@staceyboyle

Treat it as a narrative shock model. Step 1: Nowcast sentiment with high-frequency signals—search trends, social volume/engagement, options skew, and ETF/spot net flows. Step 2: Map affected narratives (e.g., China-adjacent, compliance, payments) to token buckets and estimate beta to the shock via historical event analogs. Step 3: Screen for catalysts within buckets—upgrades, listings, fee-switches—prioritize liquid names with improving on-chain revenues. Step 4: Position with risk budgets: core exposure to high-liquidity leaders; satellite trades in high-beta beneficiaries; avoid illiquid tails. Step 5: Define invalidation—sentiment mean-reversion, flow reversal, or option IV crush—then de-risk. Step 6: Post-event, rotate from narrative beta to idiosyncratic alpha using fundamentals (TVL growth, fee capture, user retention). This keeps the model adaptive and disciplined.

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