./mdx_sources/FinancialBars/financial_bars.mdx

What are the numerous sorts of bars and how do they differ? What is the purpose of information-driven bars? 

What's the distinction between organized and unstructured data? We will learn how to work with unstructured financial data and then transform it into a structured dataset that can be used by $mathrmML$ algorithms. In general, you should avoid consuming someone else's processed dataset because you will most likely find what someone else already knows or will figure out shortly. Ideally, your starting point will be a collection of unstructured, raw data that you will analyse in order to generate relevant characteristics.

Financial Bars

./mdx_sources/CrossValidation/cross_validation.mdx

CV is yet another instance where standard ML techniques fail when applied to financial problems. Overfitting will take
place, and CV will not be able to detect it.

Cross Validation

./mdx_sources/Ensemble/ensemble_learning.mdx

What makes Ensemble Methods effective, and how to avoid common errors that lead to their misuse in finance.place, and CV will not be able to detect it.

Ensemble Learning

What are the numerous sorts of labelling techniques and how they differ?

We spoke about how to create a $X$-dimensional matrix of financial variables from an unstructured dataset. Unsupervised learning algorithms can learn patterns from that matrix $X$, such as whether or not it has hierarchical clusters. Supervised learning techniques, on the other hand, require that the rows in $X$ be associated with an array of labels or values $y$, so that those labels or values may be predicted on unseen feature samples. We'll talk about how to classify financial data in this section.

Labeling

What are the numerous sorts of labelling techniques and how they differ?

Sat Mar 05 2022

Labeling

TODO