Model Trader

by diagnostikon · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install kalshi-f1-points-model-trader
Description
Trades F1 Drivers Championship winner markets on Kalshi using current points standings and Monte Carlo simulation to compute win probabilities. Requires SIMM...
README (SKILL.md)

\r \r

Kalshi F1 Points Model Trader\r

\r

This is a template. \r The default signal uses static points standings and driver ratings to Monte Carlo simulate the remaining season -- remix it with live F1 API data, qualifying pace analysis, or weather-adjusted race models. \r The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.\r \r

Strategy Overview\r

\r F1 Drivers Championship markets on Kalshi price each driver's chance of winning the title. This skill runs Monte Carlo simulations using current points standings and driver skill ratings to compute fair win probabilities, then trades when the market diverges from the model.\r \r Key advantages:\r

  • Points standings are public -- no proprietary data needed\r
  • Monte Carlo captures non-linear dynamics -- points gaps, remaining races, and driver variance\r
  • Driver ratings provide edge -- market often misprices mid-field drivers\r \r

Signal Logic\r

\r

Points-Based Monte Carlo Model\r

\r

  1. Load current F1 Drivers Championship standings\r
  2. Assign driver skill ratings (affects finishing position distribution)\r
  3. Simulate remaining races 10,000 times with weighted random finishes\r
  4. Count championship wins per driver to get win probabilities\r
  5. Compare model probability to Kalshi market price\r
  6. Trade when |model - market| >= entry_edge\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

Remix Ideas\r

\r

  • Live F1 API: Replace static standings with real-time Ergast/OpenF1 API\r
  • Qualifying pace model: Weight recent qualifying gaps for more accurate finishing distributions\r
  • Constructor performance: Factor in car development trajectory\r
  • Weather/track type: Adjust driver ratings for rain races or street circuits\r \r

Risk Parameters\r

\r | Parameter | Default | Notes |\r |-----------|---------|-------|\r | Entry edge | 10% | Min model-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 | 5 | 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-points-model-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
The automaton cron is set to `null` -- it does not run on a schedule until you configure it in the Simmer UI. `autostart: false` means it won't start automatically on install.\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
All risk parameters are declared in `clawhub.json` as `tunables` and adjustable from the Simmer UI without code changes.\r
\r
| Variable | Default | Purpose |\r
|----------|---------|---------|\r
| `SIMMER_F1_PTS_ENTRY_EDGE` | `0.10` | Min divergence between model and market to trigger trade |\r
| `SIMMER_F1_PTS_EXIT_THRESHOLD` | `0.45` | Sell position when price reaches this level |\r
| `SIMMER_F1_PTS_MAX_POSITION_USD` | `5.00` | Max USDC per trade |\r
| `SIMMER_F1_PTS_MAX_TRADES_PER_RUN` | `5` | Max trades per execution cycle |\r
| `SIMMER_F1_PTS_SLIPPAGE_MAX` | `0.15` | Max slippage before skipping (0.15 = 15%) |\r
| `SIMMER_F1_PTS_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
\r
Review the source before providing live credentials if you require full auditability.\r
Usage Guidance
This skill appears internally consistent for automated trading using Simmer and Kalshi. However, before enabling live trading: 1) Audit the simmer-sdk code (GitHub/PyPI) because the skill will pass your SIMMER_API_KEY to that SDK and the SDK will orchestrate trades. 2) Treat SOLANA_PRIVATE_KEY as extremely sensitive — only use a dedicated account with minimal funds and consider using a signing service or hardware wallet instead of pasting a full private key into an environment variable. 3) Start in dry-run and test market discovery and simulation outputs; confirm the --live flag behavior. 4) Be aware the script reads optional env vars (TRADING_VENUE, AUTOMATON_MAX_BET) not declared as required; verify any automation settings you configure. 5) If you lack the ability to audit the SDK, prefer not to provide live credentials or limit them to a throwaway account.
Capability Analysis
Type: OpenClaw Skill Name: kalshi-f1-points-model-trader Version: 1.0.0 The skill implements an F1 championship trading strategy using Monte Carlo simulations to predict win probabilities and execute trades via the `simmer-sdk`. While it requires high-risk credentials such as a `SOLANA_PRIVATE_KEY` and `SIMMER_API_KEY`, these are standard for the intended purpose of automated trading on the Simmer/Kalshi platforms. The code in `trader.py` is well-documented, lacks obfuscation, and contains no evidence of data exfiltration, unauthorized remote execution, or malicious prompt injection in `SKILL.md`.
Capability Tags
cryptorequires-wallet
Capability Assessment
Purpose & Capability
Name/description, README instructions, clawhub.json, and trader.py consistently implement an F1 championship probabilistic trader that uses simmer-sdk and (optionally) a Solana key for live trades. Required artifacts (SIMMER_API_KEY, SOLANA_PRIVATE_KEY, simmer-sdk) align with a trading skill and are expected.
Instruction Scope
SKILL.md and trader.py focus on market discovery, Monte Carlo simulation, and trade execution. The code will read the declared SIMMER API key and an optional TRADING_VENUE and AUTOMATON_MAX_BET environment variables; AUTOMATON_MAX_BET and TRADING_VENUE are not listed in the top-level requires but are optional controls. The skill defaults to dry-run and only performs real trades with an explicit --live flag, which constrains accidental live execution.
Install Mechanism
This is an instruction-only skill with a pip dependency (simmer-sdk) listed in clawhub.json and SKILL.md. No downloads from unknown hosts or archive extraction are present in the package. The dependency points to a PyPI project and a GitHub repo for review.
Credentials
The skill requires SIMMER_API_KEY (trading authority) and SOLANA_PRIVATE_KEY (base58 private key) — both are reasonable for a live trading agent but are very high-sensitivity credentials. The code also reads optional env vars (TRADING_VENUE, AUTOMATON_MAX_BET) that are not listed as required; this is not malicious but worth noting. Requiring a private key is proportionate to the described live-trading functionality, but it increases risk if provided without auditing.
Persistence & Privilege
always is false and autostart is false; automaton is marked managed with an entrypoint but will not start automatically on install. The skill does not request system-wide or other-skills' config access. Default model invocation/autonomous invocation remains allowed (platform default) but is not combined with any unusual persistent privileges.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install kalshi-f1-points-model-trader
  3. After installation, invoke the skill by name or use /kalshi-f1-points-model-trader
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release
Metadata
Slug kalshi-f1-points-model-trader
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Kalshi F1 Points Model Trader?

Trades F1 Drivers Championship winner markets on Kalshi using current points standings and Monte Carlo simulation to compute win probabilities. Requires SIMM... It is an AI Agent Skill for Claude Code / OpenClaw, with 93 downloads so far.

How do I install Kalshi F1 Points Model Trader?

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

Is Kalshi F1 Points Model Trader free?

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

Which platforms does Kalshi F1 Points Model Trader support?

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

Who created Kalshi F1 Points Model Trader?

It is built and maintained by diagnostikon (@diagnostikon); the current version is v1.0.0.

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