The Future of Trading
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2015: The Always On Desk
November 4, 2010
Given is the fact that the trading process will be increasingly automated.
“The machines are getting smarter. We're outsourcing more of the nuts and bolts of the actual trading process,”said Jose Marques, managing director and global head of Electronic Equity Trading for the Markets division of Deutsche Bank Securities.
But, more critically, the algorithms that are used to effect trades will only get more sophisticated.
"We're moving toward algorithms where the machine can understand what's actually in the trader's head or what the portfolio manager's real objective is in executing that trade and be able to make the real-time tradeoff between liquidity and the cost of that liquidity,'' Marques said.
To date, algorithms have been fairly simplistic. The most widely used benchmark is what is called the Volume Weighted Average Price of a stock. The “VWAP” is calculated by multiplying the price against the shares in every transaction in a stock, then adding them all up, then dividing by the total number of shares traded that day. If the average price of a firm’s trades is better than the average weighted price, then it is considered a good trade.
What’s next? Algos that will already be inferring what’s inside a trader’s head, said Marques.
"Rather than giving mechanical instructions such as 'go buy 10,000 shares by time-slicing it every 15 seconds, 300 shares at a time,’ we may see a future situation where the trader is able to communicate to the machine and say, my portfolio objective is to minimize my implementation costs and given my horizon, I'd like to be relatively aggressive."
With the right formulas, a company’s trading systems will spot opportunities and act on them, with the prior approval of trading managers.
A basic example is pairs trading, said Jamil Nazarali, senior managing director and Global Head of the Electronic Trading Group for Knight Capital Group, which provides market access and trade execution services to buy- and sell-side firms.
In this tactic, traders find two securities, often rivals, where their daily price movements are very similar because they face very similar conditions. The trick is to act when the correlation weakens and one price moves up while the other moves down.
An algorithm, Nazarali notes, can fairly reliably be built that can make the correct buy and sell orders when the two stocks are moving apart and profiting from those moves by the time the prices converge again, to their traditional correlation.
Then there are “genetic” algorithms which evolve over time toward better solutions to trading in stocks or other financial instruments. These are often considered forms of artificial intelligence, where computer coding tries to replicate the thinking process of the human brain.
The approach has not made huge approaches onto Wall Street. But Adam Afshar, the president of Hyde Park Global Investments in Atlanta, employs no analysts, portfolio managers or traders to identify arbitrage opportunities and price discrepancies. He relies solely on a robotic trading system to carry out instructions – and figure out how to improve trading strategies in the process.