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Trading – Man vs. Machines: Part 3 of a Continuing Series on “How Algorithm Trading Can Supercharge Your Trading Profit”

Trading room – Mon 26 Nov 2018


In our prior series on how algorithm trading can supercharge your trading profit, we looked at the introduction to the world of algorithm trading and how it is a force that has changed and continued to disrupt the global investment and trading world.

You can read Part 1 here on Introduction: When humans and machines collide.
You can read Part 2 here on Man vs. Machine: Kasparov vs. Deep Blue

In Part 3 of these series, we continue off from Part 2 where the astounding chess event in 1996/1997 saw World Chess Champion Garry Kasparov then took on IBM and its super-computer Deep Blue in the ultimate battle of man versus machine and his loss for humanity.

We mentioned that in reality, there was much to learn from this historic game of man vs. machine and its relation to many technology-based applications that govern our world today including automated trading systems in trading that is mushrooming today.

Kasparov himself argued then that the computer must actually have been controlled by a real grandmaster, implying that he in fact lost to a human being behind the machine rather than the machine intelligence itself.

He and his supporters believed that Deep Blue’s playing was too human to be that of a machine.
Meanwhile, to many of those in the outside world then, by the computer’s performance, it appeared that artificial intelligence had reached a stage where it could outsmart humanity – at least at a game that had long been considered too complex for a machine.

But whose side of the story is right?

Courtesy of a study by Mark Anderson, Professor in Computing and Information Systems at Edge Hill University, we have a balanced analysis of the situation.

The reality was that Deep Blue’s victory was precisely because of its rigid, un-humanlike commitment to cold, hard logic in the face of Kasparov’s (human) emotional behavior.

This wasn’t artificial (or real) intelligence that demonstrated our own creative style of thinking and learning, but the application of simple rules on a grand scale.

According to Anderson, what the match did do, however, was to signal the start of a societal shift that is gaining increasing speed and influence today.

The kind of vast data processing that Deep Blue relied on is now found in nearly every corner of our lives, from the financial systems that dominate the economy to online dating apps that try to find us the perfect partner.

What started as student project then under Deep Blue, helped usher in the age of big data.

There has also been speculation that Deep Blue only triumphed because of a bug in the code during the first game. The subsequent detailed analysis of the logs has added new dimensions to the story, including the understanding that Deep Blue (the machine) made several big mistakes.

One of Deep Blue’s designers had said that when a glitch prevented the computer from selecting one of the moves it had analyzed, it instead made a random move that Kasparov misinterpreted as a deeper strategy. That psychological advantage eventually wore Kasparov down and led him to make errors in his own game.

The world champion was supposedly so shaken by what he saw as the machine’s “superior intelligence” that he was unable to recover his composure and played too cautiously from then on.

He eventually missed the chance to come back from the open file tactic even when Deep Blue made a “terrible blunder”.

According to Anderson, whichever of these accounts of Kasparov’s reactions to the match are true, they point to the fact that his defeat was at least partly down to the frailties of human nature.

He over-thought some of the machine’s moves and became unnecessarily anxious about its abilities, making errors that ultimately led to his defeat.

Deep Blue then didn’t even possess anything like the artificial intelligence techniques that today have helped computers win at far more complex games, such as Go.

Instead, Deep Blue merely relied on the computer’s ability to search through huge numbers of possible moves, using large numbers of processors running the same set of calculations simultaneously to analyse 200,000,000 possible moves a second (or around 40-74 moves into the future) as compared to a human chess master which typically analyses 10 moves into the future.

The world of chess playing machines, meanwhile, has evolved since the Deep Blue victory. Whereas Deep Blue was a custom-built computer relying on the brute force of its processors to analyze millions of moves, new chess machines were software programs that used learning techniques to minimize the searches needed.

This can beat the brute force techniques using only a desktop PC.

But despite this advance, Anderson concludes that we still don’t have chess machines that resembles human intelligence in the way it plays the game – although they don’t need to.

The humans made errors, became anxious and feared for their reputations.

The machines, on the other hand, relentlessly applied logical calculations to the game in their attempts to win.

His thinking was that one day we might have computers that truly replicate human thinking, but the story of the last 20 years has been the rise of systems that are superior precisely because they are machines.

So, could automated trading system using artificial intelligence beat a master trader?

It has been said that machines despite their advanced technologies still lack one common ingredient in human thinking and that could ironically be the relatively simple concept of “human common sense”, but more on this in later part of our series.

That said, the power of machines and artificial intelligence in human progress is undeniable.

Even when switching his focus to quant strategies in 2016, hedge fund magnate Paul Tudor had only hoped to emphasize so-called big-data strategies—those that use powerful computers and the internet to crunch mountains of raw data to uncover previously invisible insights in the market, an approach already emphasized at a number of hedge funds that were performing well.

Institutional Investor’s (an international finance publisher) 17th-annual Hedge Fund 100 ranking of the 100 largest hedge fund firms in the world as of year-end 2017 finds that the four biggest hedge fund firms — and five of the six biggest — on its ranking rely largely or fully on quantitative strategies using computers to make their investment decisions and have continued to attract assets.
The value of assets managed by the quants alone passed USD1 trillion in 2018.

In the world of trading and finance, the more apt question is probably NOT to ask whether the power of machines and algorithm could beat a master investor or trader.
As the world’s famous hedge fund trader magnate Paul Tudor had said, no man is better than a machine, and no machine is better than a man with a machine

The most powerful force in trading is when human intelligence is combined with machine intelligence (automation, process and artificial learning).

No machine or automation is likely to replace elite traders COMPLETELY unless one chose to let the machine take over with no understanding of its functions such as with pure automated black box trading system.

In our continuing upcoming series, we look further at how quantitative strategies using algorithm have become so popular and why it is increasingly being favored as technologies evolved in big data and artificial intelligence.

At Malacca Securities Quantitative Trading and Analytics Division, we strongly believe that big data analytics and algorithm trading is the future of trading for the crème de la crème traders and institutional funds in the market.

Join and network with us at our mPower Algorithm and mPower Trading programs and at our Elite Education courses.