Artificial Intelligence Drives Quantitative Investing
Artificial intelligence (AI) is not a new field, but with the rapid development of computing power, cloud resources, and open-source tools, the barrier to using machine learning (ML—a subset of AI) for investment modeling has been significantly lowered.
AI is accelerating the evolution of quantitative investing in the cryptocurrency market, creating new possibilities for this fast-growing and emerging sector. With unprecedented data processing capabilities, AI models can track the complex and dynamic relationships between crypto assets, revealing the deeper driving logic behind market behavior.
Traditional quantitative methods often rely on relatively limited market effects, such as short-term price deviations or overreactions to specific indicators, mainly focusing on brief mispricing behaviors. While these models can capture classic factors like value and momentum, their scope and adaptability are relatively limited. In contrast, AI systems can identify hundreds of high-frequency, rapidly changing potential trading signals—ranging from on-chain transaction data and token price behaviors to social media sentiment, news headlines, developer activity, and real-time investor responses to major crypto project upgrades or regulatory developments. And this is just the tip of the iceberg.
Unlike conventional machine learning models, which primarily identify linear relationships within datasets, AI excels at detecting far more complex nonlinear patterns in the crypto market. This enables a deeper understanding of the multi-variable interactions behind price fluctuations—dramatically improving both the accuracy and breadth of identifying potential trading opportunities, especially in a highly volatile and emotionally driven crypto environment.
Traditional quantitative models often assume a linear relationship between an asset’s future price trend and a specific signal. For example, an increase in the number of active addresses on a blockchain might be interpreted as a bullish signal. However, due to the complex interplay of market expectations, manipulative behavior, or news events, such singular relationships may not always hold—and may fail precisely when alpha opportunities arise.
In contrast, nonlinear machine learning models trained on historical data can extract more intricate “conditional patterns” from tens of thousands of data variables. For instance, a model might detect a complex interaction between on-chain transaction volume, developer activity, social sentiment, and commentary from a key opinion leader (KOL) to determine whether a token is likely to experience significant price movement. Another example is the model’s ability to detect potential short squeeze reversals when a cryptocurrency is heavily shorted and the market overreacts to negative news—allowing it to predict the timing and magnitude of a sharp upward price movement, something traditional signal-based models often miss.
Within the vast sea of crypto financial data, thousands of such “reflexive mechanism”-like nonlinear relationships exist, offering investors a continuous and stable source of excess returns (alpha). NITG leverages this AI-powered quantitative technology to consistently uncover new sources of value in cryptocurrency investing—providing users with a truly data-driven path to wealth generation.