AAPL Triple-Barrier Direction Classifier (educational)

⚠️ This is an educational portfolio artifact, NOT a trading signal.

Under 5-fold purged k-fold cross-validation (López de Prado, AFML, Ch.7), this XGBoost classifier reaches mean accuracy ~38% on a 3-class triple-barrier label set (random baseline = 33%, p<0.05 in 3 of 5 folds). However, directional accuracy when the model picks a side is ~36% — worse than coin flip. The model is mildly informative about "will something happen vs nothing" but uninformative about "up vs down." Do not trade real money on this.

Reference-backed financial-ML pipeline: triple-barrier labeling (AFML Ch.3), fractional differentiation (Ch.5), purged k-fold CV (Ch.7), XGBoost classifier. Repo: this folder.

As-of date (YYYY-MM-DD)

Valid range: 2010-09-08 → 2024-12-30

Class probabilities

60-day price context

Headline result table (mean over 5 purged folds):

Model	Accuracy	Beat random (p<0.05)	Dir.acc when acting
Majority	35.0%	0/5 folds	N/A
SES	36.8%	2/5 folds	always abstains
ARIMA	36.8%	2/5 folds	always abstains
LSTM	35.8%	2/5 folds	33% (worse than 50%)
XGBoost	37.8%	3/5 folds	36% (worse than 50%)