execution

Execution costs are small numbers buried in large noise, so distinguishing a good desk from a lucky one takes more data than most institutions have. This lesson covers what transaction cost analysis can establish, the reversion test that detects information leakage, and what happens to any measure once people are paid on it.

A schedule says how much to trade and when. It says nothing about how each slice is sent, and that choice determines much of the realised cost. This lesson covers the standard algorithm families and what each one's benchmark actually rewards, then the placement decisions underneath: passive against aggressive, displayed against hidden, and which venue.

Trading fast costs impact. Trading slowly exposes the order to drift. Neither can be minimised without worsening the other, so the schedule is an optimisation with a parameter that encodes urgency. This lesson builds that trade-off, derives the shape of the resulting trajectory, and identifies what the model cannot see.

The cost of a trade is not the commission, and it is not the spread. It is the gap between the return the decision would have produced on paper and the return the account actually got. This lesson builds that measure, decomposes it into four sources, and shows why the largest component is often the trade nobody made.

The edge that survives statistics still has to survive trading. Spread, market impact and the square-root law, why every strategy has a capital ceiling, and the research protocol that makes a backtest worth believing.