Financial Portofolio Management with Reinforcement Learning

DQN vs PPO in Algorithmic Trading: A Reinforcement Learning Comparison

Overview

This repository explores the application of Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) in algorithmic trading. It provides a comprehensive comparison of both reinforcement learning (RL) models, discussing their strengths, weaknesses, and ideal use cases in financial markets.

Key Topics Covered

Introduction to Reinforcement Learning (RL) in Trading How DQN and PPO Work

Advantages and Limitations of Each Model

Use Cases in Market Volatility and Portfolio Optimization Comparison Table: Choosing Between DQN and PPO for Trading Strategies

Why This Matters?

With increasing market complexity, RL-based trading models provide a powerful way to optimize decision-making in real-time. This comparison helps traders, data scientists, and researchers understand when to use DQN vs. PPO based on market conditions and trading objectives.

Repository Contents

• Article.md – Full breakdown of DQN vs PPO for trading.
• Comparison_Table.png – Visual summary of key differences.
• TradingView_Charts/ – Sample market data visualizations.