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 SIMMER_API_KEY and simmer-sdk. Use when you want to capture alpha from volatility skew mispricing.

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

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

bash

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: [e-mail]

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