When HFT Steals Liquidity – Exploratory Trading In The eMini [zerohedge]

On November 12, 2012,  Adam D. Clark-Joseph published Exploratory Trading, which analyzes CFTC audit level trading data in the eMini S&P 500 futures market. This is a special, “regulators-only” data-set that contains all orders and trades, and each order and trade has a trader identifier. What this paper exposes is astounding. The following is our summary of Clark-Joseph’s paper.

Exploratory trading

Exploratory trading is a form of manipulation designed to test the market’s reaction to a trade. Probing for stop orders would be one form of exploratory trading. This paper specifically investigates exploratory trading that attempts to determine whether the bid/ask spread is about to shift up or down a level. The impact on the market would be an increase in intraday volatility. Exploratory trading distorts the market’s view of supply and demand and induces trading activity from other participants. Furthermore, as participants learn of the strategy, they will employ counter-measures – which will further muddy an accurate picture of supply and demand for everyone else. This is why regulations ban manipulation.

The Top 8 HFTs Remove Liquidity 59% of the Time

Passive market making involves buying at the bid, and selling at the ask, which earns the market maker the bid/ask spread. Passive market making provides liquidity, narrows spreads, and lowers trading costs. Aggressive trading removes liquidity: buying at the ask (removes sell orders) and selling at the bid (removes buy orders).

Between September 17, 2010 and November 1, 2010 in the eMini futures contract (December 2010 contract, symbol ESZ0):

• 41,778 accounts traded this contract
• 30 of these accounts (less than 1/10th of 1%) met criteria to be classified as HFT.

These 30 HFT accounts:

• participated in 46.7% of total trading volume.
• grossed $1.51 million per trading day.

Of these 30 HFT, the top 8:

• were aggressive 59.2% by volume (the other 22 were aggressive 35.9% by volume).
• grossed $793,342 per trading day.

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From High Frequency Trading To A Broken Market: A Primer In Two Parts [Zerohedge]

One of the topics most often discussed on Zero Hedge before the wholesale takeover of capital markets by central planners was finally accepted by everyone, was the domination of market structure (first in equities, and now in commodities, FX and even credit) by new technologies such asHigh Frequency Trading as a result of a shift in the market to a technological platform domination, away from the specialist model, and one where the entire concept of discounting, the primary role of the market in the Old Normal, has been made redundant courtesy of a race to be the first to react to events (i.e., backward looking, and direct contravention with the primary function of markets) courtesy of milli- and nanosecond, collocated servers which collect pennies in front of steamroller and generate profits purely by “virtue” of being the first to trade.

This new “technology paradigm” developed in the aftermath of the regulator complicit adoption of Reg NMS (and to a smaller extend Reg ATS) which unleashed a veritable cornucopia of “SkyNet”-controlled algorithmic traders, even as regulators did not and still do not, to this day understand all the evils that rapid technologization of the stock market has brought, most vividly captured in the May 2010 flash crash, and daily subsequent mini flash crashes, which have achieved one thing only: the total collapse of faith in the stock market by ordinary investors, who now see it for what it is (and always has been but to a far lesser extent) – a gamed casino, in which not only the house always wins and the regulators are either corrupt or clueless, or both.

And while more and more “dumb money” Joe Sixpacks awake every day to the farce that is the stock market, one entity that continues to ignore it, whether due to its own incompetence, due to conflicts of interest, due to corruption, due to co-option, or for whatever other reason, are the regulators, in this case the Securities and Exchange Commission: arguably the most incapable entity to handle the topological nightmare that the current market landscape has become. Which is to be expected: after all only an idiot would expect that when the SEC invites a GETCO, or a DE Shaw to explain and observe the fragmentation of the market, and the evils brought upon by HFT, either in a closed session or before congress, that they would voluntarily expose their business for the parasitic fallout of what once was known as capital formation. After all, it is their bread and butter: to expect them to commit professional suicide by truly showcasing the ugliness beneath it all is beyond stupid. And the flip side are various fringe blogs, which must be relegated to the tinfoil crackpot ranks of conspiracy theorist (even as conspiracy theory after conspiracy theory becomes conspiracy fact after conspiracy fact).

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CFTC Economist Revitalizes HFT, Flash Crash Debate [terrapinn]

High frequency trading (HFT) has suffered an onslaught of negativity in the media of late, serving as the sacrificial lamb of repudiation for such debacles as Facebook’s IPO glitch and Knight Capital’s $440m loss.

And a U.S. Commodity Futures Trading Commission (CFTC) economist is the latest to add insult to injury. Andrei Kirilenko – who has been with the CFTC since 2008 and a Chief Economist there since 2010, according to his official bio – has released a report indicating that small investors are greatly disadvantagedby their lack of access to supercomputers and elaborate algorithms, and stating that HFTs cost retail investors roughly $5 per contract. Kirilenko also insinuated that retail investors would leave the market, opting to “go some place that’s darker” to avoid the losses inflicted by HFTs.

While Kirilenko’s report did nothing more than articulate statistics on the competitive advantage HFTs have over smaller investors, it has nonetheless been met with a melee of criticism, dissenters (most notoriously;Dealbreaker’s Matt Levine), and recapitulations of the dangers of high frequency trading.

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Crackdown on dark pools, high-frequency trading [afr]

Australia’s major investment banks may be forced to hold stock-exchange licences under a tough new regime proposed by the government to crack down on secretive trading venues known as dark pools.

Financial Services Minister Bill Shorten raised the controversial licensing proposal as part of the next phase of reforms after finalising an initial round of measures yesterday to put the clamps on dark pools and risky high-frequency trading.

The government’s plan prompted an immediate backlash from investment bankers who warned a licensing regime would lead to a dramatic escalation in costs.

Mr Shorten defended the government’s reforms, saying it was acting to pRotect investors.

“I am aware that some investors have expressed concern about the use of high-frequency trading and dark pools,” Mr Shorten said.

“The government is acting to ensure that investors have continued confidence in Australia’s financial markets.”

HOW RULES WILL AFFECT DARK POOLS AND HFT

“I believe that these new rules will help to reduce the risk of market volatility from high-frequency trading and provide increased investor protection for retail investors and others trading in dark pools.”

Dark pools are private exchanges where investors can trade shares among themselves, away from the scrutiny of the public market.

 

 

All the major investment banks in Australia, including Citigroup, Credit Suisse, Deutsche Bank, Goldman Sachs and UBS, offer clients access to in-house dark pools.

Under rules finalised by the government on Tuesday, dark pool operators will have to offer a better price than the stock exchange before investors are allowed to use the alternative trading venues.

In a bid to control risky “high-frequency” trading of shares, stockbrokers will be required to install “kill switches” to shut their systems if they posed a threat to the market. Trading in individual stocks will also be suspended for two minutes in the event of wild share-price swings.

While theses changes were well flagged, Mr Shorten announced the government would conduct a review of whether dark pool operators should be licensed as part of the next stage of reforms. It is believed the review will consider whether dark pools should come under the market licensing regime which applies to the Australian Securities Exchange and its rival, Chi-X.

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High-Frequency Trading Synchronizes Prices in Financial Markets

High-speed computerized trading, often called high-frequency trading” (HFT), has increased dramatically in financial markets over the last decade. In the US and Europe, it now accounts for nearly one-half of all trades. Although evidence suggests that HFT contributes to the eciency of markets, there are concerns it also adds to market instability, especially during times of stress. Currently, it is unclear how or why HFT produces these outcomes. In this paper, I use data from NASDAQ to show that HFT synchronizes prices in nancial markets, making the values of related securities change contemporaneously. With a model, I demonstrate how price synchronization leads to increased efficiency: prices are more accurate and transaction costs are reduced. During times of stress, however, localized errors quickly propagate through the nancial system if safeguards are
not in place. In addition, there is potential for HFT to enforce incorrect relationships between securities, making prices more (or less) correlated than economic fundamentals warrant. This research
highlights an important role that HFT plays in markets and helps answer several puzzling questions that previously seemed dicult to explain: why HFT is so prevalent, why HFT concentrates in
certain securities and largely ignores others, and nally, how HFT can lower transaction costs yet still make prots.

Link to paper

Germany Restricts High-Frequency Trading, Chicago Fed Recommends Same [commissionideas.blogspot]

“Algos-Only” Tomorrow As NYSE Shuts Floor Trading Due To Sandy [Zerohedge]

The NYSE has just released a statement clarifying its hours tomorrow – due to the storm:

• *NYSE TRADING FLOOR TO CLOSE TOMORROW; ALL TRADING TO BE ON ARCA

So, hold tight as all those low-lying humans will have left the building in the calm thoughtful hands of Johnny-5 and his friends.

Via Bloomberg:

Oct. 28 (Bloomberg) — The New York Stock Exchange said it will shut its trading floor starting tomorrow and invoke contingency plans to move all trading to NYSE Arca, its electronic exchange, as Hurricane Sandy heads toward the city.

 

“The re-opening of physical trading floor operations is subject to city and state determinations and local conditions; updates will be forthcoming,” NYSE Euronext said in a statement today.

 

NYSE Amex Options will open electronically and NYSE MKT, formerly known as NYSE Amex, will be suspended, the exchange operator said.

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Algorithmic Trading to Algorithmic Campaigning, Behind the Political Scene w/Sasha Issenberg

Nanex ~ 10-Oct-2012 ~ FXB – New Algo in town

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Guest Post: Nanex: Investors Need To Realize The Machines Have Taken Over [Zerohedge]

Submitted by Adam Taggart of Peak Prosperity,

Germany Does What The SEC Hasn’t – Prepares To Ban HFT [Zerohedge]

The EU assembly just voted affirmatively to impose a spate of rules to control ‘high-frequency-trading that, as the WSJ reports, was advanced by Germany following their concerns that speedy traders have brought instability to markets. It is somehow reassuring that three-years after we first brought HFT to the mainstream’s agenda, at least one nation is taking it seriously, doing something about it, instead of being filibustered into the ‘liquidity-providing’ meme. The rules will initially require registration, collect fees on excessive use of HFT methods, and install circuit breakers with the goals to “limit the risks associated with high-frequency trading” per a senior German FinMin; but the more stringent rules to come will have the greatest impact as they intend to includerequirements for orders to rest on the exchange book for at least half-a-second, and potentially order-to-trade ratio caps. Not surprisingly, the HFTs believe a “one-size-fits-all approach would be very harmful.” Indeed – to their profits.

Via WSJ: Germany to Tap Brakes On High-Speed Trading

BERLIN—Germany is set to advance a bill Wednesday imposing a spate of new rules on high-frequency trading, escalating Europe’s sweeping response to concerns that speedy traders have brought instability to the markets.

The measure seeks to require traders to register with Germany’s Federal Financial Supervisory Authority, collect fees from those who use high-speed trading systems excessively, and force stock markets to install circuit breakers that can interrupt trading if a problem is detected.

 

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How high-frequency journalism is like high-frequency trading [poynter]

by Paul Wilson

In two very different industries — the news media and the financial sector — once unfathomable technology is feeding intense competition in ways that have prompted serious soul searching and some very troubling mistakes.

For the world’s financial markets, questions are being asked about high-frequency trading (HFT) where complex algorithms analyze markets and execute orders at incredibly quick speeds, often competing over milliseconds.

For journalism, there is no comparable term — though “high-frequency journalism” might be appropriate. Tools like Twitter have removed built-in, age-old safeguards (notably, time) that many reporters used to double-check information, amping up competition and lowering the bar for verification before publication.

This summer could be a case study in what can now go wrong in both industries.

• For HFT, there was Knight Capital’s astounding loss of $440 million in 45 minutes, which itself followed 2010’s notorious ‘flash crash’, among other incidents.
• For journalism, there was incorrect reporting following the Supreme Court’s ruling on President Obama’s landmark healthcare legislation, which, of course followed inaccurate reports of celebrity death.

And, of course, both industries struggle with a sort of “echo effect” — where a high degree of interconnectedness makes mistakes propagate faster than ever before.

With so much at stake, are the two industries, essentially, engaging in competition for competition’s sake, and dismissing the public good that is supposed to be part of the bargain?

Incentivizing speed

Wired notes that “Wall Street used to bet on companies that build things. Now it just bets on technologies that make faster and faster trades.”

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Dark Pools and High-Frequency Drone Wars w/Scott Patterson

Guest Post: An Austrian View On High Frequency Trading [ZeroHedge]

Submitted by Martin Sibileau from A View From The Trenches

An Austrian View On High Frequency Trading

In our last letter, we made some comments on high-frequency trading. Today, we want to briefly analyse, from a macroeconomic perspective, the underlying ideas thrown in its favour, as well as the impact this activity has on the capital markets. Why is this important? Because more than half of the trading volume in equities in the main world exchanges is driven high-frequency trades today (More than 70% of volume in the US exchanges alone).

What is high-frequency trading? We will never exhaustively address this issue here. We recommend that you do your own research on the subject. There are numerous articles on this topic. High-frequency trading (HFT) consists in using sophisticated technology to trade securities. It is highly quantitative, employing algorithms to analyze incoming market data. HF investment positions are held only very briefly, with HF traders trading in and out of positions intraday tens of thousands of times. The important feature is that at the end of a trading day there is no net investment position. Processing speed and access to the exchanges are critical.

HFT strategies can be broadly thought in terms of three main groups: Those that provide liquidity, those that trade headlines and those that trade statistics. The statistical ones are the easiest to understand (at least for us): They are based on technical analysis, correlations. The headline strategies seek to profit from momentum trading, filtering information that describes intra-day action in the exchanges. The so-called liquidity strategies are either based on market making (to profit from bid/ask spreads) or from rebate trading. Operationally, HF traders collectively send millions of orders, the most part of which (we understand above 90%) are cancelled before they are even hit. This often causes delays in the exchanges that receive them, potentially creating arbitrage opportunities in those stocks that trade in multiple exchanges.

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This Is What Happens When An HFT Algo Goes Totally Berserk And Serves Knight Capital With The Bill [Zerohedge]

We all know something went horribly wrong in various NYSE-traded stocks today between 9:30 am and 10:15 am. So wrong in fact that the NYSE had to step in and cancel numerous trades in 6 symbols. However it did not DK millions of other trades in 134 other symbols, the vast majority of which we assume traded errantly due to the market making of Knight Capital (as admitted by the company), which today saw its biggest drop ever since going public on volume about 60 times greater than its average. We also all know that one should buy low and sell high. At least that is what human traders are taught, and that is what they attempt. Because if one consistently does the opposite, one will simply run out of money. Well, the opposite is precisely what the berserk algo in Knight’s Market Making group may have done if Nanex, which has done a forensic analysis of one of the trades in question, is correct. In other words, instead of at least attempting to provide liquidity via limit trades, Knight’s algorithm acted as a market order… gone horribly wrong. As the third chart below shows what the algo did with furious repetition and steadfast consistency was to buy at the offer, and sell at the bid, in other words buy high and sell low. Over and over and over and over. As Nanex laconically notes, “In the case of EXC, that means losing about 15 cents on every pair of trades. Do that 40 times a second, 2400 times a minute, and you now have a system that’s very efficient at burning money.” Which also means that by not DK’ing several hundred million prints, the NYSE may have just thrown Knight under the bus, because the market maker is suddenly on the hook for tens if not hundreds of millions in inverse market making profits.

Here we will assume that readers are sufficiently familiar with market structure to know that market makers only participate in the market in order to collect liquidity rebates, i.e., to be the limit on the bid which is hit, or the offer which is lifted. What Knight did was effectively the opposite, and it also collected the opposite of a rebate: i.e., it paid someone else for no reason at all… well technically to withdraw liquidity. However liquidity that led to creation of losses not profits.

Naturally, when the entire logic of trading was perverted courtesy of Knight’s busted algo, everything went Bizarro Day, and stocks went higher, because they went higher, and the higher they went, the greater the incentive for the algo to keep pushing the stock higher. This explains not only the volume surge, but also the shocking price moves in some stocks such as China Cord Blood which exploded several hundred percent higher before someone had to finally step in. And what is most notable is that because there were neither fat finger trades, nor busted algos that took out the entire bid or offer stack in one trade, thus triggering circuit breakers, but a slow methodical bleed, there was no reason under the current SEC order cancellation methodology to bail out Knight and its berserk algorithm.

Simply said: today may be the single worst day in Knight’s market making history. And sadly, as the NYSE already noted minutes before the market close, the decision to not cancel any more trades is “not subject to appeal.”

From Nanex:

What really happened, or how to lose a ton of money, fast.

What follows should strike you as crazy. If it doesn’t, read it again, because it is.

The following 3 charts plot non-ISO trades (regular trade condition) reported from NYSE in the stock of Exelon Corporation (symbol EXC). By plotting and connecting only regular trades from NYSE we get a clearer picture of the nature (some might say horror) of this event.

1. EXC One second interval chart. Circles are trades, the blue coloring is the NYSE bid and ask which is mostly covered by gray lines that connect the trades.

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Deutsche Bank Algo to Help Buy Side Navigate the Close [advancedtrading]

With a flood of stock orders hitting the tape in the last few minutes of trading each day,Deutsche Bank’s Autobahn Equity business has rolled out a new algorithm in US markets to help institutional clients and other market participants that seek to match closing prices.

The new algorithm TargetClose has also been designed to keep up with heavy market data volumes that occur in the last few minutes of the day as well as help the buy side manage their workflow challenges.

“When you look at market volume profiles, a lot of volume is concentrated at the end of the day or even in just the last few seconds,” said Jose Marques, Global head of Electronic Equity Trading at Deutsche Bank in an interview with Advanced Trading. “It’s become very hard for a human trader to participate in the last few minutes because the quote rates and amount of market data is enormous. If you are manually managing several market orders at once at the close, workflow issues are significant,” according to Marques.

The bank said that TargetClose is designed to project each day’s closing volume based on real-time and historical market data. It continuously adapts to market conditions in order to minimize the trade’s deviation from the closing price on the date of the trade. The algo is underpinned by Deutsche Bank’s technology, including integration with its ultra-fast smart order router, and takes advantage of low-latency models, and participates in dark pools to improve performance, the bank added.

Buy-side traders know that significant volume is done at the end of the day, so a lot of these traders may execute via an algorithm all day long holding back 10-to 20 percent for manual execution near the close as a way to add human value, said Marques. “Having tools for them that are optimized to deal with the problems in the last few minutes of the day are in demand,” he said.

There is demand for end-of-day prices because a lot of institutional traders are explicitly benchmarked to the closing prices and they execute their baskets to match the closing prices. There are also players who need the closing prices or who do index rebalancing and issuers of ETFs who are hedging trades that need the closing prints.

While other Wall Street firms offer this type of functionality, Marques said the unique part of this particular algorithm is the implementation and how it handles the technology issues created by high volume and high data rates just a few minutes before the market close.

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Can Social Media Sharpen Quant Models? [AdvancedTrader]

Social media may be here to stay, but the chatter surrounding the closure of the Twitter-based hedge fund operated by Derwent Capital Markets shows the platform is in its infancy as a buy-side trading tool.

Under normal circumstances, a hedge fund with a paltry $40 million in assets under management wouldn’t get a second look from the media or the investment community. But the world’s eyes were fixed on Derwent Capital’s Absolute Return fund from Day One, since the hedge fund’s strategy was built solely on an algorithm that used Twitter to predict the direction of the stock market.

The algorithm, crafted by computer scientists at Indiana University and the University of Manchester, was designed to scan and analyze large Twitter feeds on a daily basis in order to capture the pulse of the public’s mood. It would then mine that data to detect where the stock market was headed three or four days in advance — to the tune of an 88 percent accuracy rate, according to Indiana University professor Johan Bollen, who helped build the platform.

[How Useful is Social Media-Based Sentiment Analysis to the Buy Side?.]

During its lone month of trading in July 2011, the algorithm proved to be a success, earning a sterling return of 1.86 percent, besting the average hedge fund and the broader market for that period. Nevertheless, Derwent chose to shutter the hedge fund and reportedly decided — at the urging of one of its largest backers — to sell the platform as a tool for private investors.

Not Ready for Prime Time

Yet whenever Derwent’s Twitter-based algorithm hits the marketplace, it’s unlikely that buy-side trading firms will be lining up to add it to their arsenals. Experts say that while such platforms hold considerable promise as trading tools for hedge funds and traditional asset managers, it’ll be years before they catch on with the typical buy-side trading desk.

“Most traders would scorn this,” says John Bates, the founder and chief technology officer of Progress Software. “I remember when this came out — I got an email from one of our customers saying he’d rather put his private parts in a guillotine than trade on Twitter.”

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Germany moves to regulate high-frequency trading [Reuters]

Members of Germany’s parliament and representatives of the finance ministry have agreed on the key points for regulating high-frequency trading on German stock exchanges, participants in the discussions told Reuters on Thursday.

The regulation is likely to stipulate that high-frequency traders must have prior authorisation but is unlikely to make a minimum holding period for orders mandatory, the participants said.

“We expect to have a draft law in the second half of the year,” a participant in the discussions said.

High-frequency traders plug algorithms into computers to generate numerous, lightning-speed automatic trades that are designed to make money from arbitrage on razor-thin price differences and movements.

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Wash Trading By High-Frequency Firms Said To Face U.S. Scrutiny [Bloomberg]

High-frequency trading firms are drawing scrutiny from U.S. regulators seeking evidence that they may be distorting market prices by conducting transactions with themselves, said two people with knowledge of the matter.

So-called wash trades, in which a party buys a contract from itself, could be executed inadvertently by firms with multiple algorithms active in the same stock or derivative, said the people, who requested anonymity because the review isn’t public. Such trades, which can alter the price of shares if they are executed above or below market rates, would be illegal if deemed intentional efforts to manipulate stocks.

The Securities and Exchange Commission and Commodity Futures Trading Commission have sharpened their focus on high- frequency and algorithmic trading since May 6, 2010, when about $862 billion was erased from stock values in 20 minutes before share prices recovered from the plunge. Regulators have expressed concern that some firms and electronic exchanges don’t have sufficient controls to prevent a range of events — from improper trades to programming glitches — that could roil markets even when there is no wrongdoing.

High-frequency trading, in which computer algorithms are used to buy and sell stocks in fractions of a second, accounts for more than half of equity trading volume. Getco LLC and Citadel LLC, both based in Chicago, and New York-based Virtu Financial LLC are among the biggest automated-trading firms.

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Are Trading Algos Proprietary?

Electronic trading had a blank slate when it was first envisioned. Many just figured that they would take their pit trading skills and transfer them to the screen. However, innovation creates more innovation. Engineers automated a lot of those old pit skills and created algorithms to automatically trade. In the pit, we always knew pit location was a big deal. Little did we know that co-location was going to be an even bigger deal in the electronic age.

As electronic trading has developed, there have been lawsuits filed when engineers have switched firms. They are being sued over the trading codes they developed. Firms are suing former employees. Part of the reason they do this is to try and keep them in line. Once you figure out a good code that works, it’s pretty easy to go off on your own, raise some capital and let it rip.

But is trading code a patentable proprietary thing?

When we were in the trading pits, nothing was patentable. If I figured out a better way to leg a spread, or put a trade on, you could follow me. You were free to copy me and try to compete. TheCrush spread, the Crack spread, and any other strategy was easily copied once you figured out how to execute it.

If I backed you as a trader, taught you how to execute the strategy, and then you left and began executing it on your own I had little recourse. I might be able to beat up your reputation a little, but I couldn’t stop you from trading with a lawsuit.

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High speed traders look to restructure

By Philip Stafford in London and Arash Massoudi in New York

Old High Frequency Tick Data R Package exists with spreads, trade direction, statistics, volatility for forex and equity

Old High Frequency Tick Data R Package exists with spreads, trade direction, statistics, volatility for forex and equity
This high frequency R package looks fantastic. It includes a lot of analysis on high frequency data where the number of observations could easily be 100K or way more. It contains so many juicy benefits including:
1.    A very decent PDF sample is included. This can be quite rare as there are some real world examples including a few equity analysis and even foreign exchange trading pair.
2.    A good section is described on duration which is part of high frequency data.
3.    Many traders will always find spread results but again to see an R function do this is rare. On the provided equity example like Microsoft, this shows the spread between the bid and ask quotes. It is quite convenient. There is also an example provided with a foreign exchange pair as well between  bid and ask quotes in multiples of ticks with a specified tick size. I never saw anything like this in Matlab.
4.    There is a handy function which triggers a trading direction. Here you can analyze the dataset to know when to buy or sell based on this function.   You can also specify the time lag as well.
5.    There is a set of handy functions for standard statistic measurements like mean, standard deviation, etc. As well, you get associated plots like histograms as well as functions for calendar patterns.
6.    There is a realized volatility function which is based on Anderson versus other volatility models like GARCH models, stochastic volatility models, or the volatility implied by options or other
Derivative prices.

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