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