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#

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#

ParameterDefaultNotesEntry edge8%Min fair-vs-market divergence to tradeExit threshold45%Sell when position price reaches thisMax position size$5.00 USDCPer marketMax trades per run4Rate limitingMax slippage15%Skip if slippage exceedsMin liquidity$0Disabled 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.

ScenarioModeFinancial riskpython trader.pyDry runNoneCron / automatonDry runNonepython trader.py --liveLive (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#

VariableRequiredNotesSIMMER_API_KEYYesTrading authority. Treat as a high-value credential.SOLANA_PRIVATE_KEYYesBase58-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.

VariableDefaultPurposeSIMMER_CRYPTO_VSKEW_ENTRY_EDGE``0.08Min divergence between fair and market to trigger tradeSIMMER_CRYPTO_VSKEW_EXIT_THRESHOLD``0.45Sell position when price reaches this levelSIMMER_CRYPTO_VSKEW_MAX_POSITION_USD``5.00Max USDC per tradeSIMMER_CRYPTO_VSKEW_MAX_TRADES_PER_RUN``4Max trades per execution cycleSIMMER_CRYPTO_VSKEW_SLIPPAGE_MAX``0.15Max slippage before skipping (0.15 = 15%)SIMMER_CRYPTO_VSKEW_MIN_LIQUIDITY``0Min 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.