Divine Quantum was formed in 2007 using mathematical economics.
Divine Quantum 2 was born in the era of big data, which can sample and infer the laws of financial markets for prediction.
Divine Quantum 3 is jointly developed by Rainmaker Quantum and the CEO of the short-term CFD market order suitable for the current financial market.

Divine Quantum uses the principles of mathematical economics as the core basic algorithm. This theory first establishes the assumption that "people are rational", uses mathematical methods to simulate various economic phenomena, and deduces solutions to related problems. It uses the Internet to crawl the core terminals of exchanges to extract data through machine learning modeling, such as: market depth, industry data, product data, corporate cash flow, corporate profitability, etc. Divine Quantum counts all relevant data to give a 'rational' forward-looking profit probability.

In the era of vigorous development of big data, we see a whole new way to deal with financial markets. In addition to combining all the functions of the first generation, the core functions of Divine Quantum 2 are based on the big data analysis system.

With the advent of the 5G era, the network speed not only activates the field of the Internet of Things, but also enables more Internet data to be collected into a large database, which greatly increases the amount of information in big data. In order to cope with the new technological environment, we have found corresponding strategies to reshape the thinking mode of Divine Quantum 2.
Divine Quantum was most successful in 2006, when the U.S. real estate industry was vigorously developed, and many financial institutions valued the idea of cash flow from subprime loans, thus securitizing assets to form collateralized debt obligations (CDO), packaged and sold to other financial institutions. Institutions and these purchased institutions will be repackaged and sold to other financial institutions in this cycle. Divine Quantum timely analyzed that the capital flow of a single house in the United States was as high as 20-50 times and the bond rating was too high. It gave a prediction contrary to the optimism of the market, and the probability of risk was greater than profit. This helped Rainmaker identify this opportunity and short collateralized debt obligations (CDOs) in advance, becoming one of the few financial institutions that made money during the 2008 subprime mortgage crisis.
In 2007, Stephen Smale was received the highest honor in mathematics, the Wolf Prize in Mathematics, principles of mathematical economics, for his influential and groundbreaking contributions to mathematical economics.
Mathematical economics, in a broad sense, refers to the use of mathematical models to carry out economic analysis and explain the theory of economic phenomena.
It collects massive data and samples it through the mining methods of “data capture” and “data detection”, and then conducts multi-dimensional analysis, data management, data preprocessing, model and inference considerations, complexity considerations, etc. to infer and finally form a visualized user behavior factors, so as to deeply search for the underlying laws of the market, and then predict the future. Divine Quantum 2 collects and analyzes public sentiment including online comments, media reports, professional communications, listing announcements, opinion leaders’ blogs and more to predict stock and currency price movements.
The highest achievement of Divine Quantum 2 which is in 2017, it was found through analysis that market predators in the cryptocurrency market were operating the greed of retail investors, and the premium of cryptocurrencies was too high, thus giving Rainmaker information and cooperating with well-known exchanges to deploy in advance and at the highest level. The price of Bitcoin has been shorted, which led to panic selling by some institutions and retail investors.
Divine Quantum 3 sums up the second-generation algorithms to form 6 core thinking modes, including big data analysis and mining, quantum parallel computing, deep neural network mode, Monte Carlo algorithm, strategy and valuation network and risk value algorithm. It first collects data from big data as ‘fuel’ and accelerates all analysis and calculation speeds through quantum parallel computing. Through adaptive machine learning and in-depth calculation, it can still run the relevant data accurately even in the case of a huge amount of data. analyze. It employs engineered decision intelligence in forecasting the future, enabling continuous and accurate decisions based on insights into market dynamics.
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