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Wednesday, 21 September 2011

Rage Against The Machine: Wall-Street Markets Matrix Secret Exposed

The Matrix, played with money. Computers ruling the stock markets, the world of high-speed trading, Wall Street's secret and financial markets advantage Exposed:

High-speed trading

They're unknown and invisible to most of us, but electronic trading programs now rule the stock markets.

"Anyone who is doing anything sensible right now is either losing money or is out of the market entirely." These are the words of a quant trader, who is seeing something scary in the capital markets. Scary enough to merit a warning that we could be on the verge of another October 87, August 2007, or January 2008.

There is not much publicly available data to follow what goes on in the mystery shrouded quant world and High frequency trading. However, one of the charts that tracks the market neutral performance is the HSKAX, or the Highbridge Statistical Market Neutral Fund, presented below. As one can see we have crossed into major statistically deviant territory, likely approaching a level that is 6 standard deviations away from the recent norms.

High-frequency trading acording to wikipedia

High frequency trading (HFT) is the use of sophisticated technological tools to trade securities like stocks or options, and is typically characterized by several distinguishing features[1]:
HFT is highly quantitative, employing computerized algorithms to analyze incoming market data and implement proprietary trading strategies;
HFT usually implies a firm holds an investment position only for very brief periods of time - even just seconds - and rapidly trades into and out of those positions, sometimes thousands or tens of thousands of times a day;
HFT firms typically end a trading day with no net investment position in the securities they trade;
HFT operations are usually found in proprietary firms or on proprietary trading desks in larger, diversified firms;
HFT strategies are usually very sensitive to the processing speed of markets and of their own access to the market.
In high-frequency trading, programs analyze market data to capture trading opportunities that may open up for only a fraction of a second to several hours.[2] High-frequency trading, (HFT), uses computer programs and sometimes specialised hardware [3] to hold short-term positions in equities, options, futures, ETFs, currencies, and other financial instruments that possess electronic trading capability.[4] High-frequency traders compete on a basis of speed with other high frequency traders, not long-term investors (who typically look for opportunities over a period of weeks, months, or years), and compete with each other for very small, consistent profits.[5][6] As a result, high-frequency trading has been shown to have a potential Sharpe ratio (measure of reward per unit of risk) thousands of times higher than the traditional buy-and-hold strategies. By 2010 High Frequency Trading accounted for over 70% of equity trades taking place in the US and was rapidly growing in popularity in Europe and Asia. Aiming to capture just a fraction of a penny per share or currency unit on every trade, high-frequency traders move in and out of such short-term positions several times each day. Fractions of a penny accumulate fast to produce significantly positive results at the end of every day.[5] High frequency trading firms do not employ significant leverage, do not accumulate positions, and typically liquidate their entire portfolios on a daily basis.[6]

One financial industry source claims algorithmic trading, including high-frequency trading, substantially improves market liquidity.[6] An academic study shows [7] additional benefits, including lowering the costs of trading,[7] increasing the informativeness of quotes,[7] improved linkage between markets,[7] and other positive spillover effects, at least in quiescent or stable markets; the authors of this study also note that "it remains an open question whether algorithmic trading and algorithmic liquidity supply are equally beneficial in more turbulent or declining markets...algorithmic liquidity suppliers may simply turn off their machines when markets spike downward."[7]

Algorithmic and high frequency trading were both implicated in the May 6, 2010 Flash Crash, when high frequency liquidity providers were in fact found to have withdrawn from the market.[8][9][10][11][12][13][14][15] A July, 2011 report by the International Organization of Securities Commissions (IOSCO), an international body of securities regulators, concluded that while "algorithms and HFT technology have been used by market participants to manage their trading and risk, their usage was also clearly a contributing factor in the flash crash event of May 6, 2010.
What is high-speed trading?

It’s Wall Street’s winning edge. By harnessing massive computer power to buy and sell stocks in the blink of an eye, high-speed traders leverage tiny changes in value to make huge profits. The technique was pioneered in the early years of this decade by a hedge fund that hired astrophysicists, mathematicians, and statisticians to devise electronic trading programs. Other firms, including Goldman Sachs and Credit Suisse, quickly followed suit. Few outside the securities industry knew much about the practice until computer glitches helped cause the Dow to plummet 600 points in 15 minutes in May. But high-speed trading—also called high-frequency trading—now accounts for up to 70 percent of all trading in shares listed on the New York Stock Exchange.

What advantage does it provide?

Two-tenths of a second, which is enough to make traders rich. Using a high-speed system, traders can buy into a stock rally or sell into a decline slightly ahead of the pack, gaining a more favorable price than ordinary investors making the same trade a fraction of a second later. The advantage rarely amounts to more than a couple of cents, but compounded over the course of millions of daily transactions, it can add up to tens of millions of dollars. High-speed trading “is where all the money is getting made,” says William Donaldson, former head of the NYSE. “If an individual investor doesn’t have the means to keep up, they’re at a huge disadvantage.”

How does it work?

Automatically. High-speed trading firms, from Wall Street powerhouses like Goldman Sachs to little-known shops with a handful of employees, program their computers to scan markets and exploit ephemeral price differences on the same stock trading on different exchanges. Their computer algorithms automatically generate thousands of transactions per second to profit from a price difference. For a fee, many stock exchanges even allow high-speed traders to get a few milliseconds’ preview of orders of 10,000 or more shares, giving them added incentive to handle unwieldy orders. “It is a rigged game,” says Sal Arnuk of Themis Trading, a brokerage firm. Some high-speed traders go one step further, paying a stock exchange for the right to install computer servers right next to the exchange’s own servers, a practice known as “co-location.”

Does co-location increase speed?

Yes. The physical proximity to the exchange server reduces the time from when a firm’s buy or sell order is entered and when it’s executed. “By co-locating,” says Adam Afshar of Hyde Park Global, a high-speed trading firm, “we are able to take 21 milliseconds off our trades. In the past, 21 milliseconds was a trivial matter. Now it’s a pivotal matter.” Several academic studies have found that shaving even one millisecond off every trade can be worth $100 million a year to a large, high-speed trading firm.

Does this trading distort markets?

Its fans claim it makes them more efficient. In this view, high-speed traders actually perform a valuable service by adding liquidity to the market, meaning they generate so much activity that other market participants can quickly find buyers and sellers for their trades. Critics counter that high-speed traders exploit a costly technological advantage—high-speed computer systems can cost $250 million—to suck unearned profit out of the market. “They’re like locusts,” says professional trader Joe Saluzzi. “They come in, swarm the market, squeeze as much as they can, and when they’re done they’ll just move on to the next market.”

Do small investors get hurt?

Yes, but without knowing it. High-speed traders take some of the profits that would have gone to ordinary, slower investors. Institutional investors, such as pension funds and insurance companies, also suffer. Large institutions conduct multimillion-dollar stock trades, which high-speed traders sniff out using pattern-recognition algorithms. The traders then use their speed advantage to jump ahead of the institutions, buying if the institution is buying, which sends the price up, and selling if it’s selling, sending the price down. This “front-running” reduces returns to big funds’ beneficiaries and pensioners.

Did speed traders cause May’s ‘flash crash’?

Probably. Securities regulators suspect the flash crash started when a clerk entered an erroneous price for a stock-options transaction. High-speed trading algorithms spotted the anomaly and reacted with a cascade of orders to sell the stocks related to the options, sending markets plunging before humans could intervene. High-speed trading, say researchers from the Federal Reserve Bank of Chicago, “has the potential to generate errors and losses at a speed and magnitude far greater” than anything we’ve known in the past. That’s why the major stock exchanges recently installed “circuit breakers” to temporarily halt trading in a stock if its price rises or falls more than 10 percent within five minutes. Even if the breakers succeed in reducing risk, however, they can do nothing to level the high-speed playing field. “The dilemma,” says financial consultant Sang Lee, “is, do we slow down the faster guys or require that the rest of the market speed up?”

The servers that run the world

On May 6, stock traders were in full panic mode. “Guys, this is probably the craziest I’ve seen it down here ever!” shouted a trader on the floor of the Chicago Futures Exchange, amid a frenzied sell-off of stocks. Meanwhile, in a quiet, air-conditioned room in Jersey City, the only sound was a monotonous hum emitted by hundreds of Dell and Hewlett-Packard computer servers. The servers were serenely in charge, spitting out sell orders by the hundreds of thousands. Like a science-fiction film in which robots turn on their creators, the May 6 flash crash appears to have been a triumph of machines over humans. “Why do we pretend that people are in control?” asks Wall Street Journal blogger Evan Newmark. Trillions of dollars in wealth is at the mercy, he says, of “a bunch of computers making ugly, messy love with each other.”

A Report from NANEX http://www.nanex.net/StrangeDays/08252011.html named:

"Dear HFT, Please Explain This" shows that:

On August 25, 2011 at 15:45:48, in a one second period of time, there were more than 10,000 quotes and exactly zero trades in DELL. Close inspection of these quotes reveals something very disturbing. This cannot be dismissed as a computer problem or glitch. This can't be explained as stupidity or some oversight. It is not pinging for hidden liquidity. And it's certainly not price discovery. As far as we can tell, it's not adding liquidity or narrowing the bid/ask spread.

What caused this blast of 10,000 quotes in DELL appears deliberate. Of the 10,000 quotes, the bid and ask prices remain the same. The bid size also remains constant except for one change after the first 7,000 or so quotes. The only real variation is the ask size. Not a simple 2 step variation, but one that repeats in a mathematical pattern with a long cycle. This makes it difficult to detect, but it also confirms that it must be emanating from a single source.

There are approximately 4,000 stocks that quote during active trading. Which means 40 million quotes/second if just one of the 9 exchanges allow this nonsense to spread to all 4,000 symbols. You would need 40 gigabits per second of bandwidth to receive data at that rate. Unfortunately, we think it's just a matter of time, because events like this one in Dell are no longer isolated or rare. And it doesn't look like there are any grown-ups in charge.

Another article from NANEX http://www.nanex.net/StrangeDays/08292011.html where they award the HTF WITH THE PIG AWARD (We hereby award the originator of this mess the HFT Pig Award.), named :

Strange Days Aug 29th, 2011

On August 29, 2011 over a 4 second period beginning at 12:19:48, there were 44,939 quotes from one exchange in one stock: NVIDIA (symbol NVDA). All but 14 of these quotes set the NBBO. There were exactly zero trades. During this period, these NVDA quotes accounted for 81% of all NBBO quotes for all NYSE, AMEX, ARCA, and Nasdaq equities. The algorithm appears to be the same one that caused excessive quotes in Dell on August 25th.

The Chart below shows the percentage of quotes and NBBO quotes in NVDA compared to quotes from all stocks.

HFT Breaks Speed-of-Light Barrier, Sets Trading Speed World Record.
Adds a new unit of time measurement to the lexicon: fantaseconds.
On September 15, 2011, beginning at 12:48:54.600, there was a time warp in the trading of Yahoo! (YHOO) stock. HFT has reached speeds faster than the speed-of-light, allowing time travel into the future. Up to 190 milliseconds into the future, or 0.19 fantaseconds is the record so far. It all happened in just over one second of trading, the evidence buried under an avalanche of about 19,000 quotes and 3,000 individual trade executions. The facts of the matter are indisputable. Based on official UQDF/UTDF exchange timestamps, there is unmistakable proof that YHOO trades were executed on quotes that didn't exist until 190 milliseconds later!

Millions of traders depend on the accuracy of exchange timestamps -- especially after bad timestamps were found to be a key factor in the disastrous market crash known as the flash crash of May 2010. We are confident the exchange timestamp problem has been completely addressed by now: the SEC would have made sure of it. Adding accurate timestamps is not exactly rocket science; it's not even considered to be a difficult problem. Based on recent marketing materials, the exchanges are practically experts on measuring time. And with hundreds of millions in annual data feed subscriptions paid by the same subscribers expecting quotes with accurate timestamps, there is no shortage of funds to make it happen.

So we can be certain the exchange timestamps were accurate, which means that HFT has truly entered the era of the fantasecond.

But let us suppose for a moment that in reality, quotes became queued (delayed) and were timestamped after leaving this queue. After detailed analysis of the UQDF data feed (see chart below) that transmits this information to traders, we find that the traffic rate for all output lines and specifically multicast line #6 which carries YHOO, were well below peak rates. So it doesn't appear there were any capacity problems which have always been an excellent indication of feed delay.

This raises a few thorny questions.

Does this mean there are far more delays than previously thought? Is there a delay every time we see an explosion of quotes in one stock? Because recently, that, sort, of thing, happens, all the time.

Regulation NMS (Reg. NMS) makes it clear that direct exchange feeds are prohibited from having a speed advantage over the UQDF data feed. The reason is primarily because UQDF computes the NBBO, which is the key component of Reg. NMS that assures investors they are getting the best price when buying or selling stocks. This assurance is called trade-though price protection.

How does one ensure trade-through price protection if the price being protected hasn't even occurred yet?

Maybe it would be better to just fantasize about fantaseconds after all.

The first chart is a 250 ms interval chart of the NBBO in YHOO which is plotted as vertical lines and colored red if the NBBO was crossed during the interval, yellow if it was locked, and gray if it was normal. The implied quote rate is shown as a histogram at the bottom and scaled in quotes/second. We will focus on the time shown in the black circle.

Zoomed in detail of above chart in 2 millisecond intervals.  Note that this chart shows just 2.1 seconds of time.

The next chart includes trade executions which are plotted as dots or squares and sized according to trade size. A unique color and shape is assigned to each reporting exchange. What is unusual about this chart is that trades are reported ahead of quotes. The trades (dots and squares) should trail after the quotes (vertical bars). Up to the point in time that is labeled A, and after the point labeled B, trades and quotes were in sync. Between these two points, quote timestamps began falling behind.

By plotting quotes and trades from just one of the active exchanges, we can easily measure how far in time the trade messages came before the quotes and therefore estimate the minimum amount of time the quotes were delayed. Below is a 1 millisecond interval chart of YHOO showing only Nasdaq trades (black circles) and the Nasdaq bid-ask spread (gray vertical bars). Note that this chart shows just over 1 second of time.

1 NQEX Nasdaq Exchange
8 CINC National Stock Exchange
9 PHIL Philidelphia Stock Exchange
11 BOST Boston Stock Exchange
60 BATS BATS Trading
63 BATY BATS Y Exchange
64 EDGE Direct Edge A
65 EDGX Direct Edge X

The chart below shows quote message rates for UQDF and multicast line #6 which is the line that carries YHOO quotes. The time of the event is shown at the lower left. Although traffic from YHOO was a significant percentage of all traffic on UQDF, it was not high enough to indicate any problems. Note the much higher surge on the right side of the chart; there weren't any known problems at that time in YHOO.


High Frequency Trading - Regulators Seek Secret HFT Codes


The requests for proprietary code and algorithm parameters by the Financial Industry Regulatory Authority (FINRA), a Wall Street brokerage regulator, are part of investigations into suspicious market activity, said Tom Gira, executive vice president of FINRA's market regulation unit.

``It's not a fishing expedition or educational exercise. It's because there's something that's troubling us in the marketplace,'' he said in an interview.

The Securities and Exchange Commission, meanwhile, has also begun making requests for proprietary algorithmic trading data as part of its authority to examine financial firms for compliance with U.S. regulations, according to agency officials and outside lawyers.

The requests by SEC examiners are not necessarily related to any suspicions of specific wrong-doing, although the decision to ask for it can be triggered by a tip, complaint or referral.

People must Wake Up Take The Squares and Rage Against The Machine

Wake up. Organize and protest for our distinctly freedoms! Change the system one by one.

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