Automated Financial Trading System

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An Automated Financial Trading System is an algorithmic trading system that executes automated financial market trades through computer programs without human intervention based on predefined trading rules and automated market conditions.

References

2025-04-27

[1] https://en.wikipedia.org/wiki/Automated_trading_system
[2] https://trendspider.com/learning-center/algorithmic-trading-the-basics/
[3] https://en.wikipedia.org/wiki/Electronic_trading
[4] https://bookmap.com/blog/top-trading-algo-bots-automating-your-trading-strategy
[5] https://www.investing.com/brokers/automated-trading/
[6] https://intrinio.com/blog/how-to-use-dark-pool-data-for-trading-fintech-navigating-shadows
[7] https://orhanergun.net/hft-high-frequency-trading
[8] https://blog.counselstack.com/algorithmic-trading-regulations-compliance-risk-controls/
[9] https://www.fia.org/sites/default/files/2024-07/FIA_WP_AUTOMATED%20TRADING%20RISK%20CONTROLS_FINAL_0.pdf
[10] https://techbullion.com/real-time-monitoring-of-automated-trading-systems-minimizing-losses/

2024

  • (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Automated_trading_system Retrieved:2024-12-4.
    • An automated trading system (ATS), a subset of algorithmic trading, uses a computer program to create buy and sell orders and automatically submits the orders to a market center or exchange. The computer program will automatically generate orders based on predefined set of rules using a trading strategy which is based on technical analysis, advanced statistical and mathematical computations or input from other electronic sources. * These automated trading systems are mostly employed by investment banks or hedge funds, but are also available to private investors using simple online tools. An estimated 70% to 80% of all market transactions are carried out through automated trading software, in contrast to manual trades. Automated trading systems are often used with electronic trading in automated market centers, including electronic communication networks, "dark pools", and automated exchanges. Automated trading systems and electronic trading platforms can execute repetitive tasks at speeds orders of magnitude greater than any human equivalent. Traditional risk controls and safeguards that relied on human judgment are not appropriate for automated trading and this has caused issues such as the 2010 Flash Crash. New controls such as trading curbs or 'circuit breakers' have been put in place in some electronic markets to deal with automated trading systems.

2014

  • (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/algorithmic_trading Retrieved:2014-12-2.
    • Algorithmic trading, also called automated trading, black-box trading, or algo trading, is the use of electronic platforms for entering trading orders with an algorithm which executes pre-programmed trading instructions whose variables may include timing, price, or quantity of the order, or in many cases initiating the order by automated computer programs. Algorithmic trading is widely used by investment banks, pension funds, mutual funds, and other buy-side (investor-driven) institutional traders, to divide large trades into several smaller trades to manage market impact and risk.[1] [2] Many types of algorithmic or automated trading activities can be described as high-frequency trading (HFT). As a result, in February 2012, the Commodity Futures Trading Commission (CFTC) formed a special working group that included academics and industry experts to advise the CFTC on how best to define HFT. [3] [4] HFT strategies utilize computers that make elaborate decisions to initiate orders based on information that is received electronically, before human traders are capable of processing the information they observe. Algorithmic trading and HFT have resulted in a dramatic change of the market microstructure, particularly in the way liquidity is provided. Algorithmic trading may be used in any investment strategy, including market making, inter-market spreading, arbitrage, or pure speculation (including trend following). The investment decision and implementation may be augmented at any stage with algorithmic support or may operate completely automatically. One of the main issues regarding HFT is the difficulty in determining how profitable it is. A report released in August 2009 by the TABB Group, a financial services industry research firm, estimated that the 300 securities firms and hedge funds that specialize in this type of trading took in a maximum of US$21 billion in profits in 2008, which the authors called "relatively small" and "surprisingly modest" when compared to the market's overall trading volume. In March 2014, Virtu Financial, a high-frequency trading firm, reported that during five years it made profit 1,277 out of 1,278 days, losing money just one day. [5] A third of all European Union and United States stock trades in 2006 were driven by automatic programs, or algorithms, according to Boston-based financial services industry research and consulting firm Aite Group. [6] As of 2009, studies suggested HFT firms accounted for 60-73% of all US equity trading volume, with that number falling to approximately 50% in 2012.[7] [8] In 2006, at the London Stock Exchange, over 40% of all orders were entered by algorithmic traders, with 60% predicted for 2007. American markets and European markets generally have a higher proportion of algorithmic trades than other markets, and estimates for 2008 range as high as an 80% proportion in some markets. Foreign exchange markets also have active algorithmic trading (about 25% of orders in 2006). [9] Futures markets are considered fairly easy to integrate into algorithmic trading, [10] with about 20% of options volume expected to be computer-generated by 2010. Bond markets are moving toward more access to algorithmic traders. Algorithmic trading and HFT have been the subject of much public debate since the U.S. Securities and Exchange Commission and the Commodity Futures Trading Commission said in reports that an algorithmic trade entered by a mutual fund company triggered a wave of selling that led to the 2010_Flash Crash. The same reports found HFT strategies may have contributed to subsequent volatility. As a result of these events, the Dow Jones Industrial Average suffered its second largest intraday point swing ever to that date, though prices quickly recovered. (See List of largest daily changes in the Dow Jones Industrial Average.) 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.” However, other researchers have reached a different conclusion. One 2010 study found that HFT did not significantly alter trading inventory during the Flash Crash. Some algorithmic trading ahead of index fund rebalancing transfers profits from investors.
  1. ↑ Moving markets Shifts in trading patterns are making technology ever more important, The Economist, Feb 2, 2006
  2. ↑ Algorithmic Trading: Hype or Reality?
  3. ↑ CFTC Panel Urges Broad Definition of High-Frequency Trading, Bloomberg News, June 20, 2012
  4. ↑ Futures Trading Commission Votes to Establish a New Subcommittee of the Technology Advisory Committee (TAC) to focus on High Frequency Trading, February 9, 2012, Commodity Futures Trading Commission
  5. ↑ Virtu Financial Form S-1, available at https://www.sec.gov/Archives/edgar/data/1592386/000104746914002070/a2218589zs-1.htm
  6. ↑ [1]
  7. ↑ Rob Iati, The Real Story of Trading Software Espionage, AdvancedTrading.com, July 10, 2009
  8. ↑ Times Topics: High-Frequency Trading, The New York Times, December 20, 2012
  9. ↑ A London Hedge Fund That Opts for Engineers, Not M.B.A.’s by Heather Timmons, August 18, 2006
  10. ↑ Looking for options Derivatives drive the battle of the exchanges, April 15, 2007, Economist.com

2013

2011 =

  • (Hendershott et al., 2011) ⇒ Terrence Hendershott, Charles M. Jones, and Albert J. Menkveld. (2011). “Does algorithmic trading improve liquidity?.” In: The Journal of Finance, 66(1). doi:10.1111/j.1540-6261.2010.01624.x
    • ABSTRACT: Algorithmic trading (AT) has increased sharply over the past decade. Does it improve market quality, and should it be encouraged? We provide the first analysis of this question. The New York Stock Exchange automated quote dissemination in 2003, and we use this change in market structure that increases AT as an exogenous instrument to measure the causal effect of AT on liquidity. For large stocks in particular, AT narrows spreads, reduces adverse selection, and reduces trade-related price discovery. The findings indicate that AT improves liquidity and enhances the informativeness of quotes.
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