Sequence Models for Trade and Quote Arrival

A simple model predicting the next trade's price impact might feed it a snapshot of current features, bid-ask spread, order book depth, recent volume, treating each moment as independent of the ones before it. But order flow has memory: a burst of aggressive buying tends to be followed by more of the same for a short while, and the shape of recent quote and trade history often predicts what comes next better than any single snapshot does. Sequence models, recurrent neural networks, or more recently transformer-style attention models, take a whole window of recent trades and quotes as input, letting the model learn patterns in how that sequence unfolds rather than compressing it down to a handful of summary statistics first.

The tradeoff is cost and data hunger: sequence models need much more training data and computation than a snapshot-based model, and the extra complexity only pays off if the underlying process really does have meaningful short-term memory worth capturing.

A model fed the last 50 trades in sequence can pick up on a building imbalance of buyer-initiated trades that a model fed only the current bid-ask spread and last trade price would completely miss.