financial-machine-learning

Shuffled cross-validation reports 54 percent accuracy on data containing no signal, because neighbouring samples share their futures. This lesson builds purging, embargo and sample uniqueness weights, shows the illusion scaling with label overlap and vanishing when the fix is applied, and covers why a single walk-forward path is weak evidence.

Prices are non-stationary and returns are stationary but forget everything, so the standard fix throws away the level information a model needed. This lesson builds fractional differentiation, which makes the choice a dial rather than a switch, and shows by computation that most of the memory can be kept while still passing a stationarity test.

The default label, the sign of the return over a fixed horizon, describes a trade nobody would take: no stop, no target, and a holding period chosen by the modeller rather than by the market. This lesson builds the triple-barrier method and meta-labelling, and shows with computed numbers how much of a fixed-horizon dataset is fiction.

Machine learning practice assumes independent samples, a stable data-generating process, and an environment that does not react to you. Financial data violates all three, and the result is models that validate beautifully and lose money. This lesson establishes what is actually broken before the rest of the course fixes it.