GS trading source code stolen

[tony m]

Nice try Serge - but you forgot the 11th commandment: don't get caught

 

[Andy Nguyen]

AdvancedTrading (supposedly a more technical place) takes on this whole episode and promptly plays down the significant of this whole thing

The Real Story of Trading Software Espionage
By Rob Iati, Partner, The TABB Group
Jul 10, 2009

Much has been made of the 32MB of Goldman Sachs' proprietary algorithmic trading code ("trading secrets") allegedly stolen by Sergey Aleynikov, now portrayed in the financial media as the new Julius Rosenberg, Aldrich Ames, Robert Hanssen and John Walker all rolled into one. That may prove to be true; but while it makes for a great news story at this point in time, it highlights the new significance of high-frequency trading — which is built on this technology — in the marketplace.

We are all keenly aware that electronic routing and execution has become the mechanism by which our capital markets operate. Algorithms account for more than 25% of all shares traded by the buy side today — a number steadily rising for several years now. However, the incredible capabilities offered by technology have given meteoric rise to a relative few high-frequency proprietary trading firms that now wield far greater influence on the markets today than most people recognize. The familiar names of Lehman, Bear and Merrill are being replaced by less familiar ones like Wolverine, IMC and Getco.

For example, high-frequency trading firms, which represent approximately 2% of the 20,000 or so trading firms operating in the U.S. markets today, account for 73% of all U.S. equity trading volume. These companies include proprietary trading desks for a small number of major investment banks, less than 100 of the most sophisticated hedge funds and hundreds of the most secretive prop shops, all of which operate with one thing in mind — capture profit opportunities by being smarter and faster than the closest competition.

They are, as a rule, secretive, stealthy, smart, and relatively unknown. The key to being smarter is their unique technology that enables them to profit on a number of these quantitative strategies, which they will protect at all costs.

The Value of High-Frequency Trading Strategies

Proprietary trading takes in a number of unique strategies, including market making, arbitrage (ETFs, futures, options), pairs trading and others based on the linked trading of more than one asset class, e.g., futures index and cash equities. In fact TABB Group estimates that annual aggregate profits of low-latency arbitrage strategies exceed $21 billion, spread out among the few hundred firms that deploy them. While we know all the large investment banks such as Goldman Sachs are committed to prop trading profitability, the hundreds of smaller, private high-frequency prop shops extend much greater influence in the marketplace by providing liquidity that keeps activity flowing.

While none of us knows the ingredients of Goldman's "secret sauce," we can say that any algorithmic code in and of itself is precious but has limited value until placed in the right circumstances. Those circumstances are not available to just any Tom, Dick or Sergey, but represent the core strategy of the fast-rising high-frequency trading firms.

First, strategies that optimize the value of high-frequency algorithmic trading are highly dependent on ultralow latency. The right decisions are based on flowing information into your algorithm microseconds sooner than your competitors. To realize any real benefit from implementing these strategies, a trading firm must have a real-time, colocated, high-frequency trading platform—one where data is collected and orders are created and routed to execution venues in sub-millisecond times.

Next, since many of these strategies require transacting in more than one asset class and across multiple exchanges often located hundreds of miles apart, i.e., New York to Chicago, that infrastructure will often require round-trip long-haul connectivity between the data centers.

Last and most important, this code has a limited shelf life, whose competitive advantage is diluted with each second it is outstanding. While a prop desk's high level trading strategy may be consistent over time, the micro-level strategies are constantly altered — growing stale after a few days if not sooner — for two important reasons. First, because high-frequency trading depends on ridiculously precise interaction of markets and mathematical correlations between securities, traders need to regularly adjust code — sometimes slightly, sometimes more — to reflect the subtle changes in the dynamic market. The speed and volatility of today's markets is such that the relationships forming the core of our algorithm strategies often change within seconds of our ability to implement the very strategies that exploit them. Second, competitive intelligence is so good across all rival trading firms that each is exposed to the increasing susceptibility of their strategies being reverse-engineered, turning their most profitable ideas into their most risky. As a result, any firm acquiring the "stolen" code would gain benefit from it for no more than a few days before that firm would need to adjust the code to the dynamic conditions. Since these changes build on themselves, in a matter of weeks that code would look quite different from that which was originally "stolen."

There's no doubt that Goldman Sachs, or any other proprietary trading firm, could indeed lose tens of millions of dollars from its proprietary trading if their strategies are stolen — and that is very serious. The competitors that obtain access to these trading secrets could (and would) use it to front-run or trade against it, ruining even the most well-planned tactics. This news story contains many very important subplots: trading espionage, the necessity for a trading firm to have sophisticated security systems built around its technology, the requirements for risk management, and even the potential for proprietary trading software to be targeted on a wider scale for terrorist activity; but more than anything else it highlights the critical role played by high-frequency prop trading in this new market.

The Real Story of Trading Software Espionage by Advanced Trading

 

[Andy Nguyen]

More stuff to read Arrest Over Trading Software Illuminates a Wall St. Secret - NYTimes.com

<nyt_headline version="1.0" type=" "> Arrest Over Software Illuminates Wall St. Secret </nyt_headline>

<nyt_byline version="1.0" type=" "> http://topics.nytimes.com/top/reference/timestopics/people/b/alex_berenson/index.html?inline=nyt-per
</nyt_byline> Flying home to New Jersey from Chicago after the first two days at his new job, Sergey Aleynikov was prepared for the usual inconveniences: a bumpy ride, a late arrival.
He was not expecting Special Agent Michael G. McSwain of the F.B.I.

At 9:20 p.m. on July 3, Mr. McSwain arrested Mr. Aleynikov, 39, at Newark Liberty Airport, accusing him of stealing software code from Goldman Sachs, his old employer. At a bail hearing three days later, a federal prosecutor asked that Mr. Aleynikov be held without bond because the code could be used to "unfairly manipulate" stock prices.

This case is still in its earliest stages, and some lawyers question whether Mr. Aleynikov should be prosecuted criminally, or whether a civil suit may be more appropriate. But the charges, along with civil cases in Chicago and New York involving other Wall Street firms, offer a glimpse into the turbulent world of ultrafast computerized stock trading.
Little understood outside the securities industry, the business has suddenly become one of the most competitive and controversial on Wall Street. At its heart are computer programs that take years to develop and are treated as closely guarded secrets.
Mr. Aleynikov, who is free on $750,000 bond, is suspected of having taken pieces of Goldman software that enables the buying and selling of shares in milliseconds. Banks and hedge funds use such programs to profit from tiny price discrepancies among markets and in some instances leap in front of bigger orders.

Defenders of the programs say they make trading more efficient. Critics say they are little more than a tax on long-term investors and can even worsen market swings.
But no one disputes that high-frequency trading is highly profitable. The Tabb Group, a financial markets research firm, estimates that the programs will make $8 billion this year for Wall Street firms. Bernard S. Donefer, a distinguished lecturer at Baruch College and the former head of markets systems at Fidelity Investments, says profits are even higher.

 

[birdCatcher]

This is wild. It appears the code stolen is only the one the guy worked on (equity). Otherwise, I don't know how he would be able to steal the whole secret sauce.

The guy's LinkedIn profile is at Serge Aleynikov - LinkedIn
[IMGa=right]http://media.linkedin.com/mpr/mpr/shrink_80_80/p/1/000/006/15a/3381783.jpg[/IMGa]
Serge Aleynikov
VP, Equity Strategy at Goldman Sachs
Greater New York City Area Information Technology and Services
Current
VP, Equity Strategy at Goldman Sachs
Past
Director, Routing R&D at IDT Corp
Lead Development Engineer at IDT Corp
President, Sr. Technical Director at Orbit Communications & Networking Dimension
see all...
Education
Rutgers, The State University of New Jersey-New Brunswick
Rutgers, The State University of New Jersey-New Brunswick
Moscow Institute of Transportation Engineering (MIIT)

Serge Aleynikov - LinkedIn

Anyone wants to connect to him

I was curious to know - what do people think about following a career path similar to that of Serge's if the aim is to get into algotrading in N years time? Ofcourse this is assuming that one actually has the opportunities to branch out into very similar engineering jobs to start with. Is the path this way (i.e. BEng -> MFE -> N yrs EE job -> AT) considered more thorough preparation for algotrading?

 

[joel_b]

He had a specialized skill set in intercepting network traffic, I don't think it's a traditional career path, he just happened to be what they needed. That's how the whole FE field started anyway, pulling engineers and PHDs from other fields to apply knowledge to financial trading. MFE is the more formalized path that has taken form over time due to the field achieving a critical mass in people, impact and awareness.

 

[GoIllini]

I was curious to know - what do people think about following a career path similar to that of Serge's if the aim is to get into algotrading in N years time? Ofcourse this is assuming that one actually has the opportunities to branch out into very similar engineering jobs to start with. Is the path this way (i.e. BEng -> MFE -> N yrs EE job -> AT) considered more thorough preparation for algotrading?

Here is one path that I see play out every once in a while:

BEng-> Financial Programmer -> Specialized/Quant Programmer -> Algorithmic Trader

As we've seen, quant programming sometimes requires a certain degree of trust. There's a certain level of comfort with hiring someone in who has a history with the firm rather than someone from outside; criminal background checks and drug tests can only tell you so much. In one internal interview that my friend was recently in, it was emphasized that loyalty to the firm was extremely important.

Your best chance to make it into algorithmic trading is to get hired at an investment bank or hedge fund as a financial programmer, put in your two years, and then see if you can make the jump internally.

 

[Stefan Zota]

I was curious to know - what do people think about following a career path similar to that of Serge's if the aim is to get into algotrading in N years time? Ofcourse this is assuming that one actually has the opportunities to branch out into very similar engineering jobs to start with. Is the path this way (i.e. BEng -> MFE -> N yrs EE job -> AT) considered more thorough preparation for algotrading?

He worked in a quantitative area, but I don't think you can consider him a quant.

Algorithmic Trading is one area of quantitative finance that is a bit "special". At the moment it is much more oriented on implementation and price discovery rather then models/complex algorithms. Many times, fastest execution means higher chance of profit.
Now in this environment, Comp. Sci. skills (OS, networking, low-level programming) are very relevant. This is not related to an MFE curriculum, in fact these skills are better acquired in a engineering school.
Of course this is valid now, in 5 years, things may change completely.

 

[Uncle Max]

Ex-Goldman Programmer Aleynikov Indicted Over Software Theft
​

By David Glovin

Feb. 12 (Bloomberg) -- Former Goldman Sachs Group Inc. computer programmer Sergey Aleynikov was indicted on federal charges that he stole trading software from the bank.

The indictment, unsealed yesterday in Manhattan federal court, follows appeals to prosecutors by Aleynikov’s lawyer that the government dismiss the charges. Aleynikov must now enter a plea as the case moves closer to a trial.

Aleynikov, 40, was arrested July 3 and charged with theft of trade secrets and transportation of stolen property in foreign commerce. At a July 4 court appearance, a prosecutor said the alleged theft is the “most substantial” that New York-based Goldman Sachs can recall.

“Proprietary information and trade secrets are sometimes the most valuable assets of a business,” FBI Assistant Director-in-Charge Joseph Demarest said yesterday in a statement.

The proprietary code, worth millions of dollars, lets the company do “sophisticated, high-speed and high-volume trades on various stock and commodities markets,” prosecutors have said in court documents. Aleynikov planned to earn three times his salary by joining a new company and engaging in high-volume automated trading, prosecutors said at the time of his arrest.

Teza

According to the indictment, Aleynikov’s last day at Goldman was June 5, before he left to join Teza Technologies LLC, a Chicago-based firm co-founded by former Citadel Investment Group LLC trader Misha Malyshev. Beginning at 5:20 p.m., he began transferring “substantial portions” of Goldman’s code for its trading platform to an outside server in Germany, the indictment says.

“After transferring the files, Aleynikov deleted the program he used to encrypt the files and deleted the computer’s ‘bash history,’ which records the most recent commands executed on his computer,” U.S. Attorney Preet Bharara said in a statement.

Teza suspended Aleynikov after his arrest and has since fired him. On July 2, Aleynikov attended meetings at Teza’s office and brought his laptop computer and another storage device, each of which held Goldman’s source code, the indictment says.

Defense attorney Sabrina Shroff didn’t immediately return a call seeking comment yesterday. She said at an Aug. 10 court hearing that prosecutors “may be under a false impression” about the case. Only 32 of 1,024 megabits of the software code was transferred, Shroff has said.

Dual Citizenship

Aleynikov, who is free on $750,000 bond, lives in New Jersey and holds dual U.S. and Russian citizenship. He and his lawyer have said the files that prosecutors said he stole weren’t shared with anyone and he took them so he could work from home.

The indictment says Aleynikov transferred thousands of files related to the firm’s trading program over the course of his career, without telling Goldman.

Aleynikov worked at Goldman from 2007 until June, the government said in its July criminal complaint. He was part of a team of workers responsible for improving the computer platform.

He faces one count of theft of trade secrets, one count of transportation of stolen property in foreign commerce, and one count of unauthorized computer access. He faces as long as 25 years in prison if convicted.

Before joining Goldman, Aleynikov worked for about eight years at IDT Corp., the U.S. vendor of prepaid calling cards, where he led the team responsible for developing routing systems, according to the profile on the social-networking site LinkedIn.

Michael DuVally, a spokesman for Goldman, declined to comment.

The case is U.S. v. Aleynikov, U.S. District Court, 09-mag-1553, Southern District of New York (Manhattan).

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