Evolution of Systematic Trading: Why We Develop Reinforcement Learning Traders

Can AI Invest Better than Humans?

When people think of investing, they often imagine value investors like Warren Buffett, who emphasize long-term vision. But there are also legendary traders, such as Jesse Livermore or George Soros, known for their exceptional short-term decision-making and rapid judgments. Although these traders have famously used their intuition and experience to accurately predict short-term market movements, replicating their success is notoriously difficult.

Why is that? Because human intuition is frequently influenced by emotions and biases. When markets suddenly shift, fear or greed can cloud our judgment. Moreover, people tend to unconsciously overestimate their past successes or become excessively cautious following recent failures.

Then what about artificial intelligence (AI), which can make unbiased, rational decisions purely based on data?

Unlike human intuition, AI can rapidly analyze massive amounts of data, detect subtle market movements, and make consistently logical decisions. Crucially, AI never experiences fatigue, isn’t affected by psychological stress, and can respond to market changes 24 hours a day.

But is it truly as straightforward as it sounds?

AI, Reinforcement Learning, and Investment

Artificial Intelligence (AI), particularly deep learning, is typically classified into supervised learning, unsupervised learning, and reinforcement learning (though nowadays these boundaries are becoming increasingly blurred). Among these, what exactly is reinforcement learning?

Reinforcement learning is an approach where an AI learns to make increasingly better decisions through trial and error. It’s similar to how humans learn to ride a bike—by repeatedly falling and getting up again, eventually finding balance.

At its core, reinforcement learning consists of two key elements: Reward and Action. The AI continuously experiments by choosing actions in various situations, learning which actions lead to positive outcomes (rewards). For example, AlphaGo learned by playing millions of Go moves, repeatedly discovering the best moves that led to victories—the ultimate reward. Eventually, it became capable enough to surpass human champions.

This method of reinforcement learning can also be effectively applied to financial market investments. AI experiments with diverse investment strategies based on market data, analyzing the results to find strategies that maximize returns—the reward. For example, when stock prices exhibit specific patterns, the AI determines optimal moments to buy or sell, continually refining strategies through ongoing analysis.

Unlike traditional static model-based investment approaches, reinforcement learning quickly adapts and revises its strategies in response to changing market conditions. Instead of focusing on predictions like traditional quantitative models, reinforcement learning emphasizes optimization—discovering through repeated actions and reward analysis which decisions lead to the highest returns. This is especially powerful in uncertain and volatile financial markets.

Despite these advantages, applying reinforcement learning to real-world investment scenarios remains very challenging.

Challenges in Reinforcement Learning-Based Quantitative Trading

One of the biggest challenges in applying reinforcement learning to trading is the uncertainty and noise inherent in market data. Financial markets often display irregular or unexpected movements, and faulty or misleading data can lead algorithms to incorrect conclusions or poor learning outcomes.

Another significant issue is overfitting. A model overly optimized on historical data may underperform dramatically in real-world scenarios. To prevent this, reinforcement learning algorithms must continuously undergo rigorous validation and real-time updates.

Moreover, reinforcement learning often produces strategies that humans find difficult to interpret. Traditional quantitative models typically rely on mathematics, statistics, and economic theory, allowing their investment strategies to be easily explained. In contrast, reinforcement learning decisions, particularly those derived from neural networks, can appear as “black boxes,” requiring considerable effort for humans to understand and trust.

Can Reinforcement Learning-Based Trading Really Work?

Renaissance Technologies, one of America’s most successful proprietary trading firms, has consistently achieved exceptional returns over many years. Its founder, Jim Simons, was a mathematician and professor who, alongside colleagues like Leonard Baum, developed mathematical models for investing. Their primary strategy utilized Hidden Markov Models (HMM), probabilistic models designed to detect hidden states and subtle patterns in market behavior through algorithms such as Baum-Welch and Viterbi.

Though Renaissance Technologies did not explicitly use reinforcement learning, there are conceptual similarities between applying HMM and reinforcement learning to investing. Hidden Markov Models calculate probabilities of underlying states not directly observable, using algorithms like Baum-Welch or Viterbi to uncover subtle market shifts. Similarly, reinforcement learning solves Markov Decision Processes (MDP), modeling state transitions and identifying subtle market patterns often imperceptible to human observers.

Just as Renaissance Technologies achieved remarkable results using Hidden Markov Models, reinforcement learning has significant potential to deliver outstanding trading performance today. Around the globe, many researchers and financial institutions are actively developing reinforcement learning algorithms for trading, aiming to leverage this promising approach.

Introducing QMELLION

Given the rapid pace at which AI technology is evolving, AI and reinforcement learning will undoubtedly become crucial strategic tools in trading.

At QMELLION, we’re not only researching traditional trading algorithms but also pioneering AI and reinforcement learning-based strategies. We look forward to sharing our research progress and insights through this blog.