The Efficiency of Algorithmic Trading on today´s Financial Market

Updated: Nov 28




Author: Gracy Joseph

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Date of Publication: 07/11/2022






What is Algorithmic Trading?

Algorithmic Trading or algo-trading brings together trading decisions. Specifically, it contains a pre-set or a defined set of algorithms or instructions like a computer program. So, it is written by a trader or an investor to place a trade. Algorithmic Trading, is also called as black-box trading, automated trading or algo-trading. In fact, it is responsible to create gains or profits with a certain frequency, speed and authenticity. Thus, these things are impossible for a human trader to perform provided some certain conditions are met.




In particular, the algorithms or the pre-set rules are based on factors like time, cost, trading volume or any mathematical model. In addition, algo-trading rules out the effect of human feelings/touch while trading and renders markets more liquid withthe most efficient execution of trade. So, it is a combination of financial markets along with computer programming for the execution of trades. Therefore, the user needs to have computer/network access, knowledge of the financial market and coding skills to get started with it.




Basic criteria to be followed by the trader

  1. Buying some 50 shares of stock when its 50-day moving average has gone above the 200-daymoving average.

  2. Selling shares of the stick when its 50-day moving average has gone below the 200-day moving average.

These above 2 points make the program check and supervise the stock price and the moving average indicators. In particular, it will buy and sell orders accordingly when the prerequisites are fulfilled. So, due to the algo-trading, there isn’t a necessity to observe patterns and prices live or trade the orders manually.


Moving average

It's a stock indicator used in technical analysis to calculate the average of past data or pattern points. Actually, these level the day to day or month to month price fluctuations and that identifies the trends and patterns. Moving averages are calculated to know the direction of the trend of a particular stock. It is also known as a trend-following or lagging indicator as it is based on the past prices.


Why a 200-day moving average?

The longer the moving average period, the greater the lag. So, the 200-day moving average will have a greater degree of lag than a 20-day moving average as it has the prices for the past 200 days. Investors and traders have been widely following the 50-day and the 20-day moving average figures and it is considered to be crucial trading signals. Moreover, there are times when investors or traders use different periods of varying lengths while calculating the moving averages based on their trading assumptions. However, for short term trading, shorter moving averages are used and for the long term moving averages are used for the long term investors.



In fact, it is popular as it is easy to implement. It starts with the set of instructions or algorithms buying a security or stocks if the live market price is below its average market price throughout some period (days or months or years). And selling the same if its live market price is more than its average market price over that period. In Figure 2 I have considered a 30-day moving trading algorithm. The algorithm buys shares in Apple (AAPL) as shown in Figure 2, if the current live price is less than the 30-day moving average. Also, it sells it if the current live price is more than the 30-day moving average. In particular, the green arrow in Figure 2 shows the point when the algorithm has bought the shares and the red indicates when it has sold it.


Pros of using Algorithmic Trading

  • Less transaction costs

  • No manual trading hence reducing the risk of human errors

  • As the trading is performed irrespective of emotional or psychological factors the risk caused by manual errors is reduced.

  • Tradings take place in time correctly to avoid significant price changes

  • Tradings happen at the best possible rates and the order placement is efficient and accurate

  • As it is automated, there are checks made simultaneously on multiple market conditions.

  • Algo-trading is performed using historical data and live data to form a strategy.


Where do we use Algo-trading?

Actually we use it in different forms of trading and investment activities like:

  • Middle to long term investors (buy-side firms) like pension and mutual funds. In addition, insurance companies also use algo-trading for buying stocks in big quantities to not influence stock prices.

  • Also, short term traders (sell side participants) like speculators and market makers. They are the ones who have benefited from this as it helps in creating enough liquidity for sellers in the market.

  • Last but not the least, the systematic traders who are the trend followers or pair traders that use market neutral trading strategy. In fact, they use id for efficient programming of their trading rules automatically.


Cons of using Algorithmic Trading

  • As it is automated and without any human intervention there is a possibility that the trading algorithm may miss out on trades. This is because it doesn’t show any signs of what the algorithm aims to look for.

  • Reliance on technology can be a disadvantage as the trading orders are stored on the computer rather than on the server.


How to learn and use it?

Algo-trading is legal. In fact, there are no significant rules and regulations that tell us how to use it. Also it depends on quantitative and qualitative analysis and as this is an investment in the financial market, prior knowledge of stocks and trading is a must. Last, COBOL programming languages like C/C++ are popular amongst the traders to perform algorithmic trading.



Conclusion

While performing algo-trading, one needs to have coding skills as it opens and closes trades according to the instructions provided. Traders/investors can set or come to an agreement when they want to have their trades opened or closed. Also, it can provide computing power for the higher trading frequency. Therefore, with the rise in technology and strategies, algo-trading is prevailing in the financial market.


 

References List:

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  • C. Team, “Six essential skills of master traders,” January 2022. [Online]. Available: https: //corporatefinanceinstitute.com/resources/knowledge/ trading-investing/six-essential-skills-of-master-traders/.

  • S. Seth, “Basics of algorithmic trading: Concepts and examples,” June 2022. [Online]. Available: https://www.investopedia.com/contributors/53524/.

  • J. Fernando, “Moving average (ma): Purpose, uses, formula, and examples,” August 2022. [Online]. Available: https://www.investopedia.com/terms/m/movingaverage.asp.

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  • S. Markets, “Requirements for algorithmic trading,” June 2019. [Online]. Available: https://www. nasdaq.com/articles.

  • Y. W. a. G. Yan, “ Survey on the application of deep learning in algorithmic trading,” Data Science in Finance and Economics, vol. 345– 361, p. 1, 2021.

  • A. (AAPL), “Trading view of stocks,” Trading View, [Online]. Available: https://www.tradingview.com/ chart/?symbol=AAPL.

  • P. image, “Algorithmic Trading,” Pexels, [Online]. Available: https://www.pexels.com/search/ algorithmic.

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