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Algorithmic Trading

Updated: Jul 31, 2020

- By Aastha and Nandini|

As the dependence on computers and technology increases with an exponential rate, investors are no exception.

Thanks to algorithmic trading, a growing number of speculators are realizing what they consider to be ideal market conditions are actually imprudent.

 Also known as Algo Trading, algorithmic trading is a method of stock trading that uses intricate mathematical models and formulas to initiate high-speed, automated financial transactions. The objective of algorithmic trading is to assist speculators with executing an explicit financial strategy as fast as conceivable, to acquire higher profits.

One of the biggest problems that a shop floor trader faces is his ability to be disciplined and stick to the plan. With algo trading, you can be guaranteed that the robot will be disciplined and stick to the trading plan that has been set up. Frequently, it is the capacity to stick to the plan that makes the difference between a gainful merchant and an unbeneficial broker. The secret to algo trading’s sudden popularity lies in the advantages it holds over manual trading such as higher speed, accuracy, and reduced costs.


Algorithmic trading uses computer-based programs to trade at high speed and volume according to some preset criteria, such as stock rates and specific market fluctuations. For instance, a merchant might use algorithmic trading to execute orders rapidly when a certain stock reaches or falls below a specific price. The algorithm can indicate as to how many shares a trader should buy or sell based on such market conditions. Once a program is put in motion, that trader can then sit back and relax knowing that trades will automatically take place once those preset requirements are met.


A very basic algorithmic trading system can be based on just one or two very simple indicators. The following are the examples of algorithmic trading strategies starting from the simplest and progressing to more complex systems-

• Trend following strategies

• Mean reversion strategies

• Arbitrage trading strategies

• Statistical arbitrage

• Index arbitrage 


• Quantitative Investing Strategies

• Quant trading strategies 

• Index Changes


Moving average trading algorithms are famous and incredibly simple to implement. The algorithm purchases a security (e.g. stocks) if its current market price is lower than its average market price over some time period and sells a security if its market price is over its average market price over some period. Let us consider an example of the criteria that a trader Rakesh mostly uses while executing a trade.

Rakesh uses moving averages as the technical indicator and buy the stocks of a company only when the 30 days 'moving average' price of the stock goes more than the 180 days moving average price and similarly, sells the stock when the 30 days 'moving average' price of the stock goes below the 180 days moving average price.

This criterion can be easily fed as a computer program into the software.

So, this algo will only buy a specified number of shares of the specified company, only when the 30-days moving average is more than the 180-days moving average.

Therefore, Rakesh has specified the time, price, and volume to the program as an algorithm, and the software will keep on monitoring the price of the stock and execute the trade on the trader's behalf as soon as the pre-specified criteria are met.

Rakesh does not need to keep a track of the prices and identify the trading opportunity, the algorithm does it for him.


One significant decrement of algorithmic trading is that one simple mistake can have countless repercussions. It's one thing for a dealer to make an awful call and lose cash on a solitary exchange, but when you have a faulty algorithm, the results can be downright catastrophic. The quality control measures can help prevent losses due to poorly defined or coded algorithms. Investors should remember the risk of abandoning control and letting computers do all of the work.

Another downside of Algo trading is the regularly occurring flash crash in the framework. A flash crash is an event in electronic securities markets wherein the withdrawal of stock orders rapidly amplifies price declines. The result appears to be a rapid sell-off of securities that can happen over a few minutes, resulting in dramatic declines. Flash crashes can trigger circuit breakers at significant stock trades like the NYSE, which halt trading until buy and sell orders can be coordinated up equally and trading can resume in a precise manner.


Algo trading is quickly becoming standard for short term traders and longer-term fund managers.

Like most businesses, continued automation is now a feature of monetary markets. New technologies like machine learning and big data are also resulting in new approaches to trading, most of which are best suited to automated trading. Certainly, algorithmic trading is commanding a larger share of the market than it did before and its popularity and applications are only just beginning.


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