Reinforcement Learning

In this article, we will discuss about Reinforcement Learning and how it can be used to design trading strategies.
 

What is Reinforcement Learning?

 

Reinforcement Learning is a fascinating concept that revolves around the idea of rewarding good decisions and penalizing bad decisions. It's like training a model to learn from its own experiences and make better decisions over time.
 

In trading, the ultimate goal is to design algorithms that can make decisions leading to long-term profitability. This is no easy task, as it often involves making short-term decisions that may not immediately seem profitable, but have the potential to yield consistent profitability over time. Here's where Reinforcement Learning comes into play. To illustrate this, let’s take the example of Amazon stock.

 

 

Reinforcement Learning: Capturing Trends in Changing Markets

 

The graph below shows Amazon's share price between 2015 and 2019. 

 

During this period, the price remained relatively stable from late 2018 to the beginning of 2020. Many people would assume that a mean-reverting strategy would be more effective in this situation.
 

 

However, starting in early 2020, the price of Amazon's shares started to increase and subsequently followed a trend. Therefore, if a mean-reverting strategy had been employed from the beginning of 2020, it would have resulted in losses! Traders who primarily focused on the mean-reverting market conditions of the previous year might have exited the market when the price started to trend.

 

On the other hand, if someone had taken a long position and held onto the stock, it would have proven beneficial in the long run. By training a reinforcement learning model on price patterns from 2016 to the beginning of 2020, it can gain a broader perspective and potentially hold onto a stock for significant profits in the future.

 

Elements that make Reinforcement Learning a game-changer in trading 

 

Have you ever played a game where you had to make decisions based on what was happening around you? Maybe you had to choose between two paths in a game or decide which move to make in a board game. Well, that's kind of what deep reinforcement learning for trading is like!

 

The agent or ML model has to make decisions based on what's happening in the market (the game board). The agent gets points (called "rewards" or "penalties") based on whether the decisions it makes lead to making money or losing money.

 

Just like in a game, the more the agent plays and learns from its mistakes, the better it gets at making decisions. It learns to look for certain patterns or clues in the market to help it make better decisions. Here are some of the common jargons used in RL:

 

• Time-based exit: Set a fixed holding period for your trades to capture short-term movements in the market, such as a few days or a week. This would allow you to capture short-term movements in the market based on changes in sentiment.
• Profit-based exit: Set a profit target for each trade, based on your expected returns from the sentiment analysis. Once the price reaches this target, you would exit the trade and take your profits.
• Stop-loss exit: Set a stop-loss level for each trade, based on your risk tolerance. This would limit the losses if the sentiment reverses.
• Reversal-based exit: Use a reversal signal to exit your trades when sentiment changes from positive to negative or vice versa, to capture trends in the market and avoid losses due to sudden sentiment changes.

 

Concepts for each of the above and Python code are covered in detail in the Deep Reinforcement Learning course. Take a free preview to learn more about it.

 

How do we design reward functions in Reinforcement Learning-based trading systems? 

 

Well, it's time to think beyond simple profits! We can explore alternative metrics like profit-per-tick or the renowned Sharpe Ratio, offering a comprehensive evaluation of our trading performance. We can even incorporate penalties for the model for staying in trades for too long, preventing undesirable behaviours and ensuring our strategy remains finely tuned.  

 

Challenges of using Reinforcement Learning

 

Reinforcement Learning does face a few challenges such as type 2 chaos and noise in financial data. Type 2 chaos introduces unpredictability as minute changes in starting conditions can lead to entirely different outcomes, influenced by the observer's actions.

 

Additionally, financial time-series data contains noise, making it difficult to distinguish true signals from random fluctuations. Adapting to changing market regimes can result in false signals and poor performance during convergence. Overcoming these requires robust strategies, noise-filtering, and adaptability to varying market conditions.

 

Despite its challenges, Reinforcement Learning provides a promising approach to navigating the complexities of trading and achieving consistent success in dynamic financial markets. The beauty of Reinforcement Learning is that it can be used to learn from historical data and apply those lessons to new situations. This means that the agent can continuously improve its performance as it trades, without the need for human intervention.

To learn more about Reinforcement Learning models and how to create your own reinforcement learning trading strategies, you can check out the free preview of the Deep Reinforcement Learning course.

 

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