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Introduction

In recent years, there has been a surge of interest in applying deep reinforcement learning techniques to solve complex problems in various domains. One such domain where deep reinforcement learning has shown promising results is in market making. Market making is a crucial activity in financial markets, where market makers provide liquidity by continuously quoting both buy and sell prices for a particular asset. The goal of a market maker is to profit from the bid-ask spread while maintaining a balanced inventory of the asset.

Deep reinforcement learning, a combination of deep learning and reinforcement learning, offers a powerful framework for training agents to make optimal decisions in dynamic and uncertain environments. By learning from interactions with the market, a deep reinforcement learning agent can adapt its trading strategy to changing market conditions and maximize profit.

This thesis aims to explore the application of deep reinforcement learning in market making and investigate its effectiveness in improving market making strategies. The following sections provide a detailed overview of the background of the study, problem statement, objectives, limitations, scope, significance of the study, structure of the thesis, and definition of key terms.

1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of Market Making
2.2 Traditional Market Making Strategies
2.3 Reinforcement Learning in Finance
2.4 Deep Reinforcement Learning
2.5 Deep Reinforcement Learning in Finance
2.6 Market Making using Deep Reinforcement Learning
2.7 Previous Studies on Market Making
2.8 Challenges and Limitations
2.9 Gaps in Existing Literature
2.10 Summary

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Model Architecture
3.4 Training Process
3.5 Evaluation Metrics
3.6 Performance Measures
3.7 Hyperparameter Tuning
3.8 Validation and Testing
3.9 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Performance of Deep Reinforcement Learning Agent
4.2 Comparison with Traditional Market Making Strategies
4.3 Impact of Hyperparameter Tuning
4.4 Robustness of the Model
4.5 Interpretability of the Model
4.6 Real-world Implementation Considerations
4.7 Regulatory Compliance
4.8 Market Dynamics
4.9 Future Research Directions

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Implications for Practice
5.4 Limitations and Future Research
5.5 Conclusion

Thesis Overview

Deep reinforcement learning has gained significant attention in recent years for its ability to solve complex decision-making problems in various domains. One such domain where deep reinforcement learning has shown promise is in market making, a crucial activity in financial markets. This thesis aims to explore the application of deep reinforcement learning in market making and evaluate its effectiveness in optimizing market making strategies.

The literature review provides an overview of traditional market making strategies, reinforcement learning in finance, and deep reinforcement learning. It also discusses previous studies on market making using deep reinforcement learning, highlighting the challenges and limitations in existing literature. The research methodology outlines the design, data collection, model architecture, training process, evaluation metrics, and ethical considerations involved in the study.

The discussion of findings evaluates the performance of the deep reinforcement learning agent in market making and compares it with traditional strategies. It also discusses the impact of hyperparameter tuning, model robustness, interpretability, real-world implementation considerations, regulatory compliance, and future research directions. The conclusion summarizes the findings, contributions to the field, implications for practice, limitations, and suggestions for future research.

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