computer code for Quantitative asset management by michael robbins
email info@quantitativeassetmanagement.com for passwords
under construction
FEATURED COMPUTER CODE
BACKTESTING TAX-LOSS HARVESTING STRATEGIES
ANALYZING TRANSACTION DATA FROM 101 OF THE LARGEST ROBOADVISORS (300MB)
QUANTAMENTAL GLOBAL TACTICAL ASSET ALLOCATION (1GB)
HIGH-FREQUENCY TRANSACTION COST PREDICTION AND ANALYSIS (100GB)
OPPORTUNISTIC REBALANCING
Excerpt from the book
Missing and Minority Data. Most financial time series have a great deal of uninteresting data, such as small price changes, and little important data, such as large rallies and routs. As rare as this minority data is, the rarest and most valuable data are the minority data that look like majority data—the small price movements that warn of large ones to come.
Oversampling, undersampling, and combinations of both are common ways to manage imbalanced data. Oversampling may include random oversampling, SVM, Synthetic Minority Oversampling Technique (SMOTE), “borderline” methods that use only misclassified minority data, and Adaptive Synthetic Sampling (ADASYN), which uses a density metric.