Although both quantitative trading and artificial intelligence trading involve using technological methods for making trading decisions, they also have some drawbacks. Here are some weaknesses of quantitative trading compared to artificial intelligence trading:

Dependence on historical data: Quantitative trading typically relies on the analysis of historical data and model building. Therefore, in emerging markets or markets experiencing significant economic changes, quantitative trading may not be as flexible in adapting as artificial intelligence trading.

Lack of subjective judgment: Quantitative trading primarily relies on rules and algorithms to make trading decisions, lacking the intuition and subjective judgment of human traders. This can sometimes result in an inability to capture certain non-regular market sentiments or events, leading to instability in trading strategies.

Sensitivity to data quality: The results of quantitative trading heavily depend on the accuracy and reliability of the historical data used. If there are errors or missing data, or if the data cannot accurately reflect the current market conditions due to market changes, it can negatively impact the success of trading strategies.

High initial costs: Quantitative trading requires establishing and maintaining a large amount of technological infrastructure, including high-performance computers, data storage and processing systems, etc. These facilities require significant capital investment and expertise to maintain, resulting in high initial costs.

Sensitivity to model risk: Quantitative trading models are typically built based on historical data, which may lead to inaccuracies and instability in the investment process for investment targets with limited market history, such as the emergence of new markets like cryptocurrencies. Quantitative trading may lose opportunities due to this deficiency.

With the advancement of technology, the application of artificial intelligence has had a profound impact on quantitative trading. Quantitative trading is a trading strategy that uses mathematical models and extensive historical data for investment decisions, and the introduction of artificial intelligence has made quantitative trading more precise, efficient, and intelligent.

Firstly, artificial intelligence technology can analyze and process vast amounts of financial data through methods like data mining and machine learning to discover patterns and trends in financial markets. Compared to traditional quantitative trading methods, artificial intelligence can more accurately capture market dynamics and changes, improving the accuracy of investment decisions.

Secondly, artificial intelligence technology can automate trading operations, executing trades through algorithms and programs to reduce human intervention and operational risks. This makes trade execution faster, more precise, enables real-time market monitoring, and timely adjustment of investment portfolios.

Moreover, artificial intelligence technology can also help optimize and improve quantitative trading strategies. Through training and optimization using machine learning algorithms, effective parameter adjustments and optimization of quantitative trading models can be achieved, enhancing the profitability and risk control capabilities of trading strategies.

Given that artificial intelligence trading can access data in real-time and make decisions based on real-time market conditions, it can better adapt to market changes; artificial intelligence can handle more complex data and patterns to make more accurate market judgments; artificial intelligence trading can monitor market changes in real-time and automatically make trading decisions, enabling quick responses to market opportunities; artificial intelligence trading can continuously optimize its trading strategies through machine learning and deep learning algorithms to adapt to market changes...etc., artificial intelligence has stronger adaptability and decision-making capabilities. Since 2019, TEA Business School has been transitioning from quantitative trading to artificial intelligence trading.