Algorithmic Trading

Algorithmic Trading

Shruti Chand

In this article, Shruti Chand (ESSEC Business School, Master in Management, 2020-2022) elaborates on the concept of algorithmic trading.

This read will help you get started with understanding algorithmic trading and how it is practiced in today’s world.

What is it?

Today, as most activities of the world are moving towards (or already switched to) automation, trading is no different. The process of trading is automated using computer algorithms; which is basically a set of instructions. Trading algorithms are coded based on parameters such as stock price, volume, time, etc. When the current market conditions meet the criteria pre-defined in the algorithm, it executes a buy or sell order, without any human intervention. This is algorithmic trading.

Most algo-trading today is high-frequency trading (HFT), which attempts to capitalize on placing a large number of orders at rapid speeds (tens of thousands of trades per second) across multiple markets and multiple decision parameters based on preprogrammed instructions.

Some studies believe that around 92% of trading in the Forex market was performed by trading algorithms rather than humans.

New developments in artificial intelligence have enabled computer programmers to develop programs that can improve themselves through an iterative process called deep learning. Traders are developing algorithms that rely on this technique to make themselves more profitable.

How is it done?

We illustrate the implementation of algorithmic trading with two examples: technical analysis, arbitrage and market making.

Technical analysis:

Following trends in technical indicators such as moving average or price level movements is a safe and easy strategy used in programs in Algo-trading. There is no involvement of price predictions or forecasts.

Consider the following trade criteria:

  • Buy 100 shares of a stock when the 50-day moving average of the stock goes higher than its 200-day moving average (a moving average is basically the smoothening out of the price fluctuations by taking the average of previous data points, facilitating the identification of trends).
  • Sell the shares when the 50-day moving average of the stock goes lower than its 200-day moving average.

Using these two simple instructions, a computer program will automatically monitor the stock price (and the moving averages) and implement the buy and sell orders when the defined conditions are met. The trader no longer needs to painstakingly monitor live prices and graphs or put in the orders manually. This is done automatically by the algo-trading system by correctly identifying the trading opportunity.

Using 50-day and 200-day moving averages is a fairly popular trend-following strategy.


To profit from arbitrage opportunities is a common strategy in algo-trading.

When a stock is listed in two different markets, you can buy shares at a lower price in one market and simultaneously sell them at a higher price in the other market. This offers the price differential as a risk-free profit, which defines an arbitrage. The same can be replicated for assets traded in the sport market and their futures in the derivatives market as the price differential may not exist from time to time. Implementing an algorithm to identify such price differentials and placing the orders efficiently helps seize profitable opportunities.

Market making:

Besides that, algo-trading fairly affects how liquidity is provided to market participants as market making has been highly automized.

Other strategies:

Besides these, there are various other strategies implemented by traders like Index Fund Rebalancing, Mathematical Model-based Strategies, Trading Range (Mean Reversion), Percentage of Volume (POV), etc.


Pros and Cons of Algorithmic Trading:


 Naturally, removing humans from the equation does have its undeniable merits.

The trading process becomes much faster and efficient. Additionally, the scope of human error is eliminated from the trading execution (although coding errors may still persist). Furthermore, the trades are not at risk of being driven by human emotions and other psychological factors.

Additionally, algo-trading significantly cuts down on costs associated with trading.

According to research, algorithmic trading is especially beneficial for large order sizes that may comprise as much as 10% of the overall trading volume.


While it has its advantages, algorithmic trading can also exacerbate the market’s negative tendencies by causing crashes (called “flash crash”) and immediate loss of liquidity.

The speed of order execution, an advantage in normal circumstances, can become a problem when several orders are executed simultaneously without human involvement. The flash crash of 2010 has been blamed on algo-trading.

Additionally, the liquidity that is created through rapid buy and sell orders, can disappear in a moment, eliminating the chance for traders to profit off-price changes. It can also cause instant loss of liquidity. Research has revealed that algorithmic trading was a major factor in causing a loss of liquidity in currency markets after the Swiss franc discontinued its euro peg in 2015.


Relevance to the SimTrade certificate

This post deals with Algorithmic Trading which is used by various traders and investors in different instruments. This can be learned in the SimTrade Certificate:

About theory

  • By taking the market orders course , you will know more about how investors can use various strategies to invest in order to trade in the market.

Take SimTrade courses

About practice

  • By launching the series of Market maker simulations, you can extend your learning about financial markets and trading approaches.

Take SimTrade courses

About the author

Article written by Shruti Chand (ESSEC Business School, Master in Management, 2020-2022).

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