Algosensey Quantitative Think Tank Center-Wealth Forge Institute: THE LEAP FROM QUANTITATIVE TRADING TO ARTIFICIAL INTELLIGENCE

From its inception,Algosensey Quantitative Think Tank Center the Wealth Forge Institute has been committed to the development of a "Lazy Investment System" and has always strived to find a more efficient and intelligent way of investing. At its inception, Prof. William Decker collaborated with numerous industry experts, scholars and technological talents to develop an automated quantitative trading model on which the Robotics Trading System (RTS) was developed. This system greatly optimizes the traditional quantitative model in the adjustment of parameters, real-time monitoring, data processing speed and the adaptability of trading strategies, etc., can be more efficient, fast processing of massive amounts of trading data, assisting traders to make more accurate trading strategies.

Although quantitative trading and artificial intelligence trading are both methods of investment trading and decision making using computer technology, they also have some shortcomings. Here are some of the weaknesses of quantitative trading compared to AI trading:

DATA DEPENDENCY

Quantitative trading is usually based on data by analyzing historical data and constructing corresponding investment models, which is the antithesis of subjective trading. Therefore, quantitative trading may not be as flexible as AI trading to respond to emerging markets or markets with dramatically changing economic conditions.

LACK OF SUBJECTIVE JUDGMENT

Quantitative trading relies heavily on rules and algorithms to make trading decisions and lacks the intuition and subjective judgment of a human trader. This can sometimes result in an inability to capture certain non-regular market sentiments or events, while not taking into account any uncertainties in the market, which can lead to instability in trading strategies.

SENSITIVITY TO DATA QUALITY

Quantitative trading results are strongly dependent 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 due to market changes, this can negatively impact the success of the trading strategy.

HIGH INITIAL COSTS

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

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 large number of opportunities in the emerging market cryptocurrency market in the rise of the emerging market, and quantitative trading because of this flaw to lose the first opportunity.

With the advancement of technology, artificial intelligence technology brings a whole new potential, and the finance-related industry may be the biggest beneficiary of the technology questioned. As a result, the development of Artificial Intelligence based on big models has become a new hotspot in the development of financial technology. Although the same use of computer technology, but compared with the traditional quantitative trading model, artificial intelligence is more reliable, can achieve more efficient, more accurate, more intelligent investment decisions for traders, to help more people to achieve financial freedom and dreams.

First of all, in terms of extensive data processing, the development of artificial intelligence not only provides efficient data analysis for various transactions of financial institutions, but also supports the decision-making of regulatory authorities. Compared with traditional quantitative trading methods, AI can effectively extract information from a variety of traditional and non-traditional data sources, and can also reflect complex non-linear relationships in the data to improve the accuracy of investment decisions.

Second, AI technology can lead to more reliable automated trading methods, i.e., automating trading operations through algorithms and programs. The use of AI to replace traditional manual review and supervision of financial transactions and service information, and to control potentially illegal behavior in trading activities, can better avoid operational and moral risks, while allowing for faster and more accurate trade execution, as well as the ability to monitor market changes in real time and make timely adjustments to investment portfolios.

In addition, artificial intelligence technology has the ability to self-learning and upgrading, can analyze the massive amount of data at the same time to upgrade itself, and constantly optimize and improve the trading strategy, to improve the profitability of the trading strategy and the ability to control risk.

Whereas AI trading can acquire data in real time and make decisions based on real-time market conditions, which is more adaptable to market changes; AI can handle more complex data and patterns, thus obtaining more accurate market judgments; AI trading can monitor market changes in real time and make trading decisions automatically, which allows it to respond quickly to opportunities in the market; AI trading can continuously optimize its trading strategies through machine learning and deep learning algorithms constantly optimize their trading strategies so as to adapt to changes in the market and so on. Artificial intelligence has a stronger adaptive ability and decision-making ability, and starting in 2018, the Wealth Forge Institute began to leap from traditional quantitative trading to the field of artificial intelligence trading.