limit-order-book

If your cost per round trip equals the move you are trying to capture, you need 100 percent directional accuracy to break even. This lesson computes that hurdle, replaces accuracy with metrics tied to a tradeable decision, and covers the capacity and latency limits that decide whether a real edge is worth anything.

At a one-event horizon, 92 percent of mid-price labels are exactly no-change, so a model that always predicts flat scores 92 percent accuracy. This lesson computes that baseline across horizons and works through the four mechanisms that turn a genuine measurement into a number nobody can reproduce.

Convolutional and recurrent networks have been applied to order book data with published success, and the architectures encode real assumptions about the book's structure. This lesson explains what each one assumes, why the gains over simple baselines are smaller than headline numbers suggest, and where the modelling effort is better spent.

Before choosing an architecture you have to decide what the input is, and an order book offers several incompatible representations that are not equally informative. This lesson covers what each data level contains, why raw prices are the wrong features, and the representation choices that decide more than the model does.