Applying Deep Reinforcement Learning for Market Making Strategies
In the world of finance, market making plays a crucial role in maintaining liquidity and efficiency in financial markets. Market makers continuously provide buy and sell quotes for a financial instrument, thereby facilitating trading activities. However, market making comes with its own set of challenges, such as managing inventory risk and capturing spread effectively.
In this tutorial, we will explore how Deep Reinforcement Learning (DRL) can be applied to develop market making strategies that address these challenges. We will leverage the power of neural networks to learn optimal trading policies in dynamic market environments. Specifically, we will focus on managing inventory risk and maximizing spread capture using DRL techniques.
Understanding Market Making
Before we delve into the technical details of applying DRL to market making, let’s first understand the concept of market making. Market makers are entities that stand ready to buy and sell financial instruments at quoted prices. They earn profits by capturing the bid-ask spread, which is the difference between the buying (bid) and selling (ask) prices of an asset.
Market making involves two key components:
- Inventory Management: Market makers need to…
