algorithmic trading involves three broad areas of algorithms execution algorithms profit seeking or black box algorithms and high frequency trading hft algorithms while not wholly separated in real world applications these are all automated processes for financial trades and decision making that use price timing volume and more along with sets of rules to tackle trading problems that might once have required a team of financial specialists 
algorithmic trading uses complex mathematical models with human oversight to make decisions to trade securities and hft algorithmic trading enables firms to make tens of thousands of trades per second algorithmic trading can be used for among other things order execution arbitrage and trend trading strategies 1understanding algorithmic tradingthe use of algorithms in trading increased after computerized trading systems were introduced in american financial markets during the 1970s in 1976 the new york stock exchange introduced its designated order turnaround system for routing orders from traders to specialists on the exchange floor 2 in the following decades exchanges enhanced their abilities to accept electronic trading and by 2009 upward of 60 of all trades in the u s were executed by computers 3michael lewis the author of bestselling books on underdogs in finance baseball and other sectors brought hft algorithmic trading to the public s attention with flash boys which documented the lives of wall street traders and entrepreneurs who helped build the companies that came to define the structure of electronic trading in the u s 4 his book showed that these companies were engaged in an arms race to build ever faster computers which could communicate with exchanges ever more quickly to gain an advantage over competitors with speed using order types that benefited them to the detriment of average investors the algorithms used in financial trading are rules or instructions designed to make trading decisions automatically they range from simple single stock to more complex black box algorithms that analyze market conditions price moves and other financial data to execute trades at optimal times for the least cost to maximum profit ratio the crossover of computer engineering and finance is notorious for its leaden jargon so we won t weigh you down with too many terms here while some phrases might change slightly from one trading firm to the next the following should give you an idea of the wide uses for algorithmic trading5 let s walk through a straightforward algorithmic trading example suppose you ve programmed an algorithm to buy 100 shares of a particular stock of company xyz whenever the 75 day moving average goes above the 200 day moving average this is known as a bullish crossover in technical analysis and often indicates an upward price trend the execution algorithm monitors these averages and automatically executes the trade when this condition is met eliminating the need for you to watch the market continuously this allows for precise emotion free trading based on specific predetermined rules which is the essence of algorithmic trading we ve separated these algorithms since they function differently than those above and are at the heart of debates over using artificial intelligence ai in finance black box algorithms are not just preset executable rules for certain strategies the name is for a family of algorithms in trading and a host of other fields 1 the term black box refers to an algorithm with obscure and undisclosable internal mechanisms 7unlike other algorithms that follow predefined execution rules such as trading at a certain volume or price black box algorithms are characterized by their goal oriented approach as complicated as the algorithms above can be designers determine the goal and choose specific rules and algorithms to get there trading at certain prices at certain times with a certain volume black box systems are different since while designers set objectives the algorithms autonomously determine the best way to achieve them based on market conditions outside events etc often those using the term in the public sphere confuse two issues there are quantified strategies that firms and others regard as trade secrets which users know but don t share competitors and regulators may not understand the strategies for example a high frequency trading firm might be using however that s because those who do within the firm aren t sharing proprietary technology then there are black box systems a hallmark of black box algorithms especially those employing artificial intelligence and machine learning is another issue namely that the decision making processes of these systems are opaque even to their designers 8 while we can measure and evaluate these algorithms outcomes understanding the exact processes undertaken to arrive at these outcomes has been a challenge 1 this lack of transparency can be a strength since it allows for sophisticated adaptive strategies to process vast amounts of data and variables but this can also be a weakness because the rationale behind specific decisions or trades is not always clear since we generally define responsibility in terms of why something was decided this is not a minor issue regarding legal and ethical responsibility within these systems thus this obscurity raises questions about accountability and risk management within the financial world as traders and investors might not fully grasp the basis of the algorithmic systems being used 9 despite this black box algorithms are popular in high frequency trading and other advanced investment strategies because they can outperform more transparent and rule based sometimes called linear approaches such systems are at the leading edge of financial technology research as fintech firms look to take the major advances in machine learning and artificial intelligence in recent years and apply them to financial trading 710just as smartphone apps and advanced ai systems have enabled non specialists to create tailored applications and application programming interfaces popularly known as apis the world of algorithmic trading has allowed outsiders to have a hand in expanding upon their proprietary work this open source approach permits individual traders and amateur programmers to participate in what was once the domain of specialized professionals hedge funds and investment firms such as two sigma and panagora have at times leveraged this shift by crowdsourcing algorithms and trumpeting their efforts to pay back the community of programmers by going the other way and releasing improvements to open source applications for all to use 11 they also host competitions where amateur programmers can propose their trading algorithms with the most profitable applications earning commissions or recognition but just as tech companies have leveraged open access applications and programming for problem solving and community engagement fintech firms are increasingly going beyond just using open access cloud computing and similar apps common all over the business world the fintech open source finos foundation said in a november 2023 report that about a quarter of financial service professionals were involved in open source data science and artificial intelligence machine learning platforms 12 nevertheless there may be limits to how far this can go in the financial sector about two thirds of those finos surveyed said that they or their firms worried about using open access systems given the need to safeguard proprietary knowledge advantages and disadvantages of algorithmic tradingspeed executes trades faster than humans accuracy reduces chances of manual errors efficiency can trade 24 7 without fatigue emotionless avoids emotional trading decisions backtesting traders and researchers can test diverse scenarios outside real world trading system failure technical glitches can cause losses over optimization can lead to unrealistic results potential liquidity issues market manipulation may be used for nefarious purposes complacency not adapting algorithmic system to market and regulatory changes using algorithmic trading can offer quicker and more efficient responses to market changes and events they can also automate and ensure a closer alignment between investment decisions and trading instructions leading to lower market impact costs and timing risks as well as a higher rate of order completion here are additional advantages 1algorithmic trading has its limits both for individual traders and concerning the externalities for other traders and the market as a whole 1