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Monday, February 27, 2012

High Frequency Trading Demonstration Is Major Success: ProtectVEST and AdVanceVEST by EchoVectorVEST MDPP

On Friday, 24 February, 2012, ProtectVEST and AdvanceVEST by EchoVectorVEST MDPP Performed a Real-Time, Time-Stamped, Advanced Risk Management Methodology HIGH FREQUENCY TRADING (HFT) Simulation Day in both the DIA and GLD ETFs for public demonstration, review, and education.  

Results were outstanding.  Over 110 motions were publically logged and recorded in real-time during the ProtectVEST and AdvanceVEST by EchoVectorVEST MDPP provided HFT Simulation Sample Day.  

To see and review the results of each of these over 110 generated time-stamped real-time motions on 2/24/2012 you can go to www.echovectorvest.blogspot.com.  

Provided below is ProtectVEST and AdvanceVEST by EchoVectorVEST MDPP ADVANCED RISK MANAGEMENT HFT SHORTHAND CODE utilized in the simulation.

See:  www.echovectorvest.blogspot.com for Friday 24 February 2012  

"We're keeping watch for you"


ProtectVEST and AdvanceVEST by EchoVectorVEST MDPP Real-Time Time-Stamped Confirmed Example of High Frequency Trading (HFT) in the DIA ETF and the GLD ETF utilizing MDPPADVANCED RISK MANAGEMENT TECHNOLOGY and Performed for Public Education and Review: Friday 24 February 2012, With ProtectVEST and AdvanceVEST MDPP HFT SHORTHAND CODE Utilized

PROTECTVEST AND ADVANCEVEST BY ECHOVECTORVEST MDPP
ADVANCED RISK MANAGEMENT
HFT SHORTHAND CODE REFERENCES 

HFT = High Frequency Trading
g = gld etf
d = dia etf
.xx = Price quoted to cents on active $ price level.
r = reset (new adjustment and setting of OOTVs)
t = TAUA, trailing automatic upward adjustment of ootv(s)
b = for both FNPI and FNPDS. Also assume both without mention of "b" unless "-b" specified. instead.
f = fulfilled . No designated "f" does not preclude fulfilment occurred.
fr = from prior active high reset (adjustment) of active OOTV(s).
c = cover
cov = cover
na = now active
s = OOTV(s) reset (adjusted) to same value as cover price level
ts = TAUA set at same level as cover price quoted
t1 = TAUA set 1 cent above cover price quoted
t02 = TAUA set 2 cents above cover price quoted, etc.
t03 = TAUA set 3 cents above cover price quoted, etc.
t05 = TAUA set 5 cents above cover price quoted, etc.
t10 = TAUA set 10 cents above cover price quoted, etc.
a = active high reset price levels for active fnpi and fnpds OOTV(s) settings
apr = prior active high OOTV adjustment price level from TAUA or specified reset.
g = capital gain lock base differential (multiply by 3 for total (gt) when b = FNPI and FNPDS).
gt = captial gain lock total differential, total differential from total short-term move.
ae = time am est
pe = time pm est
tp = current trading price (bid, last print)

Example:

g c .70 rs t05 b f fr .80 g .10 gt .30
Posted by at 7:00 AM 





For Real-time Intraday 
MDPP Forecast Model 
Generated
OTAPS and HFT 
Signals and Output

Click on  http://echovectorvest.blogspot.com/

HiHigh-frequency trading

From Wikipedia, the free encyclopedia
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The examples and perspective in this article may not represent a worldwide view of the subject. Please improve this article and discuss the issue on the talk page. (April 2012)
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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][2][3]
  • It is highly quantitative, employing computerized algorithms to analyze incoming market data and implement proprietary trading strategies;
  • An investment position is held only for very brief periods of time - from seconds to hours - and rapidly trades into and out of those positions, sometimes thousands or tens of thousands of times a day;[4]
  • At the end of a trading day there is no net investment position;
  • It is mostly employed by proprietary firms or on proprietary trading desks in larger, diversified firms;
  • It is very sensitive to the processing speed of markets and of their own access to the market;
  • Many high-frequency traders provide liquidity and price discovery to the markets through market-making and arbitrage trading; high-frequency traders also take liquidity to manage risk or lock in profits.
Positions are taken in equities, options, futures, ETFs, currencies, and other financial instruments that can be traded electronically.[5]
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 for very small, consistent profits.[6][7] 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.[8]
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.[2] High-frequency trading firms do not employ significant leverage, do not accumulate positions, and typically liquidate their entire portfolios on a daily basis.[7]
By 2010 high-frequency trading accounted for over 70% of equity trades in the US and was rapidly growing in popularity in Europe and Asia.
Algorithmic and high-frequency trading were both found to have contributed to volatility in the May 6, 2010 Flash Crash, when high-frequency liquidity providers were in fact found to have withdrawn from the market.[9][10][11][12][13][14][15][16] A July, 2011 report by theInternational 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."[1][17]

CONTENTS

   [hide] 

[EDIT]HISTORY

High-frequency trading has taken place at least since 1999, after the U.S. Securities and Exchange Commission (SEC) authorized electronic exchanges in 1998. At the turn of the 21st century, HFT trades had an execution time of several seconds, whereas by 2010 this had decreased to milli- and even microseconds.[18] Until recently, high-frequency trading was a little-known topic outside the financial sector, with an article published by the New York Times in July 2009 being one of the first to bring the subject to the public's attention.[19]

[edit]Market growth

In the early 2000s, high-frequency trading still accounted for less than 10% of equity orders, but this proportion was soon to begin rapid growth. According to data from the NYSE, trading volume grew by about 164% between 2005 and 2009 for which high-frequency trading might be accounted.[19] As of the first quarter in 2009, total assets under management for hedge funds with high-frequency trading strategies were $141 billion, down about 21% from their peak before the worst of the crises.[20] The high-frequency strategy was first made successful by Renaissance Technologies.[21] Many high-frequency firms are market makers and provide liquidity to the market which has lowered volatility and helped narrow Bid-offer spreads, making trading and investing cheaper for other market participants.[20] In the United States, high-frequency trading firms represent 2% of the approximately 20,000 firms operating today, but account for 73% of all equity orders volume.[22] The largest high-frequency trading firms in the US include names like Getco LLC,Knight Capital Group, Jump Trading, and Citadel LLC. The Bank of England estimates similar percentages for the 2010 US market share, also suggesting that in Europe HFT accounts for about 40% of equity orders volume and for Asia about 5-10%, with potential for rapid growth.[18] By value, HFT was estimated in 2010 by consultancy Tabb Group to make up 56% of equity trades in the US and 38% in Europe.[23]

[EDIT]HIHIGH-FREQUENCY TRADING STRATEGIES

High-frequency trading is quantitative trading that is characterized by short portfolio holding periods (see Wilmott (2008)). All portfolio-allocation decisions are made by computerized quantitative models. The success of high-frequency trading strategies is largely driven by their ability to simultaneously process volumes of information, something ordinary human traders cannot do. Specific algorithms are closely guarded by their owners and are known as "algos".
Most high-frequency trading strategies fall within one of the following trading strategies:[24]
  • Market making
  • Ticker tape trading
  • Event arbitrage
  • High-frequency statistical arbitrage
 External videos
Example of a High Frequency portfolio

[edit]Market making

Main article: Market making
Market making is a set of high-frequency trading strategies that involve placing a limit order to sell (or offer) or a buy limit order (or bid) in order to earn the bid-ask spread. By doing so, market makers provide counterpart to incoming market orders. Although the role of market maker was traditionally fulfilled by specialist firms, this class of strategy is now implemented by a large range of investors, thanks to wide adoption of direct market access. As pointed out by empirical studies[25] this renewed competition among liquidity providers causes reduced effective market spreads, and therefore reduced indirect costs for final investors.
Some high-frequency trading firms use market making as their primary trading strategy.[7] Automated Trading Desk, which was bought by Citigroup in July 2007, has been an active market maker, accounting for about 6% of total volume on both the NASDAQ and the New York Stock Exchange.[26] Building up market making strategies typically involves precise modeling of the target market microstructure[27][28] together with stochastic control techniques.[29][30][31]

These strategies appear intimately related to the entry of new electronic venues. Academic study of Chi-X's entry into the European equity market reveals that its launch coincided with a large HFT that made markets using both the incumbent market, NYSE-Euronext, and the new market, Chi-X. The study shows that the new market provided ideal conditions for HFT market-making, low fees (i.e., rebates for quotes that led to execution) and a fast system, yet the HFT was equally active in the incumbent market to offload nonzero positions. New market entry and HFT arrival are further shown to coincide with a significant improvement in liquidity supply.[32]

[edit]Ticker tape trading

Much information happens to be unwittingly embedded in market data, such as quotes and volumes. By observing a flow of quotes, high-frequency trading machines are capable of extracting information that has not yet crossed the news screens. Since all quote and volume information is public, such strategies are fully compliant with all the applicable laws.
Filter trading is one of the more primitive high-frequency trading strategies that involves monitoring large amounts of stocks for significant or unusual price changes or volume activity. This includes trading on announcements, news, or other event criteria. Software would then generate a buy or sell order depending on the nature of the event being looked for.[33]

[edit]Event arbitrage

Certain recurring events generate predictable short-term responses in a selected set of securities. High-frequency traders take advantage of such predictability to generate short-term profits.

[edit]Statistical arbitrage

Another set of high-frequency trading strategies are strategies that exploit predictable temporary deviations from stable statistical relationships among securities. Statistical arbitrage at high frequencies is actively used in all liquid securities, including equities, bonds, futures, foreign exchange, etc. Such strategies may also involve classical arbitrage strategies, such as covered interest rate parity in the foreign exchange market, which gives a relationship 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. High-frequency trading allows similar arbitrages using models of greater complexity involving many more than four securities. The TABB Group estimates that annual aggregate profits of high-frequency arbitrage strategies currently exceed US$21 billion.[34]

[EDIT]LLLOW-LATENCY STRATEGIES

A separate, "naïve" class of high-frequency trading strategies relies exclusively on ultra-low latency direct market access technology. In these strategies, computer scientists rely on speed to gain minuscule advantages in arbitraging price discrepancies in some particular security trading simultaneously on disparate markets.

[EDIT]EIEFFECTS

The effects of algorithmic and high-frequency trading in volatile markets are the subject of ongoing research since regulators claim these practices contributed to volatility in the May 6, 2010 Flash Crash, as discussed later in this section.[9][10][11][12][13][16]
"The fast-growing practice of high-frequency trading, in which traders place vast flurries of securities trades, is speeding up execution times for all investors, making it cheaper to buy or sell and posing no risk to small investors." - Chicago Board Options Exchange[35]
Members of the financial industry claim high-frequency trading substantially improves market liquidity,[7] narrows Bid-offer spread, lowers volatility and makes trading and investing cheaper for other market participants.[7][20][36]
An academic study[37] found that, for large-cap stocks and in quiescent markets during periods of "generally rising stock prices", high-frequency trading lowers the cost of trading and increases the informativeness of quotes;[38] however, it found "no significant effects for smaller-cap stocks"[39], and "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."[40]
In September 2011, Nanex, LLC (a high-frequency trading software company) published a report stating the contrary. They looked at the amount of quote traffic compared to the value of trade transactions over 4 and half years and saw a 10-fold decrease in efficiency.[41] Many discussions about HFT focus solely on the frequency aspect of the algorithms and not on their decision-making logic (which is typically kept secret by the companies that develop them). This makes it difficult for observers to pre-identify market scenarios where HFT will dampen or amplify price fluctuations. The growing quote traffic compared to trade value could indicate that more firms are trying to profit from cross-market arbitrage techniques that do not add significant value through increased liquidity when measured globally.
More fully automated markets such as NASDAQ, Direct Edge, and BATS, in the US, have gained market share from less automated markets such as the NYSE. Economies of scale in electronic trading have contributed to lowering commissions and trade processing fees, and contributed to international mergers and consolidation of financial exchanges.
The speeds of computer connections, measured in milliseconds or microseconds, have become important.[42][43] Competition is developing among exchanges for the fastest processing times for completing trades. For example, in 2009 the London Stock Exchange bought a technology firm called MillenniumIT and announced plans to implement its Millennium Exchange platform[44]which they claim has an average latency of 126 microseconds.[45] Since then, competitive exchanges have continued to reduce latency, and today, with turnaround times of three milliseconds available, are useful to traders to pinpoint the consistent and probable performance ranges of financial instruments. These professionals are often dealing in versions of stock index funds like the E-mini S&Ps because they seek consistency and risk-mitigation along with top performance. They must filter market data to work into their software programming so that there is the lowest latency and highest liquidity at the time for placing stop-losses and/or taking profits. With high volatility in these markets, this becomes a complex and potentially nerve-wracking endeavor, in which a small mistake can lead to a large loss. Absolute frequency data play into the development of the trader's pre-programmed instructions.[46]
Spending on computers and software in the financial industry increased to $26.4 billion in 2005.[47]
The brief but dramatic stock market crash of May 6, 2010 was originally alleged to have been caused by high-frequency trading.[48]However, CME Group, a large futures exchange, stated that, insofar as stock index futures traded on CME Group were concerned, its investigation had found no support for the notion that high-frequency trading was related to the crash, and actually stated it had a market stabilizing effect.[49] This conclusion is contradicted in a report on the Flash Crash by the U.S. Securities and Exchange Commission and the Commodity Futures Trading Commission, wherein regulators stated that the actions of high-frequency trading firms on May 6, 2010 contributed to volatility during the crash.[9][10][11][12][13][14][15][16] Despite the original perception that high-frequency traders typically cause no market price impact,[7] and have a stabilizing effect in times of volatility,[7][36][49] and some suggestions that they may actually have been a major factor in minimizing and partially reversing the Flash Crash,[50] later reports determined that high-frequency trading had significant price impact and a destabilizing role during the Flash Crash, helping to drive prices down.[9][10][11][12]
After almost five months of investigations, the U.S. Securities and Exchange Commission and the Commodity Futures Trading Commission issued a joint report identifying the cause that set off the sequence of events leading to the Flash Crash.[51] The report found that the cause was a single sale of $4.1 billion in futures contracts by a mutual fund, identified as Waddell & Reed Financial, in an aggressive attempt to hedge its investment position.[52][53] The joint report also found that "high-frequency traders quickly magnified the impact of the mutual fund's selling."[9] The joint report "portrayed a market so fragmented and fragile that a single large trade could send stocks into a sudden spiral," that a large mutual fund firm "chose to sell a big number of futures contracts using a computer program that essentially ended up wiping out available buyers in the market," that as a result high-frequency firms "were also aggressively selling the E-mini contracts," contributing to rapid price declines.[9] The joint report also noted "'HFTs began to quickly buy and then resell contracts to each other — generating a 'hot-potato' volume effect as the same positions were passed rapidly back and forth.'"[9] The combined sales by Waddell and high-frequency firms quickly drove "the E-mini price down 3% in just four minutes."[9] As prices in the futures market fell, there was a spillover into the equities markets where "the liquidity in the market evaporated because the automated systems used by most firms to keep pace with the market paused" and scaled back their trading or withdrew from the markets altogether.[9] The joint report then noted that "Automatic computerized traders on the stock market shut down as they detected the sharp rise in buying and selling."[11] As computerized high-frequency traders exited the stock market, the resulting lack of liquidity "...caused shares of some prominent companies like Procter & Gamble and Accenture to trade down as low as a penny or as high as $100,000."[11] While some firms exited the market, high-frequency firms that remained in the market exacerbated price declines because they "'escalated their aggressive selling' during the downdraft."[citation needed]

[EDIT]CCONTROVERSY

This article may contain too much repetition or redundant language. Please helpimprove it by merging similar text or removing repeated statements. (February 2012)
High-frequency trading has been the subject of intense public focus since regulators claimed these practices contributed to volatility on May 6, 2010, popularly known as the 2010 Flash Crash,[9][10][11][12][13][14][15][16] a United States stock market crash on May 6, 2010 in which the Dow Jones Industrial Average plunged to its largest intraday point loss, but not percentage loss,[54] in history, only to recover much of those losses within minutes.[55] Another area of controversy, related to SEC and CFTC findings in their joint report on the Flash Crash that equity market "market makers and other liquidity providers widened their quote spreads, others reduced offered liquidity, and a significant number withdrew completely from the markets"[51] during the Flash Crash, is whether high-frequency market makers should be subject to regulations that would require them to stay active in volatile markets.[56] As SEC Chairman Mary Schapiro said in a speech on September 22, 2010, "...high frequency trading firms have a tremendous capacity to affect the stability and integrity of the equity markets. Currently, however, high frequency trading firms are subject to very little in the way of obligations either to protect that stability by promoting reasonable price continuity in tough times, or to refrain from exacerbating price volatility."[57]
Despite studies reporting positive findings about high-frequency trading, including that high-frequency trading reduces volatility and does not pose a systemic risk,[7][36][35][49] and both lowers transaction costs for retail investors,[37][36][35] and at the same time does so without impacting long term investors,[2][7][35] high-frequency trading is the subject of increased debate.[58] This debate has been fueled by U.S. Securities and Exchange Commission and Commodity Futures Trading Commission empirical findings that high-frequency trading contributed to volatility in the May 6, 2010 Flash Crash.[9][10][11][12][13][14][15][16] Politicians, regulators, journalists and market participants have all raised concerns on both sides of the Atlantic.[23][58][59] In September 2010, SEC chairperson Mary Schapiro signaled that US authorities were considering the introduction of regulations targeted at HFT, such as a minimum "time in force" rule, to prevent buy orders being canceled very soon after being issued. Criticisms of this proposed law are that currently exchanges allow excess message traffic to queue up at their servers' ports, where it is processed sequentially at a fixed rate and as a result poses no threat to the exchanges.[7] In addition to this, equity options markets produce far more message volume than equity markets and have consistently handled the data without issue.[7] Some HFT systems cancel many of their orders almost immediately after placing them as they don't intend the trades to carry through; the false orders are used as part of a pinging tactic to discover the upper price other traders are willing to pay.[58] Some high-frequency trading firms state that so many orders get canceled because the orders people get are not the same ones they send. This happens frequently because of an existing regulation regarding re-priced orders.[7]
Another area of concern relates to flash trading. Flash trading is a form of trading in which certain market participants are allowed to see incoming orders to buy or sell securities very slightly earlier than the general market participants, typically 30 milliseconds, in exchange for a fee. According to some sources, the programs can inspect major orders as they come in and use that information to profit.[5] Currently, the majority of exchanges either do not offer flash trading, or have discontinued it, although the exchange Direct Edge currently does offer it to participants. Direct Edge's response to this is that flash trading reduces market impact, increases average size of executed orders, reduces trading latency, and provides additional liquidity.[60] Direct Edge also allows all of its subscribers to determine whether they want their orders to participate in flash trading or not so brokers have the option to opt out of flash orders on behalf of their clients if they choose to.[60] Due to the fact that market participants can choose to utilize it for additional liquidity or not participate in it at all, Direct Edge believes the controversy is overstated, stating:
"Misconceptions respecting flash technology have, to date, stirred a passionate but ill informed debate."[60]
CounterPunch, a bi-weekly political newsletter, contends that this creates a two-tiered market in which a certain class of traders can unfairly exploit others, akin to front running.[61] Exchanges claim that the procedure benefits all traders by creating more marketliquidity and the opportunity for price improvement.
Direct Edge's response to the "two-tiered market" criticism is as follows:
"First it is difficult to address concerns that may result, particularly when there is no empirical data to support such a result. Furthermore, we do not view technology that instantaneously aggregates passive and aggressive liquidity as creating a two-tier market. Rather, flash technology democratizes access to the non-displayed market and in this regard, removes different "tiers" in market access. Additionally, any subscriber of Direct Edge can be a recipient of flashed orders."[60]

[EDIT]AADVANCED TRADING PLATFORMS

Advanced computerized trading platforms and market gateways are becoming standard tools of most types of traders, including high-frequency traders. Broker-dealers now compete on routing order flow directly, in the fastest and most efficient manner, to the line handler where it undergoes a strict set of Risk Filters before hitting the execution venue(s). Ultra Low Latency Direct Market Access(ULLDMA) is a hot topic amongst Brokers and Technology vendors such as Goldman Sachs, Credit Suisse, and UBS. Typically, ULLDMA systems can currently handle high amounts of volume and boast round-trip order execution speeds (from hitting "transmit order" to receiving an acknowledgment) of 10 milliseconds or less.
Such performance is achieved with the use of hardware acceleration or even full-hardware processing of incoming Market data, in association with high-speed communication protocols, such as 10 Gigabit Ethernet or PCI Express. More specifically, some companies provide full-hardware appliances based on FPGA to obtain sub-microsecond end-to-end Market data processing.

[EDIT]NQuOTES AND REFERENCES

  1. ^ a b "Regulatory Issues Raised by the Impact of Technological Changes on Market Integrity and Efficiency", IOSCO Technical Committee, July 2011, retrieved 2011-07-12
  2. ^ a b c Irene Aldridge (July 8, 2010). "What is High Frequency Trading, After All?". Huffington Post. Retrieved August 15, 2010.
  3. ^ "Advances in High Frequency Strategies", Complutense University Doctoral Thesis (published), December 2011, retrieved 2012-01-08
  4. ^ http://www.youtube.com/watch?v=FGHbddeUBuQ What is High Frequency Trading (video)
  5. ^ a b Ellen Brown (April 23, 2010). "Computerized Front-Running". CounterPunch. Retrieved May 8, 2010.
  6. ^ "The Microstructure of the Flash Crash: Flow Toxicity, Liquidity Crashes and the Probability of Informed Trading",Journal of Portfolio Management, October 2010, retrieved 2012-01-08
  7. ^ a b c d e f g h i j k l "Trade Worx / SEC letters". April 21, 2010. Retrieved September 10, 2010.
  8. ^ Aldridge, Irene (July 26, 2010). "How profitable is high frequency trading". Huffington Post.
  9. ^ a b c d e f g h i j k Lauricella, Tom (October 2, 2010). "How a Trading Algorithm Went Awry". The Wall Street Journal.
  10. ^ a b c d e f Mehta, Nina (1 Oct 2010). "Automatic Futures Trade Drove May Stock Crash, Report Says". Bloomberg.
  11. ^ a b c d e f g h Bowley, Graham (1 Oct 2010). "Lone $4.1 Billion Sale Led to ‘Flash Crash’ in May". The New York Times.
  12. ^ a b c d e f Spicer, Jonathan (1 Oct 2010). "Single U.S. trade helped spark May's flash crash". Reuters.
  13. ^ a b c d e Goldfarb, Zachary (1 Oct 2010). "Report examines May's 'flash crash,' expresses concern over high-speed trading". Washington Post.
  14. ^ a b c d Popper, Nathaniel (1 Oct 2010). "$4.1-billion trade set off Wall Street 'flash crash,' report finds". Los Angeles Times.
  15. ^ a b c d Younglai, Rachelle (5 Oct 2010). "U.S. probes computer algorithms after "flash crash"". Reuters.
  16. ^ a b c d e Spicer, Jonathan (15 Oct 2010). "Special report: Globally, the flash crash is no flash in the pan". Reuters.
  17. ^ Huw Jones (July 7, 2011). "Ultra fast trading needs curbs -global regulators". Reuters. Retrieved July 12, 2011.
  18. ^ a b "Patience and Finance", Bank of England, Sept. 02 2010, retrieved Sept. 10, 2010
  19. ^ a b CHARLES DUHIGG (July 23, 2009). "Stock Traders Find Speed Pays, in Milliseconds". New York Times. Retrieved Sept. 10, 2010.
  20. ^ a b c Geoffrey Rogow,Eric Ross Rise of the (Market) Machines, The Wall Street Journal, June 19, 2009
  21. ^ High frequency finance and the hedge fund category of the future
  22. ^ Aite Group Survey
  23. ^ a b Jeremy Grant (Sept. 02, 2010). "High-frequency trading: Up against a bandsaw". Financial Times. Retrieved Sept. 10, 2010.
  24. ^ Aldridge, Irene (2009), High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems (Wiley),ISBN 978-0-470-56376-2
  25. ^ Hendershott T., Jones C.M. and A.J. Menkveld (2010): \Does algorithmic trading improve liquidity?", Journal of Finance
  26. ^ "Citigroup to expand electronic trading capabilities by buying Automated Trading Desk", The Associated Press(International Herald Tribune), July 2, 2007, retrieved July 4, 2007
  27. ^ Cartea, Á. and S. Jaimungal (2012) : Modeling Asset Prices for Algorithmic and High Frequency Trading. Available at SSRN: http://ssrn.com/abstract=1722202
  28. ^ Guilbaud, Fabien and Pham, Huyên, Optimal High Frequency Trading with Limit and Market Orders (2011). Available at SSRN: http://ssrn.com/abstract=1871969
  29. ^ Avellaneda M. and S. Stoikov (2008): High frequency trading in a limit order book", Quantitative Finance, 8(3), 217-224
  30. ^ Cartea, Á., S. Jaimungal and J. Ricci (2011) : Buy Low Sell High : A High Frequency Trading Perspective. Available at SSRN: http://ssrn.com/abstract=1964781
  31. ^ Cartea, Á. and S. Jaimungal (2012) : Risk Metrics and Fine Tuning of High Frequency Trading Strategies. Available at SSRN: http://ssrn.com/abstract=2010417
  32. ^ The studies are available athttp://papers.ssrn.com/sol3/papers.cfm?abstract_id=1624329 andhttp://papers.ssrn.com/sol3/papers.cfm?abstract_id=1722924
  33. ^ "The World Of High Frequency Trading: 6 Primary Strategies", www.T3Live.com, retrieved September 15, 2010
  34. ^ Rob Iati, The Real Story of Trading Software Espionage,AdvancedTrading.com, July 10, 2009
  35. ^ a b c d High-Frequency Trading Good For Small Investors: CBOE, September 5, 2010
  36. ^ a b c d How High Frequency Trading Benefits All Investors
  37. ^ a b "Terrence Hendershott, Charles M. Jones, and Albert J. Menkveld. Does Algorithmic Trading Improve Liquidity?",Journal of Finance, 2010
  38. ^ Terrence Hendershott, Charles M. Jones, and Albert J. Menkveld 2010, p. 33.
  39. ^ Terrence Hendershott, Charles M. Jones, and Albert J. Menkveld 2010, p. 6.
  40. ^ Terrence Hendershott, Charles M. Jones, and Albert J. Menkveld 2010, p. 35.
  41. ^ http://www.nanex.net/Research/ExhibitA/ExhibitA.html
  42. ^ Dodgy tickers, The Economist, March 8, 2007
  43. ^ Pleasures and Pains of Cutting-Edge Technology Mar 19, 2007
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