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 was 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 look at stocks in a fundamentally different—and smarter—way than humans ever could. So in , Bradley 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 portfolio's 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 weight 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 long, 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 buy or sell orders that take a while to go through and that become vulnerable to price manipulation if other traders sniff them out before they're completed. These algorithms break up and optimize those orders to conceal them from the rest of the market. This, confusingly 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 outsmart 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, with 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.
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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. Indeed, 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.
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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 see the pattern, too. At the heart of the technology is an algorithm called Hierarchical Segmentation Software , which was originally developed by NASA for analyzing digital images from satellites.
The technology finds and indexes pixels that share certain properties, even if they're far apart in an image or in a different image altogether. This way, hidden features or diffuse structures within a region of tissue can be identified. In other words, puzzle solved. The culprit, the report determined, was a "large fundamental trader" that had used an algorithm to hedge its stock market position.
The trade was executed in just 20 minutes—an extremely aggressive time frame, which triggered a market plunge as other algorithms reacted, first to the sale and then to one another's behavior.
Both trades were subsequently canceled. The activity briefly paralyzed the entire financial system. The report offered some belated clarity about an event that for months had resisted easy interpretation. Legislators and regulators, spooked by behavior they couldn't explain, much less predict or prevent, began taking a harder look at computer trading. In the wake of the flash crash, Mary Schapiro, chair of the Securities and Exchange Commission , publicly mused that humans may need to wrest some control back from the machines.
In the months after the flash crash, the SEC announced a variety of measures to prevent anything like it from occurring again. All portfolio-allocation decisions are made by computerized quantitative models. The success of computerized strategies is largely driven by their ability to simultaneously process volumes of information, something ordinary human traders cannot do. Market making involves placing a limit order to sell or offer above the current market price or a buy limit order or bid below the current price on a regular and continuous basis to capture the bid-ask spread.
Another set of HFT strategies in classical arbitrage strategy might involve several securities such as covered interest rate parity in the foreign exchange market which gives a relation between the prices of a domestic bond, a bond denominated in a foreign currency, the spot price of the currency, and the price of a forward contract on the currency. If the market prices are sufficiently different from those implied in the model to cover transaction cost then four transactions can be made to guarantee a risk-free profit.
HFT allows similar arbitrages using models of greater complexity involving many more than 4 securities. A wide range of statistical arbitrage strategies have been developed whereby trading decisions are made on the basis of deviations from statistically significant relationships. Like market-making strategies, statistical arbitrage can be applied in all asset classes.
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A subset of risk, merger, convertible, or distressed securities arbitrage that counts on a specific event, such as a contract signing, regulatory approval, judicial decision, etc. Merger arbitrage also called risk arbitrage would be an example of this. Merger arbitrage generally consists of buying the stock of a company that is the target of a takeover while shorting the stock of the acquiring company.
Usually the market price of the target company is less than the price offered by the acquiring company. The spread between these two prices depends mainly on the probability and the timing of the takeover being completed as well as the prevailing level of interest rates. The bet in a merger arbitrage is that such a spread will eventually be zero, if and when the takeover is completed.
The risk is that the deal "breaks" and the spread massively widens. One strategy that some traders have employed, which has been proscribed yet likely continues, is called spoofing. It is the act of placing orders to give the impression of wanting to buy or sell shares, without ever having the intention of letting the order execute to temporarily manipulate the market to buy or sell shares at a more favorable price.
This is done by creating limit orders outside the current bid or ask price to change the reported price to other market participants. The trader can subsequently place trades based on the artificial change in price, then canceling the limit orders before they are executed. The trader then executes a market order for the sale of the shares they wished to sell. The trader subsequently cancels their limit order on the purchase he never had the intention of completing. Quote stuffing is a tactic employed by malicious traders that involves quickly entering and withdrawing large quantities of orders in an attempt to flood the market, thereby gaining an advantage over slower market participants.
HFT firms benefit from proprietary, higher-capacity feeds and the most capable, lowest latency infrastructure.
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Researchers showed high-frequency traders are able to profit by the artificially induced latencies and arbitrage opportunities that result from quote stuffing. Network-induced latency, a synonym for delay, measured in one-way delay or round-trip time, is normally defined as how much time it takes for a data packet to travel from one point to another. Low-latency traders depend on ultra-low latency networks.
They profit by providing information, such as competing bids and offers, to their algorithms microseconds faster than their competitors. This is due to the evolutionary nature of algorithmic trading strategies — they must be able to adapt and trade intelligently, regardless of market conditions, which involves being flexible enough to withstand a vast array of market scenarios.
Most of the algorithmic strategies are implemented using modern programming languages, although some still implement strategies designed in spreadsheets.