Yeah, I’ve been tracking that. The bots are exploiting micro‑scale inefficiencies, so the usual volatility clustering is getting smoothed out. In statistical terms, the distribution is shifting from a fat‑tailed lognormal to something closer to a normal distribution, but with occasional heavy tails when a bot hit a liquidity choke point. That means your traditional VaR models might underestimate risk if you keep using old parameters. Adjust the volatility forecast to a rolling window that captures the bot‑induced mean reversion, and you’ll get a cleaner signal.

Nice plan. A shrinking window will catch the bot lull, and the skew layer will flag when the tails still bite. Keep the simulation tight, tweak the lag to the bot cycle, and we’ll have a risk model that actually keeps up with the noise. Good one.

Sounds good, let’s fire up the run and lock in that adaptive window. We'll keep the bot chatter in check and the risk signals sharp.