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Although the mining of Bitcoin was not successful, it was not completely unreal. At least through this mining of Bitcoin, we can see that the computing power of the notebook is far beyond expectations.

According to the Bitcoin mining model, the amount of Bitcoin acquisition is equal to the proportion of computing power, which is the proportion of the computing power of a certain computer within the entire international Internet range.

From this perspective, since Mo Hui is here to take Bitcoin, he can dig more than twenty in ten minutes, then in other words, the computing power of this notebook accounts for at least 90%. This is just Mo Hui's rough estimate, and it is likely to be higher.

What does 90% mean that this small notebook has at least nine times the computing power of all other mining computers!

One end is a laptop for personal use, and the other end is millions or even tens of millions of mining computers...

From another perspective, the computing power of this laptop is at least equivalent to the sum of the computing power of nearly ten million computers...

Tens of millions of computers...

Mo Hui was shocked by this data. This was so scary. All the existing supercomputers were put in front of it.

However, this also gave Mo Hui inspiration. Since its computing power is so great, the most suitable way to make money should be large-scale computing.

Mo Hui suddenly smiled. This is really a place to search for.

Mo Hui is a coder and a coder who plays big data. This is really a professional match. As long as he writes the big data program well and lets this super notebook calculate, there will be a lot of things to do.

The first thing that comes to Mo Hui’s mind is financial big data. As long as Mo Hui can develop a big data software that automatically collects relevant information on the Internet and then conducts in-depth data analysis, it is easy to analyze the actual operating conditions of a company.

As long as these data are used well, they can be used in the stock market. As long as there is infinite computing power to ensure, the analysis results will infinitely approach the real situation, and even the chairman of the company must accurately grasp the future development status of the company.

Mo Hui thought about it all over the board and felt that this idea should be feasible enough, and it is enough to write the program yourself, and the public channels online can also provide enough information. As long as the data analysis algorithm is designed well, the final output results will have great reference value.

However, this is a big project for software development, and it is probably difficult to complete it alone in a short period of time. However, there is no need to worry too much. Mo Hui’s idea is to splice it. Look for various open source software online, then splice these software together, and first make the first version of big data financial analysis software.

After the first version of the software comes out, actually run the tests and start helping him make money by trading stocks, he can use the money to hire people to help develop the software.

At that time, he can divide the entire software into many modules, and each module will send a package, whether it is to individuals or other software companies, so as to break down the development and finally assemble it together. At that time, he will be the role of a project manager. As long as the overall development progress is controlled, a large team can be remotely controlled to help him develop.

Mo Hui has already thought of the name of this software, and it is called Stock God. He is going to develop the Stock God version 1.0 first.

The development cycle is not expected for the time being, but it is conceivable that even if you do a splicing and assembly job, there will be a lot of interface development work in the middle. The adhesive and assembly platform that splices these software together require him to do it himself.

The specific workload cannot be estimated, so I can only do it first. If open source software can be found and used in a co-use manner, the cycle will naturally be much shorter. If it is very unfortunate that there is no co-use software, he will probably have to develop it himself, and the time spent will be gone.

Mo Hui listed a work schedule for himself, and began to complete and advance item by item according to this schedule.

If you want to "storage" a stock god 1.0, then there are several necessary key functional modules, such as the brain of stock god 1.0. This will be a big data analysis module, which is responsible for sorting and processing all collected information and extracting guiding analysis conclusions from it.

This data analysis module must have both explicit causal analysis capabilities and implicit causal analysis capabilities.

For example, the decline in pig inventory data will inevitably lead to subsequent rise in pork prices. There is a certain causal relationship between the number of pig inventory and pork prices, and the data analysis module must have the ability to identify this causal relationship that can lead to the result by obvious causes.

For example, the rupture of the oil pipeline in the Southeast Strait will inevitably lead to an increase in oil prices in the southeastern region of the Empire, which also has some inevitable causal relationship. However, unlike the pig inventory data, the pig inventory data is normalized, which is available every day and floats every day, and the oil pipeline rupture is an occasional event.

Although oil pipe rupture is an accidental event, the data analysis module must have the ability to identify such accidental events and then give the inevitable consequences that follow.

There are many similar causal and effect-related events or data, and the data analysis module must have the ability to identify such explicit causal and effect-related events.

Corresponding to these explicit causalities is the implicit causal connection.

The famous case of beer and diapers is actually an implicit causal connection. These implicit causal connections may not be inevitable, but there is often a possibility connection between cause and effect.

In a single case, this causal relationship may not be true, but when it is placed on a large enough base, this causal relationship is highlighted, which is a causal relationship in the probability sense.

Another case is based on this probability causal relationship. A search company wants to study the possibility of an influenza outbreak this winter, but its research perspective is very interesting. It does not study it from a medical perspective, but from a program and algorithm perspective.

By analyzing 50 million most frequently searched vocabulary, comparing them with the data from the disease center's seasonal influenza transmission period over the past five years, and establishing a specific mathematical model to find correlations and find hidden possible connections. In the end, it successfully predicted the outbreak of influenza and could even be accurate to specific regions and cities.

If explicit cause and effect only needs to be marked and set in advance, then implicit cause and effect obviously need to be explored and searched. How to find these implicit cause and effect is the main function of the data analysis module and is also a landmark indicator of whether this module is designed successfully.
Chapter completed!