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Last spring, Dow Jones launched a new service called Lexicon, which sends real-time financial news to professional investors. This in itself is not surprising. The company behind The Wall Street Journal and Dow Jones Newswires made its name by publishing the kind of news that moves the stock market.

But many of the professional investors subscribing to Lexicon aren't human—they're algorithms, the lines of code that govern an increasing amount of global trading activity—and they don't read news the way humans do.

They don't need their information delivered in the form of a story or even in sentences. They just want data—the hard, actionable algorithmic trading tool that those words represent. Lexicon packages the news in a way that its robo-clients can understand. It scans every Dow Jones story in real time, looking for textual clues that might indicate how investors should feel about a stock. It then sends that information in machine-readable form to its algorithmic subscribers, which can parse it further, using the resulting algorithmic trading tool to inform algorithmic trading tool own investing decisions.

Lexicon has helped automate the process of reading the news, drawing insight from it, and using that information to buy or sell a stock. The machines aren't there just to crunch numbers anymore; they're now making the decisions.

That increasingly describes the entire algorithmic trading tool system. Over the past decade, algorithmic trading has overtaken the industry. From the single desk of a startup hedge fund to the gilded halls of Goldman Sachscomputer code is now responsible for most of the activity on Wall Street.

By some estimates, computer-aided high-frequency trading now accounts for about 70 percent of total trade volume. Increasingly, the market's ups and downs are determined not by traders competing to see who has the best information or sharpest business mind but by algorithms feverishly scanning for faint signals of potential profit. Algorithms have become algorithmic trading tool ingrained in our financial system that the markets could not operate without them.

At the algorithmic trading tool basic level, computers help prospective buyers and sellers of stocks algorithmic trading tool one another—without the bother of screaming middlemen or their commissions. High-frequency traders, sometimes called flash tradersbuy and sell thousands of shares every second, executing deals so quickly, and on such a massive scale, that they can win or lose a fortune if the price of a stock fluctuates by even a few cents.

Other algorithms are slower but more sophisticated, analyzing earning statements, stock performance, and newsfeeds to algorithmic trading tool attractive investments that others may have missed. The result is a system that is more efficient, faster, and algorithmic trading tool than any human.

It is also harder to understand, predict, and regulate. Algorithms, like most human traders, tend to follow a fairly simple set of rules. But they also respond instantly to ever-shifting market conditions, taking into account thousands or millions of data points every second.

And each trade produces new data points, creating a algorithmic trading tool of conversation in which machines respond in rapid-fire succession to one another's actions. At its best, this system represents an efficient and intelligent capital allocation machine, a market ruled by precision and mathematics rather than emotion and fallible judgment. But at its worst, it is an inscrutable and uncontrollable feedback loop.

Individually, these algorithms may be easy to control but when they interact they can create unexpected behaviors—a conversation that can overwhelm the system it was built to navigate. On May 6,the Dow Jones Industrial Average inexplicably experienced a series of drops that came to be known as the flash crashat one point shedding some points in five minutes.

Less than five months later, Progress Energy, a North Carolina utility, watched helplessly as its share price fell 90 percent. Also in late September, Apple shares dropped nearly 4 percent in just 30 seconds, before recovering a few minutes later.

These sudden drops are now routine, and it's often impossible to determine algorithmic trading tool caused them. But most observers pin the blame on the legions of powerful, superfast trading algorithms—simple instructions that interact to create a market that is incomprehensible to the human mind and impossible to predict. A good session player is hard to find, but ujam is always ready to rock.

The Web app doubles as a studio band and a recording studio. It analyzes a melody and then produces sophisticated harmonies, bass lines, drum tracks, horn parts, and more. Before ujam's AI can lay down accompaniment, it must algorithmic trading tool out which notes the user is singing or playing. Once it recognizes them, the algorithm searches for chords to algorithmic trading tool the tune, using a mix of statistical techniques and hardwired musical rules. The stats are part of the software's AI and can generate myriad chord progressions.

The rules-based module then uses its knowledge of Western musical tropes to narrow the chord options to a single selection. The service is still in alpha, but it has attracted 2, testers who want to use the AI to explore their musical creativity—and they have the recordings to prove it.

As ujam gathers more data on algorithmic trading tool preferences and musical tastes, programmers feed this info back into the system, improving its on-the-fly performance. In this respect at least, ujam is like a human: It gets better with practice. Ironically enough, the notion of using algorithms as trading tools was born as a way of empowering traders.

Before the age of electronic trading, large institutional investors used their size and connections to wrangle better terms from the human middlemen that executed buy and sell orders. Bradley algorithmic trading tool among the first traders to explore the power of algorithms in the late '90s, creating approaches to investing that favored brains over access. It took him nearly three years to build his stock-scoring program. First he created a neural network, painstakingly training it to emulate his thinking—to recognize the combination of factors that his instincts and experience told him were indicative of a significant move in a stock's price.

But Bradley didn't just want to build a machine that would think the same way he did. He wanted his algorithmically derived system to algorithmic trading tool at stocks in a fundamentally algorithmic trading tool smarter—way than humans ever could. So inBradley assembled a team of engineers to determine which characteristics were most predictive of a stock's performance. They identified a number of variables—traditional measurements like earnings growth as well as more technical factors.

Altogether, Bradley came up with seven key factors, including the judgment of his neural network, that he thought might be useful in predicting a algorithmic trading tool performance. He then tried to determine the proper weighting of each characteristic, using a publicly available program from UC Berkeley called the differential evolution optimizer. Bradley started with random weightings—perhaps earnings growth would be given twice the algorithmic trading tool of revenue growth, for example.

Then the program looked at the best-performing stocks at a given point in time. It then picked 10 of those stocks at random and looked at historical data to see how well the weights predicted their actual performance. Next the computer would go back and do the same thing all over again—with a slightly different starting date or a different starting group of stocks.

For each weighting, the test would be run thousands of times to get a thorough sense of how those stocks performed. Then the weighting would be changed and the whole process would run all over again. Eventually, Bradley's team collected performance data for thousands of weightings. Once this process was complete, Bradley collected the 10 best-performing weightings and ran them once again through the differential evolution optimizer.

The optimizer then mated those weightings—combining them to create or so offspring weightings. Those weightings were tested, and the 10 best were mated again to produce another third-generation offspring. The program also introduced occasional mutations and randomness, on the off chance that one of them might produce an accidental genius.

After dozens of generations, Bradley's team discovered ideal weightings. Bradley's effort was just the beginning. Before algorithmic trading tool, investors and portfolio managers began to tap the world's premier math, science, and engineering schools for talent. These academics brought to trading desks sophisticated knowledge of AI methods from computer science and statistics.

And they started applying those methods to every aspect of the financial industry. Some built algorithms to perform the familiar function of discovering, buying, and selling individual stocks a practice known as proprietary, or "prop," trading.

Others devised algorithms to help brokers execute large trades—massive algorithmic trading tool or sell orders that take a while to go through and that become vulnerable to price algorithmic trading tool if other traders sniff them out before they're completed.

These algorithms break up algorithmic trading tool optimize those orders to conceal them from the rest of the market. This, algorithmic trading tool enough, is known as algorithmic trading. Still others are used to crack those codes, to discover the massive orders that other quants are trying to conceal. This is called predatory trading. The result is a universe of competing lines of code, each of them trying to algorithmic trading tool and one-up the other.

And the job of the algorithmic trader is to make that submarine as stealth as possible. Meanwhile, these algorithms tend to see the market from a machine's point of view, which can be very different from a human's. Rather than focus on the behavior of individual stocks, for instance, many prop-trading algorithms look at the market as a vast weather system, algorithmic trading tool trends and movements that can be predicted and capitalized upon. These patterns may not be visible to humans, but computers, with their ability to analyze massive amounts of data at lightning speed, can sense them.

The partners at Voleon Capital Management, a three-year-old firm in Berkeley, California, take this approach. Voleon engages in statistical arbitrage, which involves sifting through enormous pools of data for patterns that can predict subtle movements across a whole class of related stocks. Situated on the third floor of a run-down office building, Voleon could be any other Bay Area web startup. Geeks pad around the office in jeans and T-shirts, moving amid half-open boxes and scribbled whiteboards.

To hear them describe it, their trading strategy bears more resemblance to those data-analysis projects than to classical investing. Algorithmic trading tool, McAuliffe and Kharitonov say that they don't even know what their bots are looking for or how they reach their conclusions.

Extract the signal from the noise,'" Kharitonov says. We're playing on a different field, trying to exploit effects that are too complex for the human brain. They require you to look at hundreds of thousands of things simultaneously and to be trading a little bit of each stock.

Humans just can't do that. To the human eye, an x-ray is a murky, lo-res puzzle. But to a machine, an x-ray—or a CT or an MRI scan—is a dense data field that can be assessed down to the pixel. No wonder AI techniques have been applied so aggressively in the field of medical imaging. But the machines can. Bartron's software—about to undergo clinical trials—could bring a new level of analysis to the field.

It aggregates hi-res image data from multiple sources—x-rays, MRIs, ultrasounds, CT scans—and then groups together biological structures that share hard-to-detect similarities. For instance, the algorithm could examine several images of the same breast to gauge tissue density; it then color-codes tissues with similar densities so a mere human can algorithmic trading tool the pattern, too.

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