Kalshi Crypto Volatility Skew Trader
/install kalshi-crypto-volatility-skew-trader
\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
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Volatility Skew Model\r
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- Parse BTC price bin boundaries from market questions\r
- Estimate market-implied vol via grid search over lognormal model\r
- Compute fair bin probabilities using historical 60% vol\r
- Compare fair probability to market price per bin\r
- Trade when
|fair - market| >= entry_edge\r \r
Conviction-Based Sizing\r
\r
conviction = min(|edge| / entry_edge, 2.0) / 2.0\rsize = 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
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clawhub install kalshi-crypto-volatility-skew-trader\r
```\r
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Requires: `SIMMER_API_KEY` and `SOLANA_PRIVATE_KEY` environment variables.\r
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## Cron Schedule\r
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Cron is set to `null` -- the skill does not run on a schedule until you configure it in the Simmer UI.\r
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## Safety & Execution Mode\r
\r
**The skill defaults to dry-run mode. Real trades only execute when `--live` is passed explicitly.**\r
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| 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
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## Required Credentials\r
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| 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
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## 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
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install kalshi-crypto-volatility-skew-trader - After installation, invoke the skill by name or use
/kalshi-crypto-volatility-skew-trader - Provide required inputs per the skill's parameter spec and get structured output
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.