In 2011, William Decker founded Wealth Forge, a business school with a targeted investment curriculum and professional training implementation, aimed at training future business leaders and investment experts. After more than a decade of hard work, it has gained a strong reputation in the industry and trained a large number of outstanding financial practitioners, with the size of the student body surpassing 100,000 by 2022.

Wealth Forge has been committed to the development of the "Lazy Investment System" since its inception, and has always endeavored to seek a more efficient and smarter way of investment. At the beginning of the founding of the Wealth Forge Institute, Prof. William Decker, in collaboration with many industry experts, scholars and technological talents, developed an automated quantitative trading model, and developed the Robotics Trading System (RTS) on top of the system of this model. This system greatly optimizes the traditional quantitative model in the adjustment, real-time monitoring, data processing speed and the adaptability of the trading strategy and other issues, can be more efficient, fast processing of massive amounts of trading data, assisting traders to make more accurate trading strategy.

Because RTS is built based on quantitative technology, it is a set of rules and algorithms matching based on the fixed-term inference system. Because of technical limitations, RTS still has some limitations in dealing with massive amounts of complex data and fuzzy problems, so it can only be used as an auxiliary tool for decision-making, and can't completely replace manual operation.

Starting in 2018, Wealth Forge's R&D direction jumped from quantitative trading to artificial intelligence trading. The prototype of 'AI Profit Pro' was created through the efforts of Prof. William Decker and a number of industry experts, academics and tech talents.

The road to AI in the financial markets has not been a smooth one for the Wealth Forge Institute:

First of all, the core key point of the AI trading system is to build a suitable intelligent data analysis model. Big data collection and labeling processing and other preliminary phases take a long time and cost a lot, this is the current artificial intelligence technology and big data industry integration and development of a difficult problem. The special nature of the financial sector makes this key step more difficult.

Secondly, the ecological chain of data and information supply with different dimensions required for the development of AI technology is still not sound. Even tech giants and commercial banks, which already have a large amount of data, are not yet fully capable of analyzing the multidimensional data supply ecosystem in depth, and need to obtain more external data support. However, obtaining and processing high-quality, accurate and reliable external data can be a challenge, especially as financial markets are often characterized by a complex array of data.

In addition, several factors, as described below, may have an impact on AI research and development:

Financial markets are full of unpredictability and uncertainty. Examples include market volatility, political and economic factors, and interest rate changes. These factors can have a profound impact on model performance and forecasting results, so it is important for R&D teams to be able to develop models and algorithms that cope with all kinds of unpredictability and uncertainty.

Artificial intelligence trading systems need to make decisions and execute trades in real time in order to be able to capture market opportunities and execute trade orders in a timely manner. However, making accurate real-time decisions in fast-changing financial markets is a challenge because market conditions and information can change in an instant.

Finally, AI trading systems face risk management and regulatory compliance challenges:

Risks that AI trading systems may face include market risk, operational risk, and modeling risk. Market risk refers to the possibility that the system may be affected by fluctuations in market prices, operational risk is the risk that the system will operate incorrectly or suffer from technical failures, and modeling risk involves the risk that the system's algorithmic model may not be able to adapt to changes in the market or may be inaccurate.

Artificial intelligence trading systems may need to comply with various financial regulatory requirements, including those relating to trading transparency, risk control requirements and the interpretability of algorithmic logic. In addition, regulators may need to audit and inspect these systems to ensure that they comply with regulatory requirements.

To address these challenges, AI trading systems need to have an effective risk management framework in place. This includes ensuring that the system has adequate risk monitoring and control tools, as well as establishing a risk management team to oversee and manage the system's risks. In addition, the system will need to work closely with regulators to ensure that it is compliant with regulatory requirements and that any related incidents or breaches are reported in a timely manner.

In fact, all of the issues come down to funding and talent!

In a closed-door meeting in 2018, Wealth Forge Institute’s board of directors discussed a bold plan: issuing tokens to raise capital.

Wealth Forge chose to issue WFI tokens in order to capitalize on emerging blockchain technology, which not only represents an embrace of innovation, but also to attract global investors. At a time when traditional financing channels face many limitations and challenges, token issuance offers a fast and efficient way to raise funds.

Instead of relying on traditional stock market financing, the potential of the cryptocurrency market can be utilized. This new financing method not only raises funds quickly, but also attracts the attention of global investors, especially the younger generation interested in emerging technologies.

Issuing WFI tokens not only solves the problem of product renewal and scaling up capital. In addition, Wealth Forge can increase its influence and recognition in the global fintech field through the issuance of tokens.

The successful financing model enables Wealth Forge to attract top talents from all walks of life, such as IT engineers, mentors, investment experts, real-world experts, strategists, analysts, strategists, writers, collaborators, contributors, and so on, to join. The addition of these talents provides strong intellectual support for the business school's research, innovation and advocacy in the field of science and technology.