Algorithmic Financial Data Prediction: A Deep Dive into Technical Indicators

Creating an Ensemble Model to Predict RSI Values

Sofien Kaabar, CFA


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 uncover how Catboost can be harnessed to predict technical indicators, providing a data-driven approach to market analysis.

On the other hand, the Relative Strength Index (RSI) is a momentum oscillator that has been a staple in technical analysis for decades. Developed by J. Welles Wilder, the RSI is designed to measure the speed and change of price movements, helping traders identify overbought or oversold conditions in a market. RSI values range from 0 to 100, with readings above 70 indicating potentially overbought conditions and readings below 30 signaling potential oversold conditions.

Traders often use the RSI to spot potential trend reversals and to confirm the strength of a prevailing trend. It provides a quantitative measure of the recent price performance of an asset, offering insights into whether it might be due for a correction or continuation.