The Bots Awaken: 2016 Promises to Be the Year of the Robot

Trading robots are a great opportunity to stay a step ahead of the game, but like everything else, there are

Finance robots, bots, algo-trading, robo-advisors, expert advisors, no matter what name you know them by, these babies are promising to take the trading world by storm in 2016. While these trading machines have been around for a while, especially in the US, a recent report from Cerulli Associates, a research firm that specializes in global asset management, said that robo-advice platforms are expected to reach $489 billion in assets under management by 2020, up from $18.7 billion today.

Although today’s robo-advisors are not particularly friendly, they are a great way for investors who need help with simple investment choices, but who can’t afford the often steep costs of face-to-face advice, to get in on the game. With time though, robo-advisors are likely to become more sophisticated, according to Ian McKenna, director of the Finance & Technology Research Center, a specialist consultancy which benchmarks technology for financial product manufacturers, advisers, distributors and consumers.

In today’s market, traditional tools and fundamental analysis are not enough to succeed and trading robots are a great opportunity to stay a step ahead of the game, but like everything else, there are pros and cons to using robots.

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Pros:

  • Bots allow you to create a customized technical scheme and automate it.
  • Bots have no room for emotion or psychological decision-making. Robots will provide you with an objective valuation of instruments and won’t change their mind based on emotion, remaining disciplined in following the program according to plan.
  • Since computers are much faster that any human could possibly be, automated systems are able to respond immediately to changing market conditions and generate orders instantly upon trading criteria being met.
  • Automated trading systems allow traders to have multiple accounts or strategies simultaneously, thus spreading risk potential over various instruments.
  • Robots are scalable.
  • Robots eliminate human error.
  • Robots reduce buy/sell spreads.

Cons:

  • When employing a robot and relying solely on it, you are essentially handing over the control of your investment to a machine that is pre-programmed to run according to a set of rules and that regardless of the circumstances, has no ability to adapt itself.
  • Robots have never been tested in real market conditions, but rather on historical data, which means non-trade related problems such as connectivity issues and broker-originated problems, would not be shown on market data and therefore ignored.
  • Technical difficulties, connectivity issues, power losses or computer crashes may cause a trade order to lag or not be sent at all.

What to consider

If you decide to join the bot revolution during 2016, there are three basic ways in which you can get your hands on a bot of your own: you can purchase one, lease one or code one.  In the long run, purchasing is the least expensive option, however, if you decide to finance the purchase, the amount of equity required as down payment is significant, plus by getting a loan, you also reduce your borrowing capabilities. While building a bot requires some coding knowledge, there is a wealth of information on the web about coding in mql, Metatrader’s own programming language and if you are up for the challenge, all it takes is a good Youtube video.

Algo-trading, or bots, have had a tremendous impact on the financial markets and will continue to do so as they become more and more popular amongst traders. In 2014, a study of the impact of algo-trading across 42 global stock markets showed that their use made the markets more liquid, more efficient and more volatile. Another study conducted in 2013 showed that over the years, the commodity markets had become increasingly self-reflective, with 60 – 70% of price changes being driven not necessarily by new information from the real world, but rather by self-generated activities, when bots base their decisions on the market’s ‘order book’ in a way reminiscent of the Sarao incident.

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