Algorithmic Financial Data Prediction: A Deep Dive into Technical Indicators

Creating an Ensemble Model to Predict RSI Values

This article aims to provide a systematic exploration of the amalgamation of Catboost and technical indicators, shedding light on its potential applications for traders and analysts. We will delve into the technical intricacies of Catboost, elucidating its practical implementation in financial engineering.

CatBoost Algorithm and the Relative Strength Index

Catboost, short for “categorical boosting,” stands out in the realm of machine learning algorithms due to its specialized focus on categorical features. Developed by Yandex, it employs a gradient boosting framework, making it particularly effective for tasks like classification and regression. Unlike traditional gradient boosting algorithms, Catboost requires minimal pre-processing of categorical data, easing the burden on practitioners.

The algorithm excels in handling complex datasets, automatically dealing with categorical variables without the need for extensive manual encoding. This feature makes Catboost an attractive choice for financial engineers and data scientists navigating datasets rich in both numerical and categorical information. As we explore further, we’ll…