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The number game: how algorithmic trading will change the sphere of finance



The New York Stock Exchange opened in 1817. As time went on, the speculative bubbles burst, the irrational exuberance began to fade, which is why the public’s perception of the market changed. As a result, new approaches to investing began to appear.

The change in the behavior of stock market players becomes apparent if we trace the story from the so-called candlestick method, invented by the rice merchant Homma Munehisa in the 18th century, to Richard Shabaker, who published books on technical analysis in the 20th century. During this time, a new type of investor has emerged, armed with technical and fundamental analysis, possessing commercial awareness and attention to value.
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In the 21st century, the most tangible changes occurred. With the development of data and technology, the old pattern was destroyed, and a new financial ecosystem emerged. People’s actions and behavior have changed in response to new market conditions. Financier Gleb Pilipenko in the pages of The market mogul publication talks about what the further development of technology in the field of finance will lead to - we have prepared an adapted version of this material.

Man against car


The most notable development that has changed investor behavior is based on powerful computing, high connection speeds, and getting information about intraday trading in real time. These ingredients gave rise to high frequency trading (HFT).

The dominance of high-frequency trading is manifested in an increase in its presence in various asset classes. As a result, a new participant appeared in the arena of battles - a car.



Changes in the competitive environment are likely to be felt by day traders: their work implies the need to make investment decisions as soon as possible. With the advent of HFT, responding to certain news or price fluctuations is unlikely to allow these players to receive the same profit from trading as before. In this sense, a person loses to algorithms that react and make simple trading decisions within milliseconds.

Consequently, the quick execution of such operations by machines as buying or selling stocks distorts price signals, which makes it difficult to manually enter a specific transaction. An example was the sensational sudden crash (flash crash), which caused unreasonable price fluctuations on American stock exchanges.

“Failures, such as those that occurred in August 2015, shaken the confidence of individual investors who rely on public markets to dictate the fundamental value of the company,” said former US Senator Ted Kaufman.

The solution to this problem seems obvious: abandon intraday trading in favor of making long-term decisions. With this approach, emphasis is placed on long-term fundamental analysis and macroeconomic factors, and not on short-term price inconsistencies dominated by machines. However, the reaction of individual investors was much more radical than one would expect.

New type of investor


The protection mechanism that retail investors have chosen has proven to be as advanced as the algorithms themselves. Communities such as Quantopian, WebSim, and Quantiacs have abandoned the view that high-frequency trading is a secret and monopolized playing field. Since then, Quantopian has become known as a " crowdsourcing hedge fund ."

The community provides its participants with various educational resources, ranging from lectures on data science and ending with testing platforms that enable them to enter the world of algorithmic trading without market knowledge.

The community is likely to expand due to increased funding from the famous trader and hedge fund owner SAC Capital Partners Steve Cohen, who promised to invest $ 250 million in Quantopian. The community approach allows a person with any background to develop the skills necessary to participate in algorithmic trading, competing with well-established HFT traders.

Although it is believed that algorithmic trading is intended for experienced computer scientists and mathematicians, Quantopian changes this view. The resource inspires fans to create self-made trading algorithms. And the creation of highly efficient code by newbies is rewarded.

A recent example is a 21-year-old student who, thanks to his own trading program, outperformed the market, receiving a 1.5% profit compared with a 8% drop in the S & P index. Such events show that technological awareness is growing among new generation traders.

The growth of communities like Quantopian indicates an increase in the desire to participate in the creation of trading strategies based on algorithms and acquire the necessary skills for this.

Quantopian founder and CEO John Fawcett said that the number of community members grew from 35,000 to 60,000 in less than a year. According to Jared Brad, founder and CEO of QuantConnect, the number of participants in his resource rose sharply to 17,000 from 6,000 a year earlier.

The fact that this is an open source platform provides an opportunity to develop a new type of investor. It combines old fundamental knowledge and innovations to meet the demands of current market conditions. Thus, the overall competition in intraday trading is likely to increase.

Conclusion


The increase in the number of amateur traders is likely to lead to a serious disruption in the market. Most of the failures are related to peer-to-peer business models, and high-frequency trading is no exception. Recently, hedge fund performance has been terrifying , and investors have begun to transfer their funds to new businesses.

Quantitative trading is gaining momentum due to stronger preferences in adopting a scientific and computerized approach. In the quantitative segment, communities like Quantopian are likely to flourish.

Traditional hedge funds operate on the basis of investor’s asset management fees, while the communities mentioned exclude such fees and share profits between the developer and the investor.

This approach will lead to the disappearance of elitism in the investment world, and everyone will be able to participate in algorithmic trading due to the huge amount of resources and the provision of capital with big names in the industry.

Changing the skill set will be associated with the transition from the fundamental assessment methods associated with daily news, to the processing of huge arrays of numerical data and backtesting. As the number of transactions made by robots grows, the acquisition of new skills will become a prerequisite and not a preference for many investors.

Other materials on finance and stock market from ITinvest :


Source: https://habr.com/ru/post/335230/

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