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Exploration of ML applications in finance, drawing inspiration and summarizing the currently available and relevant literature. The objective is to deepen my understanding of key concepts, re-implement them with improvements, and explore alternative, non-widely used frameworks.

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roodriigoooo/Machine-Learning-in-Finance-Labeling-Guide

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Machine-Learning-in-Finance-Exploration

Project Overview

This repository is a collection of concepts and their practical implementation from the ML Applied to Finance field, inspired mainly by key literature and recent prominent works such as Advances in Financial Machine Learning and Artificial Intelligence in AI by Marcos López de Prado and Yves Hilpisch.

Project Goals

  1. Understand and Apply: Learn key ML techniques and their peculiarities when they are applied to finance. Replicate the implementations proposed by the available literature.

  2. Re-explain and Improve: Provide more digestible yet detailed explanations for each concept, while suggesting improvements or modifications when appropriate.

  3. Explore New Frameworks: Offer alternative implementations using frameworks like PyTorch and other non-commonly used frameworks in the domain of finance.

  4. Real-life Applications: Apply the explored concepts to real-world financial scenarios to demonstrate practical value.

Projects

  1. Labeling in Finance: A Guide.
  2. Asset Allocation Using Machine Learning: Hierarchical Risk Parity
  3. Applications of Encoded Price Series Entropy: Forecasting Market Efficiency

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Exploration of ML applications in finance, drawing inspiration and summarizing the currently available and relevant literature. The objective is to deepen my understanding of key concepts, re-implement them with improvements, and explore alternative, non-widely used frameworks.

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