Automation has often been hailed the future of the workplace, with new studies indicating that by the end of 2020 over 1 million knowledge-work jobs will be replaced by software robotics, RPA, virtual agents and chatbots, and machine-learning-based decision management.
Many are also of the opinion that automation is the future of recruitment, the automotive industry, as well as the agriculture, manufacturing, healthcare and finance industries. With respect to the finance industry, automation is already having an enormous impact on the way investors invest, and how traders, well, trade.
So much so that roughly 75% of shares traded on U.S. stock exchanges come from automatic trading systems today. While automated trade hasn’t necessarily taken over Wall Street just yet – it is certainly halfway to doing so. Technology has fundamentally changed the financial markets over recent decades, with tech innovation having changed how we invest, analyse deals, allocate portfolios, trade and communicate with each other.
Algorithm-based electronic trade in particular has led to a reduction in systematic risks for traders, while trade-based automated systems have facilitated greater trader discipline. Trading technology means speed and efficiency, and above all else – the power to instantaneously identify patterns and trends that might inform future trends.
Thus, computers and automated systems have the power to allow traders and investors to make safer bets, by offering direction and suggestions about what to do in a given market as well as help limit the range of choices. Today, investors can browse established trading programs and subscribe to them, or download a robot to run on their own computer.
It certainly makes a trader’s job less overwhelming, not least of all by cutting out the job of analysis. Automated trading also cuts out the influence of trader emotion, bias and judgement, by approaching the market objectively and systematically. Typically the behavioural tendency of a trader is to outguess the system, even if it’s producing profitable outcomes.
But a machine will not do that, it will identify patterns of success and failure using proven algorithms, rather than gut instinct’. That’s not to say automated trading guarantees success or profitability, and it most certainly comes with its own risks and limitations. But automation can give traders some much-needed structure and discipline, as well as reduce the burdensome task of researching trends and historical trade and financial data and statistics. All in all, automated trading systems have expanded the scope of traders in more ways than one could possibly imagine.
When it comes to analysing historical trade data especially, the capacity of automation cannot be overstated. Algorithmic trading refers specifically to the computerised automated trading of financial instruments based on algorithms or mathematical rules that analyse and base themselves on historical trends, and these are given little – if any – human intervention during trading hours. These mechanical trading systems allow traders to establish specific rules for trade entries and exits and programme these so that these can be automatically executed via a computer.
Backtesting is one especially powerful way of using historical market data to make automated trading more efficient. It applies trading rules to historical market data to determine the viability of an idea. This is done because an automated trade program needs to be told categorically what to do in any trade situation, and to train the computer the algorithm must be tested on historical examples to inform decision-making processes. Think about it. An automated trade system should never make guesses, and so by taking a set of rules and applying them to historical data, traders can test out their trade algorithm before risking money in a live trade. It gives traders the freedom to test and fine-tune their rules before putting it to the test in a live market.
This may sound fairly hardcore and perhaps only available to an intelligent few, but in fact there is a large number of online trading platforms that provide easy, standardised access to historical data and real-time data, as well as platforms that also offer trading and portfolio features via programmatic APIs.
There is no denying that historical trade data analysis is integral to hedging a safer bet (if such thing even exists). Extensive study of the past behaviour of a financial market (or instrument) can provide traders with good market insight, make automated trading systems more efficient and give traders confidence in the potential outcome of a trade. But so too can it lead to overconfidence in a trade, and as a result, heavy losses. At the end of the day, automated trading is wonderful but it must be acknowledged as such alongside its flaws. And humans are, at the end of the day, the ones responsible for developing and managing the automated systems.
It’s as Forbes financial markets writer Kevin McPartland, puts it, …while the robots are certainly multiplying, people remain people and the people are still in charge…hedge fund managers talk to algorithm providers to make sure they really understand what they’re getting and banks still need to trust the people behind their high-speed trading partners, he writes. Technology is certainly making those interactions smarter and more informed. The level of data both sides of any given trade have today is immeasurably higher than it was 25 years ago. But in the end, the humans decide what to do ¦
