The Future Of Financial Autonomy - How Computer Science Will Shape Financial Markets

Historical Financial Practices

It is indisputable that trading and finance have been affected by the evolution of technology. From the 1900s to the present day, technology has shaped trading on a daily basis. In a span of a century, trading went from intense NYSE chaos to electronic brokerages. Modern-day technology allows for easy and high-volume trading for retail investors while also allowing experienced investors to trade at hypersonic speeds globally. The history of trading revolves around 4 generations of technological breakthroughs along with what may potentially be the fifth.

1900s

Just a century ago, all trading was processed through a physical location like the New York Stock Exchange. The bulk of trading was done in person and a very select group was given the privilege to trade. This system of trading securities is incredibly inefficient and slow. The volume going into financial markets at the time was incredibly low and much of that to blame was how hard it was to invest.

1960s

Fast forward a few decades, computing and electronics were introduced to the New York Stock Exchange. In this stage of trading, bulky computers executed trades more efficiently. However, despite this new technology in finance, these computers were still incredibly slow and many American households did not own one.

2000s

Fast forward a few more years, securities and exchanges were completely revolutionized through computer science and technology. Roughly 30 years prior, the Nasdaq Exchange was created which made trading completely electronic in contrast to the NYSE. In addition to the Nasdaq, the rise of retail investors and stock brokerages like Charles Schwab made investing so much more efficient and widespread.

Present

Just recently, the popular trading brokerage, Robinhood, brought a new concept to trading. The app created the idea of commission-free trading just showing how far trading has evolved because of technology. In addition, there have been some trading modifications in terms of autonomy. Bots have been created in order to execute millions of trades for a fraction of the profit in milliseconds.

Drive To Autonomy

As mentioned before, trading bots have begun to shape finance. They have revolutionized digital scalping and have time and time again returned great yields. However, despite greater popularity in trading bots, much of the bot community is still unexplored and untouched. As mentioned before, many of the present-day trading bots are used for scalping and day trading where the autonomy given to the bot is very minimal. However, as future bots become more accurate and trusted, there may be an opportunity where trading will become completely autonomous by these incredible bots.

How May This Happen

Present-day bots use technical analysis in order to execute trades accurately. In many cases, they would look at indicators like RSI, MACD, VWAP, and more in order to determine when to buy or sell. Well-coded bots will have a phenomenal yield and great potential for profit. However, many years will pass before humans will trust bots with their money.

Research-Technical Analysis

After some extensive research, a conclusion was drawn that computer science will allow for completely automated trading. According to the research, autonomous trading is not as daunting or risky as it may seem. In order to allow the bots to trade effectively, thorough and highly sophisticated code must be developed. Research suggests that bots can choose from a select group of stocks or ETFs to trade. These groups may include the S&P 500 or the Russell 2000 in order to ensure that the stocks that the bot is trading are genuine and secure. After a select group of stocks is chosen, a person can tell the bot whether it should scalp, day trade, swing trade, or position trade (mostly used for scalping, day trading, and swing trading). Next, the bot will have to identify variables through code. These variables will be the indicators for technical analysis including MACD, RSI, the ability to identify pennants and key levels, and more. The bots can retrieve this information from a third-party trading software company like Bloomberg or Tradingview and apply them to a client’s portfolio.

According to Rickhardypro, a professional data analyst and programmer from the University of Nation World Economy in Bulgaria, he states that,“ The trading will sooner or later be really quantitative heavy and the old fundamental investing ways won't work.” He exclaims how trading in the near future will be automated through variables and numbers as opposed to mental logic due to a bot's ability to identify key areas of stocks with precision timing. This thus allows the bot to have an advantage over retail investors. Although this software may seem incredibly challenging, it is actually not that complex. Instead, the greatest challenge to this kind of technology is not the technology itself, but the credibility and the trust.

Computer Science Incorporation (Variable Usage)

Like mentioned before, this kind of technology heavily incorporates software. There are multiple variables to account for including 2 separate sets of variables. The first set will determine a person’s preferred style of trading. In this part of the process, the most preferred style of trading will be given a certain value from an input by the bot owner that will be used to determine whether the bot will use its scalping or its swing trade investing capabilities. Within technical analysis, the bot will use indicators as variables. These variables will be given a numerical value depending on how bullish or bearish the indicator is suggesting. After processing multiple indicators, the bot will average the numerical value of the bullishness/bearishness of all the indicators in order to better judge whether to execute and for a specific time frame using variables.

Why Not Fundamental Analysis?

In trading, there are two forms of evaluating equity. One form is a technical analysis where a trader will use charts, graphs, indicators, and models in order to determine whether to buy or sell stocks. Another is a fundamental analysis where a trader will position trade by holding his or her stocks for the long term. Although investing long-term is a good approach towards trading, quick, accurate, and short-term trades by precision bots are more profitable due to the bot’s capabilities.

Concerns

One of the biggest concerns with this type of software technology is trust. Due to this technology being backed completely by software, data breaches and cyber-attacks are a major concern to users and traders. In addition, this kind of technology may have conflicts with the Securities and Exchanges Commission due to its autonomous-like system.

Conclusion

Autonomous trading is on the rise. The ability for bots to detect key buying signals milliseconds at a time gives it the competitive advantage over many retail investors. However, this technology comes with concerns and much of it comes with cyber attacks. The future for autonomous bots is uncertain but it can potentially revolutionize trading equities in the near future.