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A growing number of capital market companies are adopting machine learning and other intelligence systems to build algorithmic trading systems that study data that is not dependent on system-based data.

By bringing in data scientists, advances in cloud computing and access to developed startup systems for acquiring learning machines, AI is changing the business chair. The big banks have already developed a personal learning algorithm for stock trading.

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Robert Hagerty, CEO of Hegarty Group, a consulting firm focused on financial services, technology, data and artificial intelligence, said: “Machine learning is another step in the nature of marketing algorithms for machine learning that recognizes values ​​and behaviors in data history and learns from them. Learning machine.

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Although quantum programmers and strategists develop traditional algorithms, these algorithms are based on the fact that the rules are not learned by themselves; They need to be updated. Gertie said, “By learning the machine, you transfer it to the machine to learn the best business plan and develop algorithms automatically, without human help,” Gertie said. “It’s a big difference.”

The fire behind machine learning is getting more attention because of the changes caused by the global epidemic. Experts argue that this type of machine learning is faster, more complex and can be adapted to larger programs, such as the revised increase in COVID-19 exposure.

Roman Guinness, CEO of Imperative Execution, said the startup developed IntelligentCross, an alternative business system that provides AI for the first time in 2018.

The old method is to build a system based on settings that are legal in some way. “The main idea is that the world we’ve seen in the past is also the world we see in the future. In that case, everything works if the world is right,” Guinness said.

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Before the global epidemic, stock markets flourished and changed. “In that sense, it’s possible. Then COVID took place in March with a change from VIX 15 to VIX 80.” Then, all of a sudden, the trading system went into great pressure by increasing market data and tariffs, “he said.

After growing and changing the spread margin, the U.S. trade deficit rose 42% in the first quarter compared to the previous quarter, to a peak of 63.7 units per minute, TRADE reported based on the Virtu Financial report. Costs include short-term implementation and marketing committees. This study is based on the database of Virtu Global Peer Infrastructure Managers.

Instead of building strict code, the machine learning system allows the system to measure what is happening in the environment and then enter new data from the market and decision statistics, Guinness explained.

IntelligentCross integrates with buyers and sellers to reduce market impact. It accepts all time constraints, such as ATS or any change, and is compatible with special time, seconds and milliseconds apart. The difference from other gaming sites is that the artificial intelligence system adjusts the game time to reduce the market response after the transaction. After each transaction, IntelligentCross calculates the transaction and places it as a data center within its artificial intelligence system. This adjusts the game time to keep the price movement as close to zero as possible, while maximizing profits.

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For example, as change increases, tolerance decreases – as the market moves faster the rules become less relaxed. “IntelligentCross falls in terms of cost and patience. Areas with or without mobility, perform worse when the environment changes: they exhibit worse market impact, or worse,” he said.

As stock prices exploded in the market, the company saw a 6-fold increase in March compared to. January, according to data science execution mandatory. “High volumes put pressure on the business system everywhere. IntelligentCross also handles load more efficiently, with slower traffic growing by a few percent,” Guinness said.

According to the company, ATS has provided $ 30 million in savings to entrepreneurs based on $ 280 billion purchased since its inception. ATS has 0.3 settings and impact on the market compared to 1.4 bps for average conversion. This is 85% less market impact compared to a change based on the average midpoint measurement 20 milliseconds after trading.

In the same way that Imperative Execution uses AI to optimize the execution area to reduce market impact, machine learning and artificial intelligence can be used to improve algorithm performance.

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Marketing companies started developing intelligence-based algorithms about five years ago, but only in the last two years has the artificial intelligence environment of retailers reached the level of expansion the bank can predict back to AI as can be seen in A / B testing. , Writes Michael Mollmans, a former senior analyst at the TABB Group in the research report “AI and Side Algorithms for Sale: Most Appropriate Survival.”

Originally artificial intelligence became a media company and Purchasing Parts desperately wanted to use Algo in 2016-2017, but most of the time and their approach was not good, said Mulmans, who is now chief research officer at Chartis Research, in an interview. This was the first time; They have no opportunity to “make full use of artificial intelligence,” he said. When a complete environmental system is complex, they used big data, cloud computing and computing power from GPUs (graphics processing departments) to manage big data. “If you do not have the power to clean and process data,” then you do not have AI, he said.

It was only after the launch of the MiFID II in January 2018 that the Algo wheel gained acceptance as a way of strategically organizing and measuring the best-performing vendors, when investing on the sales side within AI began to bear fruit. Molman said alloy wheels are a key factor in promoting artificial intelligence and developing algo. While about 20 percent of the water is flowing in the algo wheel right now, retailers see it as the future, Mulmans said.

Banks and asset managers use natural language processing (NLP) and machine learning to extract valuable information from audio, paper and audio. Read more about AI and machine learning in “AI: Natural Language Design and Battle for Unstructured Data”.

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For example, the most common use of AI systems is in optimizing algo processing by increasing the speed level to reduce market impact and increase the cost of space. “Big data can be used to learn to align children’s algo segments more accurately than traditional algo code,” the report said. Pricing and forecasting are also fast-paced AI applications, with media optimization proving itself as a result, especially in advanced media markets in Asia.

Now machine learning has a lot to do with the technological advancement in GPUs, its configuration and Big Data. “It represents a relationship between cloud power, data growth and advances in AI and cloud learning,” Hagerty said.

Today the tool is fast. Amazon, Google and Microsoft have “a good cloud-based platform for purchasing the kind of technology that can be rented at a low price to build it in-house,” Guinness said.

There are hundreds of advanced learning platforms, like TensorFlow and Google’s Extreme Gradient Boosting (XGBoost), that have been redesigned and solved slightly different problems, depending on the model, whether it is a split or a recurring problem, or it is both nurtured and treated. To. Or learn the best support to use, Gertie said. Each of these models is still built to solve different problems, he said.

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Some companies have developed antivirus networks, logical systems, like the human brain, that receive data input and send data to multiple nodes, Mulmans said. The gates have significant flexibility in the rate of data entry, he said.

One of the tools of trading is how to make a TCA and that is part of the purchase. For example, inside the algo wheel it uses a learning curve to help the wheel change over time based on the results the algo wheel data produces, said Alistair Kerry, product manager for TCA and Algo Wheels.

To illustrate this point, suppose a shopping company uses vendor A to sell 50% of its orders as well as 50% to dealer B and get some of the performance data back. “One of the problems you encounter when operating Algo wheels is deciding how to adjust the wheel based on the performance of the various locations as well as the number of rules in your data set.”

“Education for improvement will say, given the performance of these two sites and given the amount of data it has, it has to use that small amount of money to make it difficult. Because the whole solution is completely automated, you can do a lot of small things and processes added, but did nothing and took big money. , “Said Kerry.

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“Instead of spending three to six months just collecting data, and not using any data to improve performance, you can increase ongoing performance over time,” Kerry said.

“Second, supportive learning can be receptive and consistent. If market conditions change or if performance data begins to show a difference, the algorithm will change and start moving again across multiple servers,” Kerry said.

While it is possible for individual data scientists to conduct this research on a daily basis, “it works well to adapt it to your data science application easily on the other hand, as you continue to benefit from the updated system,” Kerry said.

In light of this performance, machine learning will grow exponentially over the next few years. Harti said, “In the current economic climate, every company is looking to do more by using smaller products, improving its technical capabilities due to competitive pressures and reducing costs,” Harti said.

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Guinness said, because the higher education system, once companies understand how to use machine learning systems, they will find it more difficult than manual programming.

By being able to improve its performance, machine learning can save a company money by using a small and highly focused development team.

“Imagine if you had a collection of beautiful engineers and scientists with unlimited assembly.

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