Algorithmic trading explained 2022 – Beginner’s Guide

October 1, 2021
In algorithmic trading strategies, trading decisions are made based on preset rules programmed into a computer.

Algorithmic trading refers to trading strategies in which trading is fully automated to identify and execute trades.

In algorithmic trading strategies, trading decisions are made based on preset rules programmed into a computer. A trader or investor writes code that executes trades on behalf of the trader or investor when certain conditions are met.

The increased use of automated trading systems fits in with the general trend towards automation. However, algorithmic trading is more than just a more efficient way to enter orders.

The entire research and trading process can benefit from automation, computing power, and new areas like artificial intelligence.

Table of Contents

What is Algorithmic Trading

The trade as a profession requires a lot of patience, dedication and resilience. However, all of this is easier said than done.

Every trader knows that they must keep their calm and focus, but on numerous occasions, traders are influenced by emotional and psychological factors and make decisions that they later regret.

These inevitable and human errors can be prevented through the use of new technologies.

Algorithmic trading is how computer- or robot-generated algorithms are used to execute trades instead of humans (e.g., with robo-advisors ).

Business is done based on a set of predefined rules. For example, the trading instructions are programmed into the trading software in algorithms with reference to variables such as time, volume and price.

The computer, in turn, carries out the trade according to the instructions given to it. This makes algo trading exact, well-timed and free from emotions and most possible human errors.

So is algorithmic trading the future, or does it also have significant disadvantages? How can I start algorithmic trading? Securedbrokers has taken on these and many other questions and summarised them here.

Advantages and disadvantages of algorithmic trading

Advantages and disadvantages of algorithmic trading at a glance

  • Minimizing impact on the market
  • Action free of emotions
  • Act faster
  • Flawless action
  • Missing out on good trading opportunities
  • Flawed in irrational markets
  • High technological dependency
Algorithmic trading – advantages

Algorithmic trading offers several advantages over traditional trading methods. The benefits of algorithmic trading are listed below.

Minimizing impact on the market

A large order volume can potentially change the market price. Such a trade is known as a distorting trade because it distorts the market price.

To avoid such a situation, traders usually open positions that only move the market incrementally.

For example, an investor looking to buy a million shares of Apple could buy the shares in batches of 1,000 shares.

The investor can buy 1,000 shares every five minutes for an hour and then evaluate the impact of the trade on the market price of Apple shares . If the price remains unchanged, the investor continues to buy.

Such a strategy allows the investor to buy Apple stock without increasing the price. However, the strategy has two main drawbacks :

  • If the investor pays a fixed fee for every transaction they make, the strategy can generate significant transaction costs.
  • The strategy takes a long time to complete. In this case, if the investor buys 1,000 shares every five minutes, it would take a little more than 83 hours (more than three days) to complete the trade.

A trading algorithm can solve this problem by buying stocks and immediately checking to see if the purchase affects the market price. This can significantly reduce the number of transactions required to complete the trade and the time it takes to complete it.

Action free of emotions

Traders and investors are often influenced by sentiment and emotions and ignore their trading strategies. For example, in the run-up to the global financial crisis in 2008, the financial markets showed signs that a crisis was looming.

However, many investors ignored the signs because they were caught up in the “bull market frenzy” in the mid-2000s and did not believe a crisis was possible.

Algorithms solve the problem by ensuring that all trades conform to a set of rules.

This is definitely one of the most important advantages of algorithmic trading. The strategies are pre-formulated, and there is no room for traders to be influenced by their emotions. Thus, the psychological elements are eliminated from trading, and there is no room for deviating from the original strategies.

Act faster

Algorithmic trading processes the trades automatically. The algorithm reacts to the market change and generates orders as soon as the trading criteria are met.

The speed of entry and exit is extremely important to the trading process. A delay of only a few seconds can lead to high losses. A better entry and exit speed help you to capture the price movement at the right time.

Flawless action

Another major benefit of algorithmic trading is that it requires minimal human intervention.

This means that the possibility of errors drastically decreases. The algorithms are checked and cannot be influenced by human error.

For example, a trader can analyse technical indicators incorrectly, but the computer programs do not make such errors in ideal scenarios. Thus, the trades are executed with maximum accuracy.

Of course, the prerequisite for this is that the algorithms have been programmed correctly beforehand.

Algorithmic trading – disadvantages

So algorithmic trading offers enormous advantages. However, there are also crucial disadvantages that you as a retailer should be aware of.

The trading process becomes highly dependent on technology, and any mistake can lead to devastating disasters. The main disadvantages of algorithmic trading are described below.

Missing out on good trading opportunities

A trading algorithm can miss out on good trading opportunities if the market does not show any signs according to which the algorithm was programmed.

This disadvantage can be mitigated to some extent by simply increasing the number of indicators that the algorithm should look for. However, such a list can never be complete.

Flawed in irrational markets

Algorithms automated instructions, and there can always be situations and circumstances which algorithms cannot understand in the same way as the human mind does.

A trader has the ability to understand the irrational behaviour of the market and react accordingly. According to many experts, the market is acting very irrationally, especially with the current events around the coronavirus. While the US and the rest of the world are in an economic crisis, the stock markets are setting new records.

Algorithms only understand perfect, previously programmed scenarios. You are quick to lose control in irrational markets and become flawed in these unusual situations.

High technological dependency

One of the biggest drawbacks to algorithmic trading is its immense reliance on technology.

The trade orders are, in many cases, on your computer and not on the server. This means that if the internet connection is lost, the order will not be sent for execution.

However, this destroys the entire ideology of algorithmic trading. In such cases, as a trader, you are missing out on important opportunities and potentially losing a lot of money as a result. In addition, there are also major systemic problems in algorithmic trading that can lead to a major flash crash of the entire market.

If many traders trade with algorithms and discovering a downtrend, this can lead to a chain reaction.

In addition, technology alone determines success or failure in algorithmic trading. If other retailers have better and more sophisticated algorithms, they can exploit these weaknesses in their algorithms. This can result in high losses. You can imagine the whole thing a bit like a fight between artificial bits of intelligence.

How do algorithmic trading software work?

how do Algorithmic Trading work

Algorithmic trading systems act according to predetermined rules.  The traders and programmers set specific rules for trade entry and exit beforehand.

These specific rules must be programmed into the software. The software is then usually linked to a random access broker. All specific rules must be written in the proprietary language of that platform.

This software automatically executes the trade through a computer once the predetermined conditions are met.

The conditions under which the software trades can be very simple. For example, that a long position trade should be entered as soon as the 50-day moving average on a five-minute chart of certain security exceeds the 200-day moving average.

But they can also be complicated strategies that require a thorough understanding of the programming language specific to the user’s trading platform. The more complicated the algorithms should be, the more urgently a qualified programmer with a high level of specialist knowledge is needed.

The leading provider for algorithmic trading can be found here, especially the trading of cryptocurrencies is highlighted here:

Algorithmic Trading – Strategies

As mentioned earlier, a very simple algorithmic trading system can be based on just one or two indicators.

At the other end of the spectrum are large funds and companies with incredibly complicated algorithms, which use an interplay of artificial intelligence and big data to identify the best opportunities that can give them an edge.

See the following section for examples of algorithmic trading strategies. Starting with the simplest up towards very complex trading systems. The common denominator of all strategies is that they can be converted into an algorithm based on a rule system.

Trend following strategies

The most common algorithmic trading strategies follow trends in moving averages, channel breakouts, price level movements and other technical indicators.

These are the simplest strategies that can be implemented for algorithmic trading. Trades are initiated based on the appearance of desirable trends. This can be implemented in a straightforward manner using algorithms.

For example, using 50 and 200-day moving averages is a popular trend following strategy.

Arbitrage strategies

Arbitrage strategies can be used when the same security is traded on different exchanges at different prices. A double-listed share is bought at a lower price in one market and at the same time sold at a higher price in another market.

The implementation of an algorithm to identify such price differences and the efficient placing of orders create profitable opportunities.

Sometimes when a company is listed in different countries, an arbitrage deal also involves currency trading.

Automated trading is especially good for arbitrage because complex calculations can be performed only briefly to utilise existing opportunities.


Realignment of the index fund

Index funds have defined periods of realignment to align their holdings with their respective reference indices. This creates profitable opportunities for algorithmic traders who benefit from expected trades.

Mathematical model-based strategies

Proven mathematical models such as the delta-neutral trading strategy enable trading with a combination of options and the underlying security.

Delta neutral is a portfolio strategy consisting of several positions, with positive and negative deltas balanced.

Trading range (mean reversion)

The mean reversion strategy is based on the concept that high and low-security prices are temporary and regularly return to their mean (average) value.

By identifying and defining a price range and implementing an algorithm, trades can be automatically placed when security leaves its defined range. Usually, these values ​​are based on oscillators or volatility bands.

Volume Weighted Average Price (VWAP)

The volume-weighted average price strategy breaks a large order into small parts and releases these dynamically determined smaller parts to the market using stock-specific historical volume profiles. The aim is to execute the order close to the volume-weighted average price (VWAP). Institutional traders widely use this strategy to execute large orders.

Time Weighted Average Price (TWAP)

The time-weighted average price strategy breaks a large order into small parts and releases these dynamically determined smaller parts of the order to the market using evenly distributed time windows between the start and end times. The aim is to execute the order between the start and end time close to the average price. Thus, it minimises the impact on the market. This strategy is also widely used by institutional traders to execute large deals.


Volume Percentage (POV)

Algorithmic Trading volumne Percentage

Until the trading order is fully executed, this algorithm sends partial orders according to the defined participation quota and according to the volume traded on the markets.

The associated “ step strategy ” sends orders with a user-defined percentage of market volume and increases or decreases that participation rate when the stock price reaches user-defined levels.

Quantitative investment strategies

This algorithm uses a combination of company value, growth, dividend yield or momentum to select stocks to buy or sell. However, this strategy is not necessarily automated.

Special trading algorithms

There are some special classes of algorithms that try to identify special ” events “. These “ sniffing algorithms ”, which are used, for example, by a market maker on the sales side, are programmed to identify algorithms on the buy-side of a large order.

Such recognition of the algorithms helps the market maker identify large order opportunities and benefit from executing the orders at a higher price. This is sometimes referred to as high tech front running.


Algo Trading Conclusion: Our experience with algorithmic trading

Algorithmic Trading conclusion

The trading algorithm has become an indispensable part of modern trading. Large portfolio managers and investment funds, in particular, are increasingly using algorithms.

However, there are also risks and disadvantages. This becomes particularly clear through the “sniffing algorithms”. In addition, some algorithms are only designed to exploit weaknesses in other algorithms.

With ever-increasing amounts of data and new technologies, it can be assumed that trading in algorithms will increase rather than decrease in the future. Therefore, elementary algorithms can be particularly interesting for private investors who want to remove their emotions from trading securities.

The advantages of algorithm trading at a glance

  • Minimising impact on the market
  • Action free of emotions
  • Act faster
  • Flawless action

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Institutional investors mainly use algorithmic trading to reduce the costs associated with trading. In addition, research has shown that algorithmic trading is particularly beneficial for large order sizes, accounting for up to 10% of the total trading volume.

Yes, investing based on algorithmic decisions is serious. There are already many robo advisors and trading robots that outperform the market year after year. Here, however, you have to pay attention to the seriousness of the provider.

The most common algorithmic trading strategies follow trends in moving averages, channel breakouts, price level movements and related technical indicators. For example, using 50 and 200-day moving averages is a popular trend following strategy.

Algorithmic trading uses computer programs that act according to preset criteria. A straightforward example is that an algorithm automatically buys or sells security when it reaches or falls below a specific price.

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