Synthetic Data Generation (Part-1) - Block Bootstrapping

Synthetic Data Generation (Part-1) - Block Bootstrapping

Introduction

Data is at the core of quantitative research. The problem is history only has one path. Thus we are limited in our studies by the single historical path that a particular asset has taken. In order to gather more data, more asset data is collected and at higher and higher resolutions, however the main problem still exists; one historical path for each asset.

Read More

Labeling and Meta-Labeling Returns for ML Prediction

Labeling and Meta-Labeling Returns for ML Prediction

This post focuses on Chapter 3 in the new book Advances in Financial Machine Learning by Marcos Lopez De Prado.  In this chapter De Prado demonstrates a workflow for improved return labeling for the purposes of supervised classification models. He introduces multiple concepts but focuses on the Triple-Barrier Labeling method, which incorporates profit-taking, stop-loss, and holding period information, and  also meta-labeling which is a technique designed to address several issues.

Read More