Kalshi Crypto Volatility Skew Trader

This is a template.
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.
The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

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.

Key advantages:

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

Signal Logic

Volatility Skew Model

Parse BTC price bin boundaries from market questions
Estimate market-implied vol via grid search over lognormal model
Compute fair bin probabilities using historical 60% vol
Compare fair probability to market price per bin
Trade when |fair - market| >= entry_edge

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

Remix Ideas

Deribit options surface: Use actual options-implied vol instead of historical
Realized vol regimes: Switch between high/low vol regimes based on recent price action
GARCH forecasting: Use GARCH(1,1) to forecast forward vol dynamically
Correlation with macro: Adjust vol for Fed meetings, CPI releases, halving proximity

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-crypto-volatility-skew-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

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.

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)

All risk parameters are declared in clawhub.json as tunables and adjustable from the Simmer UI without code changes.

Variable	Default	Purpose
`SIMMER_CRYPTO_VSKEW_ENTRY_EDGE`	`0.08`	Min divergence between fair and market to trigger trade
`SIMMER_CRYPTO_VSKEW_EXIT_THRESHOLD`	`0.45`	Sell position when price reaches this level
`SIMMER_CRYPTO_VSKEW_MAX_POSITION_USD`	`5.00`	Max USDC per trade
`SIMMER_CRYPTO_VSKEW_MAX_TRADES_PER_RUN`	`4`	Max trades per execution cycle
`SIMMER_CRYPTO_VSKEW_SLIPPAGE_MAX`	`0.15`	Max slippage before skipping (0.15 = 15%)
`SIMMER_CRYPTO_VSKEW_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: hello@simmer.markets

Review the source before providing live credentials if you require full auditability.