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diagnostikon

Kalshi Crypto Volatility Skew Trader

by diagnostikon · GitHub ↗ · v1.0.5 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install kalshi-crypto-volatility-skew-trader
Description
Trades Bitcoin price bin markets on Kalshi by comparing market-implied volatility to BTC historical ~60% annualized vol using a lognormal model. Requires SIM...
README (SKILL.md)

\r \r

Kalshi Crypto Volatility Skew Trader\r

\r

This is a template. \r The default signal uses BTC historical annualized vol (~60%) to compute fair bin probabilities via lognormal model -- remix it with options-implied vol surface, realized vol regimes, or GARCH models. \r The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.\r \r

Strategy Overview\r

\r Bitcoin price bin markets on Kalshi imply a probability distribution over future BTC prices. This skill compares that implied distribution to a lognormal model calibrated on BTC's historical ~60% annualized volatility. When the market implies a different vol, the bins are mispriced.\r \r Key advantages:\r

  • Historical vol is well-documented -- BTC trailing 1-year vol has consistently averaged ~60%\r
  • Lognormal model is standard -- same framework used by options traders worldwide\r
  • Vol skew detection -- estimates implied vol from market prices and identifies directional skew\r
  • Bin-level edge -- finds the specific bins most mispriced by the vol mismatch\r \r

Signal Logic\r

\r

Volatility Skew Model\r

\r

  1. Parse BTC price bin boundaries from market questions\r
  2. Estimate market-implied vol via grid search over lognormal model\r
  3. Compute fair bin probabilities using historical 60% vol\r
  4. Compare fair probability to market price per bin\r
  5. Trade when |fair - 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

  • Deribit options surface: Use actual options-implied vol instead of historical\r
  • Realized vol regimes: Switch between high/low vol regimes based on recent price action\r
  • GARCH forecasting: Use GARCH(1,1) to forecast forward vol dynamically\r
  • Correlation with macro: Adjust vol for Fed meetings, CPI releases, halving proximity\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-crypto-volatility-skew-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_CRYPTO_VSKEW_ENTRY_EDGE` | `0.08` | Min divergence between fair and market to trigger trade |\r
| `SIMMER_CRYPTO_VSKEW_EXIT_THRESHOLD` | `0.45` | Sell position when price reaches this level |\r
| `SIMMER_CRYPTO_VSKEW_MAX_POSITION_USD` | `5.00` | Max USDC per trade |\r
| `SIMMER_CRYPTO_VSKEW_MAX_TRADES_PER_RUN` | `4` | Max trades per execution cycle |\r
| `SIMMER_CRYPTO_VSKEW_SLIPPAGE_MAX` | `0.15` | Max slippage before skipping (0.15 = 15%) |\r
| `SIMMER_CRYPTO_VSKEW_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 to implement the trading strategy it claims, but take care before providing live credentials. Actionable steps: - Do not supply SOLANA_PRIVATE_KEY or SIMMER_API_KEY to run dry-run/testing — the skill supports dry-run mode (no trades) by default; use that first. - Before giving live credentials, review the simmer-sdk package source (linked in SKILL.md) and the rest of trader.py (the file shown is truncated) to ensure no hidden network exfiltration or unexpected behavior. - Prefer using a limited/restricted account or signing service rather than a full private key in an environment variable; if you must use a key, create a dedicated account with minimal funds and withdrawal limits. - Note the registry metadata is inconsistent (it claims no required env vars). Treat that as a red flag and ask the publisher to correct the metadata. - Test thoroughly in paper mode, monitor logs, and limit MAX_POSITION_USD while validating behavior. If you need higher assurance, request a complete code audit or a signed/reproducible release of simmer-sdk and this skill.
Capability Tags
cryptorequires-wallet
Capability Assessment
Purpose & Capability
The name/description (Kalshi Bitcoin bin-market volatility-skew trader) align with the included code: trader.py uses simmer_sdk to discover Kalshi markets, compute implied vs historical vol, and optionally execute trades. However the registry metadata at the top of the evaluation incorrectly lists 'Required env vars: none' while the SKILL.md, clawhub.json and trader.py clearly require SIMMER_API_KEY and SOLANA_PRIVATE_KEY — an incoherence that should be corrected.
Instruction Scope
SKILL.md instructions and the script stay within trading scope: market discovery, model computation, and trade execution. The instructions explicitly default to dry-run and require an explicit --live flag to trade. There is no evidence in SKILL.md or visible code of unrelated data collection (no shell history reading or filesystem scraping).
Install Mechanism
This is instruction/code-only with no opaque remote installer. Dependencies are a PyPI package (simmer-sdk) declared in clawhub.json and SKILL.md, which is a reasonable dependency for a trading SDK. You should still review the simmer-sdk package source before trusting it with live credentials as the SKILL.md itself recommends.
Credentials
The skill requires two high-value secrets: SIMMER_API_KEY (API authority) and SOLANA_PRIVATE_KEY (base58 private key for signing live trades). Those are proportionate to live trading functionality, but they are highly sensitive. The registry metadata falsely reported no required env vars — that mismatch increases risk because users may not expect to supply a private key. Recommend using restricted accounts, read-only API keys for dry-run, or managed signing (if available) instead of pasting a full private key into an env var.
Persistence & Privilege
always:false and autostart:false; the skill is not forced into every agent run. Automaton entrypoint is provided but autostart is disabled. The skill can be invoked autonomously by the agent (default) which is normal for skills; this is not combined with 'always:true' or other unusual privileges.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install kalshi-crypto-volatility-skew-trader
  3. After installation, invoke the skill by name or use /kalshi-crypto-volatility-skew-trader
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.5
Rescan attempt 5
v1.0.4
Rescan attempt 4
v1.0.3
Rescan attempt 3
v1.0.2
Rescan attempt 2
v1.0.1
Rescan attempt 1
v1.0.0
Kalshi Crypto Volatility Skew Trader 1.0.0 — initial release. - Compares Kalshi BTC price bin market-implied volatility vs. historical ~60% annualized BTC vol using a lognormal model. - Executes trades where market prices diverge significantly from model fair probabilities, capturing alpha from volatility skew mispricing. - Includes risk parameters for entry/exit edge, max position/trades, slippage, and liquidity limits; all tunable in the UI. - Defaults to paper trading for user safety; live trading requires explicit `--live` flag and credentials. - Pluggable logic allows for advanced users to remix/improve the volatility model and alpha signal.
Metadata
Slug kalshi-crypto-volatility-skew-trader
Version 1.0.5
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 6
Frequently Asked Questions

What is Kalshi Crypto Volatility Skew Trader?

Trades Bitcoin price bin markets on Kalshi by comparing market-implied volatility to BTC historical ~60% annualized vol using a lognormal model. Requires SIM... It is an AI Agent Skill for Claude Code / OpenClaw, with 105 downloads so far.

How do I install Kalshi Crypto Volatility Skew Trader?

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

Is Kalshi Crypto Volatility Skew Trader free?

Yes, Kalshi Crypto Volatility Skew Trader is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Kalshi Crypto Volatility Skew Trader support?

Kalshi Crypto Volatility Skew Trader is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Kalshi Crypto Volatility Skew Trader?

It is built and maintained by diagnostikon (@diagnostikon); the current version is v1.0.5.

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