Automated trading system

The automated trading system determines whether an order should be submitted based on, for example, the current market price of an option and theoretical buy and sell prices.{{Cite journal |last=Hanif |first=Ayub |last2=Smith |first2=Robert Elliott |date=2012-09-30 |title=Algorithmic, Electronic, and Automated Trading |url=https://www.pm-research.com/content/iijtrade/7/4/78 |journal=The Journal of Trading |language=en |volume=7 |issue=4 |pages=78–86 |doi=10.3905/jot.2012.7.4.078 |issn=1559-3967|url-access=subscription }} The theoretical buy and sell prices are derived from, among other things, the current market price of the security underlying the option. A look-up table stores a range of theoretical buy and sell prices for a given range of current market price of the underlying security. Accordingly, as the price of the underlying security changes, a new theoretical price may be indexed in the look-up table, thereby avoiding calculations that would otherwise slow automated trading decisions.Marynowski, John M., et al. "Automated trading system in an electronic trading exchange." U.S. Patent No. 7,251,629. 31 Jul. 2007. A distributed processing on-line automated trading system uses structured messages to represent each stage in the negotiation between a market maker (quoter) and a potential buyer or seller (requestor).Hartheimer, Richard, et al. "Financial exchange system having automated recovery/rollback of unacknowledged orders." U.S. Patent No. 5,305,200. 19 Apr. 1994.

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• Trend following{{Cite book |last=Zubulake |first=Paul |title=The high frequency game changer: how automated trading strategies have revolutionized the markets |last2=Lee |first2=Sang |date=2011 |publisher=Wiley |isbn=978-1-118-01968-9 |series=Wiley trading series |location=Hoboken, NJ}}

Trend following is a trading strategy that bases buying and selling decisions on observable market trends. For years, various forms of trend following have emerged, like the Turtle Trader software program. Unlike financial forecasting, this strategy does not predict market movements. Instead, it identifies a trend early in the day and then trades automatically according to a predefined strategy, regardless of directional shifts. Trend following gained popularity among speculators, though remains reliant on manual human judgment to configure trading rules and entry/exit conditions. Finding the optimal initial strategy is essential. Trend following is limited by market volatility and the difficulty of accurately identifying trends.{{Cite journal |last=Fong |first=Simon |last2=Si |first2=Yain-Whar |last3=Tai |first3=Jackie |date=2012 |title=Trend following algorithms in automated derivatives market trading |url=https://doi.org/10.1016/j.eswa.2012.03.048 |journal=Expert Systems with Applications |volume=39 |issue=13 |pages=11378–11390 |doi=10.1016/j.eswa.2012.03.048 |issn=0957-4174|url-access=subscription }}

For example, the following formula could be used for trend following strategy:

:"Consider a complete probability space (Ω, F, P). Let $S\_r$ denote the stock price at time $r$ satisfying the equation

:$dS\_r\; =\; S\_r[\backslash mu\; (\backslash alpha\; \_r)dr\; +\; \backslash sigma\; dB\_r],$ $S\_t\; =\; X,$ ,

:where $\backslash alpha\; \_r\; \backslash in\; \backslash \{1,2\backslash \}$ is a two-state Markov-Chain, $\backslash mu\; (i)\; \backslash equiv\; \backslash mu\; \_i$ is the expected return rate in regime $i\; =\; 1,2,\; \backslash sigma\; >\; 0$ is the constant volatility, $B\_r$ is a standard Brownian motion, and $t$ and $T$ are the initial and terminal times, respectively".{{cite news |last1=Dai |first1=Min |last2=Yang |first2=Zhou |last3=Zhang |first3=Qing |last4=Zhu |first4=Qiji |title=Optimal Trend Following Trading Rules}}

• Volume-weighted average price

According to Volume-weighted average price Wikipedia page, VWAP is calculated using the following formula:

":$P\_\{\backslash mathrm\{VWAP\}\}\; =\; \backslash frac\{\backslash sum\_\{j\}\{P\_j\; \backslash cdot\; Q\_j\}\}\{\backslash sum\_j\{Q\_j\}\}\; \backslash ,$

where:

:$P\_\{\backslash mathrm\{VWAP\}\}$ is Volume Weighted Average Price;

:$P\_j$ is price of trade $j$;

:$Q\_j$ is quantity of trade $j$;

:$j$ is each individual trade that takes place over the defined period of time, excluding cross trades and basket cross trades".

• Mean reversion (finance)

"A continuous mean-reverting time series can be represented by an Ornstein-Uhlenbeck stochastic differential equation:

:$dx\_t\; =\; \backslash theta(\backslash mu\; -x\_t)dt\; +\; \backslash sigma\; dW\_t$

Where $\backslash theta$ is the rate of reversion to the mean, $\backslash mu$ is the mean value of the process, $\backslash sigma$ is the variance of the process and $W\_t$ is a Wiener Process or Brownian Motion".{{cite web |title=Basics of Statistical Mean Reversion Testing |url=https://www.quantstart.com/articles/Basics-of-Statistical-Mean-Reversion-Testing |website=QuantStart}}{{Cite news |last=Smith |first=William |date=2010-02-01 |title=On the Simulation and Estimation of the Mean-Reverting Ornstein-Uhlenbeck Process |url=https://commoditymodels.files.wordpress.com/2010/02/estimating-the-parameters-of-a-mean-reverting-ornstein-uhlenbeck-process1.pdf |volume=1.01}}

The concept of automated trading system was first introduced by Richard Donchian in 1949 when he used a set of rules to buy and sell the funds.{{Cite journal |last=Donchian |first=Richard |date=1995-11-15 |title=Donchian's five- and 20-day moving averages |journal=Futures Futures: News, Analysis & Strategies for Futures, Options & Derivatives Traders |location=Cedar Falls, Iowa |publisher=The Alpha Pages LLC |volume=24 |issue=13 |page=32 |via=Gale}} Donchian proposed a novel concept in which trades would be initiated autonomously in response to the fulfillment of predetermined market conditions. Due to the absence of advanced technology at the time, Donchian's staff was obligated to perform manual market charting and assess the suitability of executing rule-based trades. Although this laborious procedure was susceptible to human error, it established the foundation for the subsequent development of transacting financial assets.{{Cite journal |last=Dimov |first=Diyan |date=2022-12-19 |title=Conceptual Model of Automated Trading Systems Implementation |url=https://journal.robonomics.science/index.php/rj/article/view/25 |journal=ROBONOMICS: The Journal of the Automated Economy |language=en |volume=3 |pages=25–25 |issn=2683-099X}}

Then, in the 1980s, the concept of rule based trading (trend following) became more popular when famous traders like John Henry began to use such strategies. In the mid 1990s, some models were available for purchase. Also, improvements in technology increased the accessibility for retail investors.{{cite web |title=History of Trading Systems |date=13 January 2014 |url=http://www.automatedtrading.com/2014/01/13/history-trading-systems/}} Later, Justin-Niall Swart employed a Donchian channel-based trend-following trading method for portfolio optimization in his South African futures market analysis.{{Cite news |last=Swart |first=J.N. |date=2016 |title=Testing a price breakout strategy using Donchian Channels |publisher=University of Cape Town}}

The early form of an Automated Trading System, composed of software based on algorithms, that have historically been used by financial managers and brokers. This type of software was used to automatically manage clients' portfolios.{{Cite book |last=Durenard |first=Eugene A. |url=https://www.worldcat.org/title/847541969 |title=Professional automated trading: theory and practice |date=2013 |publisher=John Wiley & Sons |isbn=978-1-118-12985-2 |series=Wiley trading series |location=Hoboken, New Jersey |oclc=847541969}} However, the first service to free market without any supervision was first launched in 2008 which was Betterment by Jon Stein. Since then, this system has been improving with the development in the IT industry.

Around 2005, copy trading and mirror trading emerged as forms of automated algorithmic trading. These systems allowed traders to share their trading histories and strategies, which other traders could replicate in their accounts. One of the first companies to offer an auto-trading platform was Tradency in 2005 with its "Mirror Trader" software.{{Cite web |last=Lievonen |first=L. |date=2020 |title=Empirical investigation on the performance of copy-portfolios on E-TORO platform |url=https://digikogu.taltech.ee/en/Download/9c402020-d23e-447e-bcba-e41823baa02d/eToroplatvormiltegutsevatesotsiaalsekauplemise.pdf}}{{Cite web |title=Tradency, Robo for Advisors |url=https://www.tradency.com/ |access-date=2022-07-12 |website=tradency}}{{Cite web |title=Mirror Trader |url=https://www.tradency.com/mirror-trader |access-date=2022-07-12 |website=tradency}} This feature enabled traders to submit their strategies, allowing other users to replicate any trades produced by those strategies in their accounts. Subsequently, certain platforms allowed traders to connect their accounts directly in order to replicate trades automatically, without needing to code trading strategies. Since 2010, numerous online brokers have incorporated copy trading into their internet platforms, such as eToro, ZuluTrade, Ayondo, and Tradeo.{{Cite web |last=Mingwen |first=Yang |last2=Eric |first2=Zheng |last3=Vijay |first3=Mookerjee |date=2019 |title=The Transparency-Revenue Conundrum in Social Trading: Implications for Platforms and Investors |url=https://carlsonschool.umn.edu/sites/carlsonschool.umn.edu/files/inline-files/Social_Trading.pdf |publisher=Jindal School of Management, The University of Texas at Dallas}}{{Cite journal |last=Apesteguia |first=Jose |last2=Oechssler |first2=Jörg |last3=Weidenholzer |first3=Simon |date=2020 |title=Copy Trading |url=https://pubsonline.informs.org/doi/10.1287/mnsc.2019.3508 |journal=Management Science |language=en |volume=66 |issue=12 |pages=5608–5622 |doi=10.1287/mnsc.2019.3508 |issn=0025-1909}} Copy trading benefits from real-time trading decisions and order flow from credible investors, which lets less experienced traders mirror trades without performing the analysis themselves.

Now, Automated Trading System is managing huge assets all around the globe.{{Cite web|url=https://www.algonest.com/site/robo-content/|title=Robo-Advisor: Future to Financial Management?|last=Muller|first=Christopher|date=July 14, 2018|website=Algonest|access-date=June 24, 2018|archive-url=https://web.archive.org/web/20190106025105/https://algonest.com/site/robo-content|archive-date=January 6, 2019|url-status=dead}} In 2014, more than 75 percent of the stock shares traded on United States exchanges (including the New York Stock Exchange and NASDAQ) originated from automated trading system orders.{{cite web|url=http://www.abmac.com/industry-insight/as-automated-trading-takes-over-markets-rational-human-investors-matter-even-more|title=As automated trading takes over markets, rational human investors matter even more. - Abernathy MacGregor|publisher=}}{{cite web|url=http://fortune.com/2013/05/29/a-day-in-the-quiet-life-of-a-nyse-floor-trader/|title=A day in the quiet life of a NYSE floor trader|date=29 May 2013|publisher=}}

Automated trading, or high-frequency trading, causes regulatory concerns as a contributor to market fragility.{{cite journal|quote=This supports regulatory concerns about the potential drawbacks of automated trading due to operational and transmission risks and implies that fragility can arise in the absence of order flow toxicity.|title=High frequency trading and fragility|author=Giovanni Cespa, Xavier Vives|journal=Working Papers Series|publisher=European Central Bank|issue=2020|date=February 2017|url=https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp2020.en.pdf}} United States regulators have published releases{{cite web|url=http://www.jdsupra.com/legalnews/cftc-publishes-sweeping-concept-release-68577|title="CFTC Publishes Sweeping Concept Release Asking Questions About Additional Regulation of Automated Trading Strategies and High-Frequency Trading" - JD Supra|publisher=}}{{cite web|url=https://www.sec.gov/news/press/2010/2010-210.htm|title=SEC Adopts New Rule Preventing Unfiltered Market Access (Press Release No. 2010-210; November 3, 2010|publisher=}} discussing several types of risk controls that could be used to limit the extent of such disruptions, including financial and regulatory controls to prevent the entry of erroneous orders as a result of computer malfunction or human error, the breaching of various regulatory requirements, and exceeding a credit or capital limit.

The use of high-frequency trading (HFT) strategies has grown substantially over the past several years and drives a significant portion of activity on U.S. markets. Although many HFT strategies are legitimate, some are not and may be used for manipulative trading. A strategy would be illegitimate or even illegal if it causes deliberate disruption in the market or tries to manipulate it. Such strategies include "momentum ignition strategies": spoofing and layering where a market participant places a non-bona fide order on one side of the market (typically, but not always, above the offer or below the bid) in an attempt to bait other market participants to react to the non-bona fide order and then trade with another order on the other side of the market. They are also referred to as predatory/abusive strategies. Given the scale of the potential impact that these practices may have, the surveillance of abusive algorithms remains a high priority for regulators. The Financial Industry Regulatory Authority (FINRA) has reminded firms using HFT strategies and other trading algorithms of their obligation to be vigilant when testing these strategies pre- and post-launch to ensure that the strategies do not result in abusive trading.

FINRA also focuses on the entry of problematic HFT and algorithmic activity through sponsored participants who initiate their activity from outside of the United States. In this regard, FINRA reminds firms of their surveillance and control obligations under the SEC's Market Access Rule and Notice to Members 04-66,{{cite web|url=http://www.finra.org/Industry/Regulation/Notices/2004/P010269|title=Notice to Members 04-66 – FINRA.org|publisher=}} as well as potential issues related to treating such accounts as customer accounts, anti-money laundering, and margin levels as highlighted in Regulatory Notice 10-18 {{cite web|url=https://www.finra.org/Industry/Regulation/Notices/2010/P121248 |title=FINRA Issues Guidance on Master and Sub-Account Arrangements |accessdate=2014-12-25 |url-status=dead |archiveurl=https://web.archive.org/web/20141225163346/https://www.finra.org/Industry/Regulation/Notices/2010/P121248 |archivedate=2014-12-25 }} and the SEC's Office of Compliance Inspections and Examination's National Exam Risk Alert dated September 29, 2011.{{Cite web |title=Risk Alert Master Subaccounts |url=https://www.sec.gov/about/offices/ocie/riskalert-mastersubaccounts.pdf |website=www.sec.gov}}

FINRA conducts surveillance to identify cross-market and cross-product manipulation of the price of underlying equity securities. Such manipulations are done typically through abusive trading algorithms or strategies that close out pre-existing option positions at favorable prices or establish new option positions at advantageous prices.

In recent years, there have been a number of algorithmic trading malfunctions that caused substantial market disruptions. These raise concern about firms' ability to develop, implement, and effectively supervise their automated systems. FINRA has stated that it will assess whether firms' testing and controls related to algorithmic trading and other automated trading strategies are adequate in light of the U.S. Securities and Exchange Commission and firms' supervisory obligations. This assessment may take the form of examinations and targeted investigations. Firms will be required to address whether they conduct separate, independent, and robust pre-implementation testing of algorithms and trading systems. Also, whether the firm's legal, compliance, and operations staff are reviewing the design and development of the algorithms and trading systems for compliance with legal requirements will be investigated. FINRA will review whether a firm actively monitors and reviews algorithms and trading systems once they are placed into production systems and after they have been modified, including procedures and controls used to detect potential trading abuses such as wash sales, marking, layering, and momentum ignition strategies. Finally, firms will need to describe their approach to firm-wide disconnect or "kill" switches, as well as procedures for responding to catastrophic system malfunctions.{{Cite journal |last=Foley |first=Michael T. |last2=Angstadt |first2=Janet M. |last3=Pazzol |first3=Ross |last4=Van De Graaff |first4=James D. |date=2016-01-01 |title=FINRA rule amendment requires registration of associated persons who develop algorithmic trading strategies |url=https://doi.org/10.1108/JOIC-07-2016-0028 |journal=Journal of Investment Compliance |volume=17 |issue=3 |pages=39–41 |doi=10.1108/JOIC-07-2016-0028 |issn=1528-5812|url-access=subscription }}{{Cite news |last=Scopino |first=Gregory |date=2015 |title=Preparing Financial Regulation for the Second Machine Age: The Need for Oversight of Digital Intermediaries in the Futures Markets |publisher=Columbia Business Law Review |volume=2015 |issue=2: 439}}{{Cite web |date=2015-03-26 |title=Regulatory Notice 15-09 {{!}} FINRA.org |url=https://www.finra.org/rules-guidance/notices/15-09 |access-date=2024-03-23 |website=www.finra.org |language=en}}

Examples of recent substantial market disruptions include the following:

• On May 6, 2010, the Dow Jones Industrial Average declined about 1,000 points (about 9 percent) and recovered those losses within minutes. It was the second-largest point swing (1,010.14 points) and the largest one-day point decline (998.5 points) on an intraday basis in the Average's history. This market disruption became known as the Flash Crash and resulted in U.S. regulators issuing new regulations to control market access achieved through automated trading.{{cite web|url=http://www.notimetotrade.com |title=No Time To Trade |accessdate=2015-05-29 |url-status=dead |archiveurl=https://web.archive.org/web/20150529200459/http://www.notimetotrade.com/ |archivedate=2015-05-29 }}
• On August 1, 2012, between 9:30 a.m. and 10:00 a.m. EDT, Knight Capital Group lost four times its 2011 net income.{{cite web|url=https://www.bloomberg.com/news/articles/2012-08-02/knight-shows-how-to-lose-440-million-in-30-minutes|title=Knight Shows How to Lose $440 Million in 30 Minutes|first=Matthew Philips|last=matthewaphilips|website=Bloomberg News |publisher=}} Knight's CEO Thomas Joyce stated, on the day after the market disruption, that the firm had "all hands on deck" to fix a bug in one of Knight's trading algorithms that submitted erroneous orders to exchanges for nearly 150 different stocks. Trading volumes soared in so many issues, that the SPDR S&P 500 ETF (SYMBOL: SPY), which is generally the most heavily traded U.S. security, became the 52nd-most traded stock on that day, according to Eric Hunsader, CEO of market data service Nanex. Knight shares closed down 62 percent as a result of the trading error and Knight Capital nearly collapsed. Knight ultimately reached an agreement to merge with Getco, a Chicago-based high-speed trading firm.{{cite web|url=https://dealbook.nytimes.com/2012/12/19/knight-capital-and-getco-to-merge/?_r=0|title=Knight Capital and Getco to Merge|date=19 December 2012 |publisher=}}{{cite web|url=https://www.bloomberg.com/news/articles/2013-06-06/how-the-robots-lost-high-frequency-tradings-rise-and-fall|title=How the Robots Lost: High-Frequency Trading's Rise and Fall|author=Matthew Philips|work=Bloomberg}}

• High-frequency trading
• Algorithmic trading
• Day trading software
• Technical analysis software
• Systematic trading
• {{section link|Outline_of_finance#Quantitative_investing}}

{{Reflist|30em}}

Category:Share trading

Category:Financial software

Category:Electronic trading systems

Category:Algorithmic trading

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