What is Algorithmic Trading? A Guide for Beginners

Technology has always amazed humans with its incredible ability to enhance how we used to see the world around us. From entertainment to education, technology has spread its roots in different sectors making things easier and the fast-moving financial world is not an exception to it. Algo trading, also called Algorithmic trading that uses computer programs is a prime example of how technology is impacting and augmenting the forex trading market. Let’s understand algo trading in detail through this comprehensive guide highlighting its advantages and disadvantages for beginner traders.

What is Algo Trading?

Algo trading refers to trading through computer programs that are pre-programmed to follow a defined set of instructions by a trader or investor, i.e., algorithm, depending on factors like timing, price, volume, and more, for executing automated FX trades at high speed at precise moments. Thus, resulting in more trades in a limited time with fewer human emotions and trading errors.

Types of Algo trading

The algorithm in Algo trading can range from simple single stock to complex black box for obtaining the least cost-to-maximum profit ratio. The different types of algo trading are:

  • Arrival price algorithms: Such algorithms are designed by the traders to execute those trades that are as close to the stock price as when orders were placed. These algorithms help reduce the impact of adverse market conditions.
  • Portfolio algorithms: Also called basket algorithms, execute orders while evaluating the impact of other decisions and strategies in an investment portfolio.
  • Implementation shortfall algorithms: These algorithms execute trades with the motive of reducing the implementation shortfall to a minimum.
  • Percentage of volume: Using these algorithms, the traders can alter the order size reaction with that of the real-trade market trading volume with the aim of preserving the already determined percentage of the overall market.
  • Single-stock algorithms: This algorithm is created for the optimization of a single security-executed trade, after the consideration of factors like order size, and market conditions.
  • VWAP: The volume-weighted average price algorithms are designed to execute orders at a specific price (that closely matches the vol-weighted average price of an asset over a certain period.
  • TWAP: The Time-weighted average price algorithms are the defined programs that are used to distribute trades evenly in a specific period to achieve an average price mirroring the stock price’s time-weighted average price.
  • Risk-average parameter: The risk-average parameter algorithms may vary depending on trader and investment strategies. Additionally, these algorithms are not the primary ones, instead, they are used alongside other algorithms for adjusting the aggressiveness of trading depending on the risk tolerance of the trader.

Profitability with Algo Trading

Traders are transitioning from manual trading to algo trading at a very fast pace because of its accuracy in predicting the entry and exit of the trade positions after consideration of several factors, saving time and effort. However, creating an algorithm can be a difficult task for traders. Here are some factors to consider while creating an algorithmic trading strategy:

  • Make use of quantitative and mathematical tools like Calculus, Regression, probability, etc to create a trading strategy.
  • Use historical data to back-test the trading strategy. This will be profitable in discovering the viability of created strategies.
  • Since risks can arise when the market moves in opposite directions, risk management should also be in check.

Advantages vs Disadvantages of Algo Trading

Using Algo Trading can be beneficial for traders as it offers a quicker and more efficient response to market movements. The major advantages of using algo trading involve:

  • Anonymity: With algo trading, the trades can automate the trading process using computers and networks, thus, ensuring that orders are not exposed or discussed openly on the trading platforms.
  • Backtesting and research: The created algorithms can be backtested and researched on historical data to check their impact on the trade, helping traders reduce potential losses. Moreover, the researchers can use these algorithms to test various financial scenarios, thus, adding to their financial knowledge.
  • Emotionless decisions: Algo trading steps beyond human trading psychology and emotional factors to make a trade, leading to a more disciplined approach to trading.
  • Precision: Since algorithms are created by considering highly specific market conditions, it reduces the risks because of potential human error.
  • Speed and efficiency: With algo trading, the traders can execute trades at a faster rate as compared to humans, resulting in a quicker capitalization of market opportunities.
  • Market Access: Using high-speed networks, Algo Trading provides better and quicker access to markets and exchanges. If a trader does not have high-end systems, they can gain benefits from co-location and low-latency connections.
  • Low latency: It ensures that trades are executed at the correct time, instantly and accurately avoiding any significant change in price.


Though algo trading offers numerous benefits while trading CFD stocks and other assets, it also comes with its set of disadvantages. Some of them are:


  • Complexity: Since algorithmic trading strategies can be highly complex, it require sophisticated mathematical models and technical expertise to develop and implement effectively. Such complexity can lead to challenges in strategy development, testing, and maintenance.
  • Black Swan Events: Algorithmic trading systems are designed to operate on the basis of historical data and predefined rules. Thus, these systems may struggle to adapt to unforeseen events, such as market crashes or geopolitical crises, ultimately, leading to significant losses.
  • Compliance risks: The algo trading is subject to regulatory oversight, and firms must ensure compliance with relevant regulations and guidelines. If they fail to do so, it can result in regulatory scrutiny and penalties.
  • Cost: It involves a huge amount of money to develop algo trading strategies. Not only this but its maintenance can also be costly as it requires a huge technological infrastructure, data feeds, etc.
  • Illiquidity: The algorithmic trading strategies rely on liquidity in the market to execute trades efficiently. In illiquid markets, algorithms may struggle to find counterparties, leading to execution delays and unfavourable prices.
  • Price discovery challenge: It can impact price discovery in the financial market because algorithms prioritize speed and efficiency over fundamental market factors.


To conclude, algo trading has impacted the financial world with its ability to trade quickly and accurately, but not everyone can take advantage of it as it is highly technical. For someone thinking of implementing algo trading, computer programming ability is a necessity along with knowledge related to the financial world and data analysis.