Algorithmic trading involves using computers to buy and sell assets automatically. It is also referred to as automated trading, algo trading, or black-box trading. The program follows a set of instructions — an algorithm — designed to buy and sell at specific times to make a profit.
Basically, the algorithm is a set of instructions that must be executed in a specific order. The instructions are based on the variables (inputs) that you have programmed into the algorithm. Input variables include prices, volume, time, economic data, or indicator values — any type of variance can be used. Once these conditions are met, a buy or sell order is placed.
Let’s start with the basics of algorithmic trading.
How does algorithmic trading work?
Algorithmic trading requires a trading strategy implemented into an executable program. The most crucial step in algorithmic trading is to have a proven profitable trading idea. You must have an idea and a strategy before creating a trading algorithm. Once the algorithm is created, the next step is to test it against historical price data to determine if the algorithm will be profitable. However, finding an advantage in the market and coding it into a profitable strategy is no easy task.
When you find a good edge in the market, you need to be competent and skilled at trading. The best algorithmic traders are competent and experienced in trading and finance, quantitative analysis or modeling, and programming. There are also already developed web or desktop services with a user-friendly interface that will eliminate the need for programming skills.
Choosing a strategy is the most important component of a trader’s success in the market. So, before trading, analyze what trading strategy described below suits you best. The program’s action plan is integrated into the algorithm during the development of the trading robot.
Features of algorithmic trading in different markets
Algorithmic trading on the Forex market is carried out with the help of trading robots. Automation of data analysis and trade selection helps to save time and minimize transaction costs. Forex trading robots are used not only by retail traders but also by banks. The latter automate the updating of prices for currency pairs. Furthermore, algorithms are also used for speculative trading when you need to react quickly to the smallest changes in the trading space.
In the stock market, other strategies are used:
- Strategies based on technical analysis. In this case, the trading strategy is based on searching for patterns and inefficiencies using analytical tools;
- Front-running algorithms track large volumes by monitoring information in the order book, order tapes, and charts. Based on the collected data, the system evaluates where the stock price will move and how the price will behave in a similar situation is repeated;
- Pair trading, which implies the selection of two instruments, is selected, one of which is leading, and the second may be underestimated.
- Market-Making strategies. Market makers need to buy securities and hold them in their accounts so that individual traders or hedge funds can buy them in the future. Players that adhere to this strategy aim to provide liquidity for trading instruments.
- Volatility trading. To predict price volatility for certain assets, you need to use analytical tools. Such strategies are usually given by professional investors who incorporate their knowledge into the algorithms of trading robots.
Algo trading is actively developing nowadays in the crypto industry. The use of trading robots allows you to reduce the risks of trading digital assets, which are characterized by high volatility.
Choosing the direction of trading
Algo trading can be divided into quantitative and high-frequency trading. The followers of the first option strive to develop algorithms that provide the most accurate forecasts. Among quantitative traders, many people know programming, mathematics, and economics. The strategy of these investors is based on the construction of mathematical models that identify undervalued or overvalued assets in order to use them profitably.
The activities of quantitative players aim to develop an effective strategy for managing financial instruments that are not dependent on the fluctuations of the market.
High-frequency algorithmic trading (HTF) is the most popular variant of algorithmic trading. It involves the conclusion of a large number of transactions with different instruments. In this process, only fractions of a second can pass from the opening to the closing of a position. In HFT trading, robots have one important advantage over humans — speed. A small profit from individual transactions, in this case, is compensated by their large number.
Benefits of algorithmic trading
Automating processes allows you to minimize human intervention, and algorithmic trading is no exception. Some benefits of automating the trading process include the following.
- Convenience
Traders who have little free time but want to trade constantly can use algorithms to set up minimal human intervention. Once the system is configured, it will only execute trades when an opportunity arises according to the trading settings. This also means that you can trade around the clock, even if you are fast asleep. Algorithmic trading eliminates the need to monitor the market around the clock and spend time manually entering orders.
- Without emotions
If you have ever heard the term panic selling, you know that human emotions can affect trading. Stress and greed can negatively impact the decision-making process and lead to reckless selling in a down market. Algorithmic trading allows you to eliminate emotions and minimizes deviations from the original trading plan.
Since algorithmic trading allows you to execute trades automatically, decision-making time is reduced to zero. Once the predetermined conditions are met, the trades will be executed regardless of your emotions at that moment. Eliminating the emotional aspect of trading improves trading discipline even in volatile market conditions, preventing irrational decisions.
- Speed and accuracy
Algorithmic trading allows you to reduce the influence of the human factor when performing analysis or executing orders. By automating order entry and, to some extent, market analysis, traders can use algorithms to read information from multiple indicators and execute trades quickly. This enables trading at a frequency that human traders cannot physically handle. Computerized algorithmic trading software can respond to changes in the market much faster than humans. It also eliminates human error, such as accidentally entering the wrong price or amount. In a field where market conditions are unstable, the speed and accuracy of trading can give you additional advantages.
Disadvantages of algorithmic trading
You should keep in mind that although algorithmic also has some disadvantages.
- Algorithmic trading software errors
With so many different types of trading strategies based on algorithms, it is reasonable to assume that sooner or later, you will encounter errors in your work. Moreover, trading algorithms may have a short lifespan or work only in certain market conditions, the change of which may harm the success of transactions. Since the algorithms are developed, tuned, and tested by human traders taking into account certain market conditions and based on certain data, this software may not work under real conditions. The algorithms are programmed to execute trades only under predetermined conditions and cannot change trading strategies on demand.
- Regular monitoring required
Due to the possibility of the errors and failures mentioned above, algorithmic trading requires regular monitoring to ensure that trades are executed correctly. Also, the more complex and algorithmic the trading strategy is, the more likely it is to be over-optimized. This is when an algorithm’s performance is degraded by incorporating more parameters to improve its sensitivity or accuracy. Since algorithmic trading software can also execute unprofitable trades, traders need to constantly monitor the progress of the trade even if they do not manually analyze or execute trades themselves.
- Not suitable for beginners
Even though algorithmic trading is an automated process, you must still decide on a strategy, purchase reliable software, and monitor the execution of trades. Therefore, it requires a certain level of knowledge about trading and cryptocurrencies. Algorithmic trading often focuses on technical indicators, so a beginner who does not know technical analysis will have difficulty monitoring how the software works.
Main algorithmic trading strategies
If you want to start with algorithmic trading, you should first familiarize yourself with the two strategies commonly used in algorithmic trading: moving average-based and RSI-based.
- Using Moving Averages in algorithmic trading
Moving average is an indicator commonly used in the Golden Cross or Death Cross trading strategy. In this pattern, two moving averages (MA) are used. MA are lines on the chart that show the average price of an asset over some time. The strategy is to watch how the 50 MA (average price over the last 50 days) and 200 MA (average price over the last 200 days) lines cross on long-term time frames, such as daily or weekly. When two moving averages cross, it creates a golden cross/convergence (when the 50 MA crosses the 200 MA from bottom to top) and a death cross/divergence (when the 50 MA crosses the 200 MA from top to bottom).
The convergence signals a short-term momentum that is gaining ground against a long-term one. Some traders interpret this as a buy signal. Conversely, a divergence signals the predominance of a long-term momentum over a short-term one, which is interpreted as a sell signal.
Using RSI in algorithmic trading
In short, the Relative Strength Index, or RSI, is a technical indicator that measures the strength of a trend. It’s based on the average number of gains and losses over 14 days. The RSI can tell if an asset is overbought or oversold. When an asset is overbought, it’s due for a correction, i.e., a trend reversal.
Some traders use the RSI to determine the point of a trend reversal. The RSI line usually moves in the same direction as price. A divergence between the price direction and the RSI line signals an impending price reversal.
The RSI line moves in a range from 0 to 100, and the range from 30 to 70 is most commonly used as a baseline. When the indicator line rises above 70, the asset is considered overbought. When the RSI value is below 30, the asset is considered oversold. The best time frame to look for divergences is usually the 4-hour or daily window, as stronger movements are seen in these time frames over the medium to long term.
Conclusion
Automated trading strategies are a popular way to generate passive income and reduce risk in volatile markets when trading for the long term. Algo trading makes markets more liquid and trading more systematic by eliminating the influence of human emotions on trading activities.
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