The Pros and Cons of Automated Trading

July 16, 2020 0 comments

The Pros and Cons of Automated Trading

Automated Trading

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Financial markets, from stocks to cryptocurrencies, operate with a veritable mountain of information. The data can — and have been — used to write algorithms that can trade on one’s behalf, whether it be individuals or major organizations. In fact, Wall Street has had a high adoption rate for automation, with 80-90% of stock trades executed by programs instead of people. Through machine learning, these systems determine the most favorable conditions to buy and sell, which can spell the difference between huge gains and equally huge losses.

However, it’s important to note that all technology is a double-edged sword — and automated trading is not an exception. To help you decide whether it’s worth exploring, here are the main pros and cons of automated trading:



• Objectivity: It’s hard to remain objective when huge amounts of capital are at stake, especially in volatile conditions. Take, for instance, the forex market. The high volatility can be traced back to the political and economic factors that influence forex rates. This is why anyone who trades forex must constantly keep ahead of recent events in order to monitor currency fluctuations. For example, a recession is one type of event that can lower the value of a currency.

That said, emotions are the pitfall of any trader. Overexcitement at favorable conditions or panic in the face of unfavorable ones in rapidly changing markets can lead to costly mistakes. Automation takes emotions out of the picture and instead uses data to make objective decisions.

• Backtesting: Backtesting refers to the process of evaluating a particular trading strategy using historical data. It’s an invaluable tool that analysts use to test how well a particular position will work in a live market before executing the trade. It’s also applicable to different instruments, such as stocks, commodities, and cryptocurrencies.

For instance, you can predict the profitability of trading oil futures, one of the most popular commodities, during a recession using previous trends. Backtesting allows traders to optimize their trading strategy based on how well or how poorly it’s worked in the past. All in all, automating this process can minimize the environmental risks involved in trading.

• Convenience and speed: Mechanical systems like robo-advisors are accessible to traders with different levels of experience. Because of automation, trades have also become exponentially faster compared to previous decades. Being able to react automatically to sudden market changes is a huge trading advantage.



• Tech dependence: Automation, however intelligent, is not foolproof. Experts predict that failure of trusted algorithms is bound to happen — and this can result in a ‘series of cascade failures’ for financial institutions of every scale. It already happened during the trillion-dollar stock market crash of 2010, which was a result of an algorithmic error.

Even for small scale trades, a slow internet connection can already be disastrous. Trading occurs in a fast-paced environment, and a reliable technology infrastructure is crucial for staying ahead of the curve.

• Overfitting: In machine learning, overfitting means creating a statistical model with more data than is necessary. Trading algorithms have the tendency to be fed with too much historical information. That’s not necessarily bad, but overfitting can lead to inflexibility of trading strategies in present and future conditions. This is why backtesting, while useful, is not completely reliable. It creates a bias for positive outcomes and gives the impression that a particular strategy will perform exactly as predicted in a live market.

With such a high adoption rate in trading, automation is clearly here to stay. The objectivity, accessibility, and speed it offers make it a powerful and invaluable tool for traders. However, automated trading systems should not be left unmonitored. Careful analysis and insight is key to preventing a domino effect of algorithmic errors.

Interested in the marriage of finance and technology? Do have a look around our banking and financial services articles.

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