Creating Powerful AI Trading Systems: Insights from Denny’s Blog
Introduction:
Introduction: Applying Reinforcement Learning to Financial Markets
In this introductory post, I will share some high-level insights and lessons I have learned over the past few years while building and testing trading systems. While the focus has been on financial markets, specifically the crypto markets, the principles discussed here can be applied to any market.
I will challenge the notion that it is impossible to beat the market consistently, even without insider information, as suggested by the Efficient Market Hypothesis. Academic models like the EMH may have their merits in theory, but in the real world, they often fall short. Profitable systems can be built that consistently outperform the market, as I have experienced myself.
To understand markets, it is more helpful to adopt a Game Theory or Reinforcement Learning perspective rather than purely economic theories. Markets are complex systems comprised of various participants with different objectives and strategies. Recognizing that the market is an emergent property of these agents provides a fresh perspective and allows for the development of successful trading systems.
When it comes to trading, winning is relative, not absolute. It is not about being the best in an absolute sense, but rather about outperforming other players. The time horizon also plays a crucial role, as agents optimize over different periods. Thus, it is possible to win in the market, even if you are not the best player, by exploiting the weaknesses of others.
Unlike building software for products, trading infrastructure requires a different approach. It is important to focus on relative performance and seek opportunities to differentiate from competitors. While widely adopted technologies offer stability and security in other domains, in trading, they may limit your edge. Therefore, building specialized and non-generic systems tailored to the unique needs of trading can provide a significant advantage.
While machine learning can enhance trading strategies, it is not a magic bullet. The field has become commoditized, making it difficult to gain a significant edge solely based on the choice of algorithms. Instead, other factors, such as market selection, infrastructure, data collection, and low latencies, play a crucial role in achieving trading success.
To truly beat the market, it is essential to consider all these factors holistically and make deliberate decisions. By thinking outside the box and exploring unconventional markets, building efficient infrastructure, and paying attention to all the details, it is possible to gain a competitive advantage and consistently outperform the market.
In the following posts, I will delve deeper into each of these areas, sharing my experiences and insights. Stay tuned for more!
Full Article: Creating Powerful AI Trading Systems: Insights from Denny’s Blog
Applying Reinforcement Learning to Financial Markets: Lessons Learned
Introduction
Two years ago, I explored the idea of using Reinforcement Learning to predict and profit from financial markets. Since then, I have gained valuable experience and insights from building multiple trading systems. In this post, I will share some of the key takeaways from my journey.
Building Trading Systems
Over the past few years, I have developed four and a half trading systems. The first one was unsuccessful in generating profits. The second system was abandoned early on because it proved to be unworkable. The third system faced political challenges and had to be abandoned as well. However, the fourth system performed exceptionally well for 12-18 months, consistently earning 1-2 full-time salaries with a relatively small capital investment. Eventually, the profits started to decline, leading me to explore other opportunities. Despite lacking the motivation to build a new system, I learned valuable lessons from this experience.
The Role of Economics in Trading
The Efficient Market Hypothesis (EMH) is a prevailing theory in finance that suggests it is impossible to consistently beat the market. However, I have come to realize that academic theories like the EMH may not always apply to the real world. In practice, it is possible to build profitable systems that outperform the market without relying on insider information. While the market may eventually adjust and eliminate these opportunities, it often takes a considerable amount of time. Traders can adapt their strategies, continually seeking new opportunities and adjusting to the evolving market conditions. This was the case with my own trading system, which experienced decreasing profits over time but still remained profitable for more than a year.
Viewing Markets as Multiagent Problems
To better understand financial markets, I find it helpful to adopt a Game Theory or Reinforcement Learning perspective. I believe that the market itself is an emergent property resulting from the interaction of various agents, each with their own objectives. Traders may have different goals, such as selling shares at a favorable price, engaging in low-risk arbitrage strategies, predicting short-term movements based on news or charting patterns, or speculating based on fundamentals. Agents can be human traders, algorithmic traders, or a combination of both. By recognizing the dynamic nature of the market and understanding the differing time horizons of participants, traders can focus on outperforming specific competitors rather than trying to beat the market as a whole.
Winning in Relative Terms
Unlike many technology problems where absolutes matter, trading is all about relative performance. A trader can achieve success by simply being better than their competitors, even if they are not the best in absolute terms. For example, having a more efficient website than a competitor, even if it takes 10 seconds to load, can still be considered a win if the competitor’s website takes longer. Similarly, using suboptimal data may be acceptable if the alternative is even worse. Trading fees can also be justifiable if they are the industry norm. Understanding this relative perspective is crucial in building trading software that focuses on outperforming competitors rather than achieving absolute perfection.
Trading Infrastructure Considerations
When it comes to trading infrastructure, the conventional approach of relying on widely adopted, robust frameworks may not always be the best strategy. While these technologies offer stability and minimize risk in product-based companies, they do not necessarily provide a competitive edge in trading. Developing specialized trading infrastructure can offer opportunities to outperform competitors. For instance, by building hybrid cloud systems, traders can reduce latency, while using efficient binary data formats can lead to faster iteration and prediction times. In addition, creating custom integrations with exchanges can provide access to specific order types or valuable exchange-specific information. By specializing and customizing trading systems, traders can gain a significant edge in the market.
The Role of Machine Learning in Trading
While many traders believe that building a smarter prediction model using Machine Learning is the key to success, I have found that Machine Learning itself is highly commoditized. State-of-the-art models are readily available for download and implementation. Unless a trader is at the forefront of groundbreaking research, training a better model alone is unlikely to provide a significant edge. In order to succeed, traders must still invest in robust infrastructure, quality data collection, and low-latency execution. Neglecting these essential components can undermine the potential of even the best Machine Learning model.
Conclusion
Building profitable trading systems requires a combination of various factors, including specialized infrastructure, rigorous data collection, and efficient execution. While considering economic theories like the Efficient Market Hypothesis can be useful, it is important to recognize their limitations in the real world. By adopting a Game Theory or Reinforcement Learning perspective and focusing on outperforming specific competitors, traders can navigate the complexities of financial markets and increase their chances of success.
Summary: Creating Powerful AI Trading Systems: Insights from Denny’s Blog
In this post, the author reflects on their experience applying reinforcement learning to financial and crypto markets. They discuss the Efficient Market Hypothesis and how academic models may not always apply in the real world. Instead, they propose thinking about markets from a game theory or reinforcement learning perspective. The author emphasizes the importance of relative performance in trading, where even if you’re not the best, you can still win if your competition is worse. They also discuss the impact of trading infrastructure and the potential edge in building highly specialized systems. Finally, they address the misconception that a smarter machine learning model alone can provide a significant edge in trading, highlighting the importance of other factors like market selection, infrastructure, and data quality.
Frequently Asked Questions:
Q1: What is deep learning?
A1: Deep learning is a subset of machine learning that mimics the workings of the human brain’s neural networks to process and analyze complex data. It involves training artificial neural networks with multiple layers to learn and extract meaningful patterns or representations from data, enabling the system to make accurate predictions or classifications.
Q2: How does deep learning differ from traditional machine learning?
A2: Unlike traditional machine learning algorithms that often require feature engineering or manual extraction of relevant features, deep learning automatically learns and discovers the essential features directly from raw data. Deep learning models, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), are able to find complex patterns and relationships in large datasets with minimal human intervention.
Q3: What are the practical applications of deep learning?
A3: Deep learning has found numerous applications across various domains. These include computer vision (e.g., object detection, image recognition), natural language processing (e.g., speech recognition, language translation), healthcare (e.g., disease diagnosis, drug discovery), autonomous systems (e.g., self-driving cars, robotics), finance (e.g., stock market prediction, fraud detection), and many more. Deep learning’s versatility makes it a powerful technology with vast potential.
Q4: What are some common challenges faced in deep learning?
A4: Deep learning models often require large amounts of labeled data for training, which can be time-consuming and costly to gather. Another challenge is the need for significant computational resources, as training complex deep learning architectures may demand powerful GPUs or dedicated hardware. Additionally, preventing overfitting, selecting optimal model architectures, and interpretability of deep learning outputs can present additional challenges that researchers and developers need to address.
Q5: What are the future prospects of deep learning?
A5: Deep learning is a rapidly evolving field with exciting prospects. As technology advances and computational resources become more accessible, deep learning will likely continue to revolutionize industries by improving existing applications and enabling the creation of innovative ones. Smart personal assistants, autonomous vehicles, advanced healthcare diagnostics, and personalized marketing are just a glimpse of what the future holds. Deep learning’s potential to find patterns in complex data is unlocking new possibilities and reshaping the way we interact with technology.
