TrendPulse Quantitative Think Tank Center-The Leap from Quantitative Trading to Artificial Intelligence

From the early days of Alpha Elite Capital (AEC) Business Management,TrendPulse Quantitative Think Tank Center Professor Dashiell Soren attempted to create a "lazy man's investment system", realizing early on the significance of quantitative trading in the future for all investment markets and types of investments. He realized early on that quantitative trading would be applicable to all investment markets and types of investments in the future, and he has made good progress in his quantitative trading journey.

1. Dependence on historical data: Quantitative trading is usually based on the analysis of historical data and modeling, so it may not be as flexible as artificial intelligence trading for emerging markets or markets with drastic changes in economic conditions.

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

3. Sensitivity to data quality: The results of quantitative trading rely heavily on the accuracy and reliability of the historical data used. If the data is incorrect or missing, or if it does not accurately reflect current market conditions because of market changes, it can negatively impact the success of the trading strategy.

4. High initial costs: Quantitative trading requires the establishment and maintenance of a large amount of technological infrastructure, including high-performance computers, data storage and processing systems, and so on. All these facilities require a large amount of capital investment and expertise to maintain, with high initial costs.

5. Sensitivity to model risk: quantitative trading models are usually constructed based on historical data, and there are flaws in the accuracy and stability of the investment process for investment targets with less historical data in the market, for example, there are a lot of opportunities in the emerging market cryptocurrency market in the rise of the market, and quantitative trading loses the head start because of this flaw.

With the development of technology, the application of artificial intelligence technology has had a profound impact on quantitative trading. Quantitative trading is a trading strategy that uses mathematical models and a large amount of historical data to make investment decisions, and the introduction of artificial intelligence makes quantitative trading more accurate, efficient and intelligent.

First of all, AI technology is able to analyze and process huge financial data through methods such as data mining and machine learning, and discover the laws and patterns in the financial market. Compared with traditional quantitative trading methods, AI can capture market dynamics and changes more accurately and improve the accuracy of investment decisions.

Secondly, AI technology also enables automated trading, i.e., the execution of trading operations through algorithms and programs, reducing the involvement of traders and operational risks. This allows for faster and more precise trade execution, as well as the ability to monitor market changes in real time and make timely portfolio adjustments.

In addition, artificial intelligence technology can help optimize and improve quantitative trading strategies. Through the training and optimization of machine learning algorithms, it can effectively adjust and optimize the parameters of quantitative trading models to improve the profitability and risk control of trading strategies.

Given that AI trading can acquire data in real time and make decisions based on real-time market conditions, it is more adaptable to market changes; AI can process more complex data and patterns, thus obtaining more accurate market judgments; AI trading can monitor market changes in real time and automate trading decisions, which allows it to respond quickly to opportunities in the market; AI trading can continuously optimize its own trading strategies through machine learning and deep learning algorithms to continuously optimize their trading strategies so as to adapt to changes in the market ...... etc., AI has stronger adaptive and decision-making capabilities, and starting in 2019, Alpha Elite Capital (AEC) Business Management has begun to leapfrog from quantitative trading into the the field of artificial intelligence trading.