Technical definitions for our proprietary order-flow analytics, machine learning models, and execution scanners.
A specialized scanner designed to detect spoofing and generate consistent alpha by countering spoofed orders. It identifies patterns of fake liquidity—such as unbalanced quoting, phantom depth, and cyclical cancellations—to pinpoint when manipulators are overextended.
Identifies the moment a trader places a high-contract size limit order within the bid/ask spread, signaling directional conviction. The scanner validates whether this intent translates into real price movement, filtering out noise through multi-layered confirmation.
Reads structural tension in the order book by aggregating bid-ask depth across multiple levels. It isolates authentic liquidity shifts from transient noise and quote stuffing, delivering consistent, volume-driven alpha.
A machine learning-based model that predicts the next 2 ticks of price movement based on real-time order flow imbalances and book pressure.
Identifies mean-reversion zones based on exhausted order flow, typically occurring at price extremes where delta begins to decline despite continued price push, signaling a likely reversal.