Financial-data-science

This blog post focuses on a novel way to decompose to signal and noise.

Many people use Time bars to make sense of financial market data, but they're not the best choice. Why?

Differentiation to an integer degree is used to make a series stationary. However, fractional differentiation allows the exponent to be a real number. This helps to preserve memory.

To train a machine learning model, we usually need a labeled dataset. In the world of finance, this involves creating a matrix of features, $X$, and an array of labels or values, $y$. In this blog, we'll delve into various methods of labeling financial data.

You might have noticed that many financial models rely on the assumption that data points are independent and identically distributed (IID). However, this is often not the case in real-world financial applications.