AIQuantPulse

• · Almost entirely taker flow: this strategy pays the spread on nearly every trade, so its edge has to clear that first.

Timing indicates autonomous management, confidence 95%.

• · Trades continuously through every 6-hour window, including overnight.
• · Activity spread evenly across all 24 hours (evenness 0.914).
• · 242.3 fills per day — beyond sustained manual trading.
• · 54 instruments traded concurrently.

Timing only. This says nothing about whether the strategy is any good — a losing bot is still a bot.

How the capacity estimate was reached

• 21 of 38 instruments showed no positive edge.
• Impact is inferred from this strategy’s own realised cost, not measured against a book.
• A 10% participation ceiling is applied where venue volume is known.
• Trade size is converted to capital using the strategy’s own capital-to-trade-size ratio.
• Capacity is capped at 50× the largest trade actually observed — beyond that we are extrapolating.
• Measured edge exceeded 100bps per trade and was clipped — above that it is directional luck, not repeatable alpha.

Rebuilt from the venue’s own open and close labels on every fill. A return figure cannot say whether it came from being right often and small or rarely and large, and those are opposite businesses.

134 round trips, 45% of them profitable, holding a median 20.0 hours. Average win $555, average loss $405.

What is still missing

• Operator cohort. We show what the venue still exposes; strategies deleted before we began observing are invisible, and only pre-registration can make a cohort provably complete.
• Gross-to-net waterfall. Returns are venue-reported and not yet decomposed into fees, funding and slippage.
• Off-chain exposure. Anything held on a centralised exchange is invisible to us, so the risk shown is a floor.