Source: Cointelegraph
Original: “Analysis of Cryptocurrency Quantitative Trading Empowered by Artificial Intelligence (AI) (Part 1): The Evolution from Rules to Intelligence”

The History of AI and the Revolution in Finance

Since the Dartmouth Conference in 1956, artificial intelligence (AI) has evolved from simple logical reasoning to today's deep learning and natural language processing. In the financial sector, the application of AI has long surpassed traditional stock markets and has recently shone in cryptocurrency quantitative trading. The high volatility of the cryptocurrency market, its 24-hour trading characteristics, and the vast amounts of on-chain data and social media information provide a unique testing ground for AI. This article will take you through how AI has evolved from simple rule-based systems to intelligent agents capable of autonomous decision-making, redefining the future of crypto trading.

Early Rule-Based Systems - Transparent but Rigid

Rule-based quantitative trading systems (Rule-based AI) were the earliest automated decision-making paradigms applied in the cryptocurrency market. Their core feature is driving trading behavior through a set of predetermined deterministic rules (such as "buy low, sell high" thresholds). These systems use symbolic logic architecture, with a completely transparent decision-making process, and can respond to market changes in milliseconds, automatically executing buy and sell operations based on preset conditions (such as price thresholds), for example:

These systems are logically transparent and execute efficiently, but they perform poorly during extreme market volatility. Due to the static nature of their preset parameters, they struggle to adapt to new paradigms when structural changes occur in the market. The Terra/Luna ecosystem collapse in May 2022 is a typical case, during which the decoupling of the UST stablecoin triggered a liquidity black hole, leading to persistent erroneous signals from traditional technical indicators like MACD and Bollinger Bands. Rule-based systems generally fail because they cannot perceive market state transitions, requiring human intervention to recalibrate parameters and trading strategies.

At the same time, rule-based systems primarily handle structured data, such as prices and trading volumes, while the cryptocurrency market is significantly influenced by unstructured information such as social media sentiment and regulatory policies. Rule-based systems lack capabilities like natural language processing and real-time data tracking, making it difficult to effectively integrate this data, which limits their performance in sentiment-driven trading.

Breakthroughs in Deep Learning - Learning from Data

In the 2010s, the rise of machine learning (ML) and deep learning (DL) technologies enabled AI to learn complex patterns from historical data and dynamically adjust strategies. Learning-based AI systems learn from data through machine learning and deep learning algorithms, gradually improving their decision-making capabilities. Unlike rule-based systems, learning-based AI systems can adapt to market changes and handle both structured and unstructured data, thus performing well in complex market environments. Particularly in cryptocurrency trading, the high volatility and unstructured information (such as social media sentiment) pose challenges to traditional rule-based systems, while learning-based AI systems may offer better solutions. The roles of learning-based AI systems in cryptocurrency trading include:

Deep learning also addresses the shortcomings of rule-based systems in handling unstructured data (such as news and forum posts). Research shows that social media sentiment is highly correlated with Bitcoin price trends, and learning-based AI can capture these signals in real-time. Compared to rule-based systems, learning-based AI systems have several advantages. First, machine learning algorithms can dynamically adjust strategies and weights based on market changes rather than relying on static rules.

The Risk of Overfitting: The Trap of Historical Data - Overfitting refers to a phenomenon where a model performs excellently on training data but poorly on new data. This often occurs in strategies optimized based on historical data, as these strategies may over-adjust, capturing noise in the data rather than true market patterns. Due to the rapidly changing behavior patterns of cryptocurrency market participants, overfitted strategies often lead to performance degradation. For example, Gort et al. tested ten cryptocurrencies from May to June 2022, during which the market experienced two crashes. The results showed that models with less overfitting outperformed those with more overfitting in terms of returns.

Large Language Models and Intelligent Agents - The New Brain of Trading

In the 2020s, generative AI and large language models (LLM) further disrupted crypto trading. For example:

Conclusion: From Tools to Partners, the Evolution of AI

The role of AI in crypto trading has evolved from a "tool" executing fixed rules to an "intelligent partner" capable of perceiving, learning, and making decisions. In the future, with the deep integration of multi-agent systems and LLMs, AI may become the "digital neural hub" of the cryptocurrency market, providing investors with more precise risk control and yield optimization solutions.

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