What is Algorithmic Trading?
Algorithmic trading also referred to as Algo-trading, is a variant of automated trading that basically involves the usage of automated platforms and advanced tools of maths and computer programming to drive trading transactions in the financial markets. The system utilizes a mathematical model or algorithm or standardized instruction set that facilitate placing of buying or sell signal in the financial marketsFinancial MarketsThe term "financial market" refers to the marketplace where activities such as the creation and trading of various financial assets such as bonds, stocks, commodities, currencies, and derivatives take place. It provides a platform for sellers and buyers to interact and trade at a price determined by market forces. and hence facilitate trade without the involvement of humans.
Components of Algorithmic Trading
#1 – An Algorithm
An algorithm can be defined as a set of instructions that perform certain repetitive functions. It can also be developed to cater to certain problem-solving situations. It helps in the easy facilitation of data processing and identification of trends.
#2 – Computer Program & Automated Trading Platforms
An automated trading platform provides a means to execute the algorithm developed by the programmers. It, as a platform, executes the computer programs developed by the programmers and algo-traders, thereby facilitating buy and sell orders in the financial markets. These platforms also help in the back-testing of algorithms developed by the algo-traders or programmers as well before they can be deployed.
#3 – Technical Analysis
The technical analysis involves the study and analysis of the price movements of the listed securitiesListed SecuritiesListed security refers to a financial instrument such as stocks, bonds, derivatives, etc., registered with and readily tradable on the stock exchanges like NASDAQ and NYSE. in the financial markets. There are several methods, such as 150-day moving average, 200-day moving averageMoving AverageMoving Average (MA), commonly used in capital markets, can be defined as a succession of mean that is derived from a successive period of numbers or values and the same would be calculated continually as the new data is available. This can be lagging or trend-following indicator as this would be based on previous numbers., double exponential moving average, random oscillators, which helps in the identification of price trends for a particular security.
The methods of technical analysis can be developed as an algorithm. They can, in turn, be transformed into a computer program that can then be deployed into the financial markets to automate the trading function.
#4 – Back-testing
The back-testing is the process of testing the algorithm and verify whether the strategy would deliver the results as anticipated by the trader. It involves testing of the strategy developed by the programmer on the historical market data. The back-testing lets the trader identify the pitfalls that could have emerged if the strategy were used with the live market trades.
Algorithmic Trading Examples
Suppose a hedge fund has developed a quantitative model. They have developed a computer program that deploys the model into the financial market. The computer program assesses the market situation dynamically and thereby implement a hedging strategy in line with the market sentiments.
- Suppose a trader follows a trading criterion that it always purchases 100 shares whenever the price of the stock moves beyond and above the double exponential moving average.
- Simultaneously it places a sell order when the price of the stock goes below the double exponential moving average.
- The trader can hire a computer programmer who can understand the concept of the double exponential moving average.
- The programmer can develop a computer code that performs the above two instructions.
- The computer program can be made so dynamic that it can monitor the live prices of the financial markets and, in turn, trigger the above instructions.
- The computer program or the algorithm saves time for the trader of going into the trading platforms, monitor prices, and then place the trading orders.
- The flash crash of 2010 can be regarded as an example of algorithm trading. In this crisis, there was the fast placement of sell orders for securities. There were also fast withdrawals of trade orders for securities and were high-frequency trades.
- The Regulatory authorities later placed circuit breakers to prevent such flash crash from happening again in the financial markets. They also prevented algo-trades from having direct access to the exchanges.
- The algo-trading helps in the lessening of transaction costs.
- The trades are placed into the system without the need for human intervention.
- They algo-trades are placed without any emotions or biases.
- The placement of algo-trades order happens instantly and at the best possible prices.
- It also helps in the perfect market timingMarket TimingMarket timing is the plan of buying and selling the securities on the basis of decisions made by financial investors. They do security analysis to earn a profit on selling and it is the action plan to cope up with the fluctuations in the market prices..
- It helps in the processing of big orders in an efficient and faster manner.
- The regulatory authorities always install circuit breakers, which limits the functionality of algo-trades.
- The liquidity provided by algo-traders can almost disappear in an instant or a matter of seconds.
- The execution speed of algo-trades without the intervention of humans can adversely impact live trades and settlements, which further limit the functionality of trading platforms and financial markets.
- It is hard for the regulatory authority to distinguish between a trade placed by a human and trade facilitated by an algorithm. Hence, they always increase the number of market participants when they suspect that the trades are executed through algorithm trades.
- The algo-trades, if not monitored, can trigger unnecessary volatility in the financial markets.
- The devising of the algorithm can be very complex and challenging.
- Since the approach of devising an algorithm is scientific, it is difficult for a traditional trader to learn such an approach and apply such algorithms in their daily trades.
- The development of algorithms generally involves the development of predictive and quantitative models. If such models are not back-tested, they can cause enormous losses for the traditional traders who may employ them without back-testing.
- An algorithm cannot overrule and overplay subjective judgment, which is present in the financial markets.
Algorithmic Trading – Important Points
- The algorithm trading employs the usage of computer programs.
- Before executing the algo-trades in the financial markets, it is always advised to perform back-testing of the automated strategy.
- The algorithm trading is employed by the high net worth individuals and institutional investorsInstitutional InvestorsInstitutional investors are entities that pool money from a variety of investors and individuals to create a large sum that is then handed to investment managers who invest it in a variety of assets, shares, and securities. Banks, NBFCs, mutual funds, pension funds, and hedge funds are all examples..
- Many institutional investors pay commissions to budding programmers for building a small piece of code that delivers the investors profitable returns.
Algorithm trading is a mechanism that facilitates buy and sell orders in the financial markets by using an algorithm which is executed by means of computer programs. A definite criterion is developed in terms of programmable code and placed in the automated platforms to execute trades in the financial markets. The execution of algorithm trades is very fast and can cause a potential crash in the financial markets.
To monitor such trades, the regulatory authorities install circuit breakers at critical junctures. Hedge fundsHedge FundsA hedge fund is an aggressively invested portfolio made through pooling of various investors and institutional investor’s fund. It supports various assets providing high returns in exchange for higher risk through multiple risk management and hedging techniques. and institutional investors are the major users of the algorithm trading as it helps them a place and executes large trade orders very easily. They further help in devising trading strategies such as taking up long and short positionsShort PositionsA short position is a practice where the investors sell stocks that they don't own at the time of selling; the investors do so by borrowing the shares from some other investors to promise that the former will return the stocks to the latter on a later date. simultaneously to handle the lump sum amounts systematically and in a careful manner.
This article has been a guide to algorithmic trading and its meaning. Here we discuss what is algorithmic trading along with the examples. We also discuss its limitations, advantages & disadvantages. You can learn more from the following articles –