- But computerised traders have taken it to the next level.
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The real trading is done automatically by robots. About three-quarters of trades on the New York Stock Exchange and Nasdaq are done by algorithms - computer programs following complex sets of rules. And this "robo-trading" is having a profound effect on the investment world, from global hedge funds right down to personal savers.
But what are the advantages and disadvantages of allowing computers to manage the world's trillions of dollars? And if you don't fancy the DIY approach, advisers and intermediary companies have access to these tools as well.
WiseBanyan co-founder Vicki Zhou says her platform allows people to "invest algorithmically through a diversified portfolio of low-cost index funds.
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Betterment's Joe Ziemer says: "We look at 40 different variables - spousal situation, rental income, pensions - and from these we will deliver you online, robot based on trading volumes seconds, a comprehensive retirement plan.
In a recent report, the UK's Financial Conduct Authority said online financial advice could "play a major role in driving down costs".
Robots are killing off Wall Street’s traders | World Finance
This is good news for us, but bad news for advisers - Royal Bank of Scotland said it would be cutting the jobs of face-to-face advisers in response to this new technology. The need for speed Big financial institutions are always looking for an edge over their rivals.
Information is power, so if you have more of it and can put that into effect quicker than others, you'll win the race for profits. Robo-trading offers them this advantage.
- What should a beginner know?
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Computers can trade multiple times in fractions of a second, exploiting tiny changes in stock prices and indexes to turn a profit. This network will convey financial information from Chicago - where financial products called futures are traded - to the New York Stock Exchange 2.
This tiny time saving is enough to give a trader an advantage in the hyper-fast world of "flash trading" - the controversial phenomenon exposed in Michael Lewis' best-selling book, Flash Boys. And they're also getting smarter. With the rise of machine learning and artificial intelligence, they can scour reams of news, research and social media - hundreds of data sets - potentially learning and self-improving as they go.
Dr Eugene Kashdan, a former London algorithmic trader, now a mathematics lecturer at University College Dublin, explains that these data sets taken individually might not reveal much useful information.
New York-based Rebellion Research and California-based Sentient AI are developing ways that these algorithms can learn from past mistakes and refine their rules, without the need for much human intervention.
He has over 20 years of experience in the areas of sales, marketing and relationship management. An MBA from Mangalore University, Gopkumar in his current role, leads the business that offers a single window for transacting in a wide range of asset classes such as equities and commodities derivatives. Time is money in the stock market. While investors have warmed up to stock trading, the actual time required to manually keep track of interesting opportunities, enter details and execute trades often eats into profit. This is exactly where automation and robots are transforming the way that things are done.
Out of control? Proponents say algorithmic trading puts needed liquidity - the availability of buyers and sellers - into the market, and reduces costs.
However, most studies do not take into account transaction costs. Their existence implies that in fact traders might not be able to make abnormal profits. This paper examines whether or not anomalies such as intraday or time of the day effects give rise to exploitable profit opportunities by replicating the actions of traders. Specifically, the analysis is based on a trading robot which simulates their behaviour, and incorporates variable transaction costs spreads. The results suggest that trading strategies aimed at exploiting daily patterns do not generate extra profits.
Critics say it wastes the talents of highly trained mathematicians and physicists, and destabilises the markets in ways no one robot based on trading volumes especially regulators - yet understands.
On 6th Maya "flash crash" took place that regulators blamed on high-frequency algorithmic trading.
The markets recovered their value 36 minutes later. US authorities blamed a year old in west London, who was using commercially available algorithmic trading software to trade part-time from his parents' house.
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The fear is that "flash crashes" could become more frequent in a trading world dominated by self-learning robots. Is it too far-fetched to imagine a clever computer deliberately triggering a huge sell-off with the purpose of buying shares when they're cheap and making a profit as the market recovers? Stagnation Some think a more likely scenario is that all these self-learning trading algorithms, accessing all the market-relevant data there is to know, eventually converge to a single view, leading to stagnation in the market.
Trading volumes would then shrink along with spreads - the difference between buying and selling prices.
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But others believe we'll never reach that point - the world is just too complex. No algorithm will ever be able to predict the future.