Computers play a major role in today’s world. We can give many examples for it, one of the most important segments that has been transformed by the advent of computers is the stock market. The advent of computers in the capital markets has led to the online trading of stocks and the digitization of market data. This also paved the way for the emergence of more advanced systems. Intelligent systems that can analyze the market and determine its risks. algorithmic trading or algo Trading is one of these systems.
Algorithmic trading is an intelligent or semi-intelligent system that examines different markets to find investment opportunities. This system, after finding these opportunities, can trade and make a profit. In this article, we want to discuss algorithmic trading and the skills needed to enter this field.
What is Algorithmic Trading
Algorithmic trading is the use of automated algorithms to analyze the market, find business opportunities, and trade. To do this, we need a network with trading systems and brokers and computer programs for buying and selling as well as performing other business operations such as controlling prices and market conditions.
An Example Of Algorithmic Trading
In order to trade with algorithms, we use a computer program connected to the trading system (directly or through a broker). This computer trades for us. Such a program or algorithm is merely a set of comprehensible commands for a computer. A simple example of this system could include the following commands:
- Read Company A stock price data.
- Calculate their mean and standard deviation.
- If the latest price is more than the average and the standard deviation is less than a threshold value, send the order to buy N new shares.
This is a simple example. Trading algorithms can be much more complex; But with this example we can make some useful points. The simple algorithm described here has much in common with all trading algorithms:
1- Receiving data
Algorithmic Trading requires a data acquisition program, which includes price readings. It should be noted that the program itself can be very complex. This program must be connected to the market database. We may also need to use more sophisticated programs to monitor the market simultaneously.
2- Analysis and calculation
At least one tool is needed to analyze the data, which in this example is averaging and calculating the standard deviation, is also required in Algorithmic Trading.
3- Checking the conditions
The conditions are obtained with the tools of analysis and calculation and if they are correct, they will lead to a decision to buy or sell. In this example, the condition is that if the price is higher than the average and the standard deviation is less than a threshold value, a purchase order will be sent.
4- Execution of the transaction order
Execution of a trading order can also be another complex part. This section requires methods of communication with the market or brokerage. Communication management and transaction tracking are also among the tasks performed in this section.
Other common components of trading algorithms can include the following sections:
1- Risk management tools
For example, calculations of stock size and volume, amount of capital and tools to examine the performance and behavior of the system are risk management tools in Algorithmic Trading.
2- Portfolio management tools
Portfolio management tools are partly related to the above and are used to review and manage the number and volume of shares in the portfolio and analyze them.
3- Data control and storage tools
Obviously, to work with this amount of data, we need tools to control the data, read it quickly when needed, and store new information.
4- Analysis and follow-up after the transaction
Making a deal is just one part of the job. To have a picture of the future of the market, capital, increase the accuracy of forecasting and correct and improve the tools, it is necessary to follow them after the transaction.
Types of Algorithmic Trading
Algorithmic trading is very extensive; But the most popular type of algorithmic trading is algorithms that look for opportunities to buy cheap and sell expensive. This is a signal-based system. In this broad classification, various sub-categories for algorithmic trading have been created. Although joint naming has not yet been established in this area, this type of algorithm can be categorized as follows.
Technical analysis is a set of analyzes that uses price and volume patterns to predict future market changes. According to him, these analyzes are based on the assumption that there are repetitive patterns in price changes.
Technical analysis tools include a set of price and volume indicators such as the Relative Strength Index (RSI) and the Moving Average Convergence Divergence (MACD).
There are markers such as resistance and support levels and arrangements such as flag arrangement, triangular flag and patterns such as head and shoulder pattern. Even a bunch of candlestick patterns such as the Engulfing Bear pattern that can indicate the future direction of the market are used in technical analysis.
Quantitative Trading, like technical analysis, has many meanings for different people. For some traders, trading is a slightly different form of technical analysis. Quantitative trades are based on several mathematical and statistical models of market behavior.
There are many models for predicting market behavior. However, finding a model that is accurate enough and can generate profit is not an easy task. Sometimes technical analysis tools are also used in quantitative trading.
One of the most popular models in this field is cross-sectional momentum strategy. This model emphasizes buying winners and selling losers. Another popular model, such as mean-reversion strategy, emphasizes the sale of winners and the purchase of losers.
One of the most important subsets of quantitative trading is machine learning and artificial intelligence. Machine learning and artificial intelligence have attracted a lot of attention today.
By learning machine learning and feeding it with market data and statistical models, we can gain a good insight into the market. These tools are also being used to help make business decisions and improve the performance of complex systems.
By definition, Hight Frequency Trading (HFT) refers to trades that occur in one millionth of a second. No human can trade fast without the help of a computer. In these trades, receiving a particular signal can lead to buying or selling.
Although fast trades are based entirely on signals received; But speed and delay in the operation are more important than the signal itself. This requires that the code executing these transactions be as fast and efficient as possible. Such code can be written in low-level languages such as C ++.
Why Is Algorithmic Trading Important ?
Algorithmic Trading and automation have long been used in the enterprise trading environment; But later it found a place in other markets as well. In 2006, 40% of orders in the Bruce London market were made with algorithmic trading systems. This number reached 60% in 2007 and more than 80% in 2010.
Why Is There Such A Tendency Towards Algorithmic trading?
The most important reasons for this are the focus on human limitations. for example:
- Computers can process huge amounts of data in the blink of an eye. This ability makes them a powerful tool for simultaneously scanning hundreds of markets and finding trading opportunities; But a human trader can not handle this amount of work.
- Computers do not face the problems that humans face. Problems such as computational errors, the time it takes to enter trades into a system, and errors due to fat fingers can lead to incorrect keystrokes.
- Computers have no emotional attachment to the transaction or the market. Only one issue is important to them. Whether there is a business opportunity or not.
- Computers work continuously and continuously; But humans can only work several hours a day. Humans get tired and need social life.
However, none of these differences is a strong reason to trust an algorithm completely and allow it to operate without human supervision.
What do we need to do to succeed in Algorithmic Trading?
If we want to create and implement a algorithmic trading system, we need different sets of programming, statistics and market skills.
Knowing the theoretical foundations is important for learning any of the topics. Therefore, it is better to learn these concepts in the first step. In order to become a professional in algorithmic trading or algorithmic trading, you should definitely use theoretical models in practice. Practical application of what you learn is the most important part of the case. Those who want to build their own trading system need the following skills.
Here are the minimum technical skills you need to succeed in modeling in the long run:
- risk management
Other skills can be added to this list; But these skills go a little beyond what we call “necessary minimums.”
If you do not know how to program, it is better to start today. To be able to do serious work in Algorithmic Trading, you definitely need to know programming. Programming can enable you to analyze and search markets.
Forget clicks, right-click, windows and icons. You can not succeed in algorithmic trading without coding. The more you program, the more you will enjoy it.
It is best to be familiar with C-based grammars such as C ++ and Java; But at the same time try to master the basics, data structure and algorithms. Here are the programming skills you need for template trading:
Python is a simple and powerful language and provides many tools and libraries for data processing. These libraries and tools make data analysis very easy.
The R programming language is built for statistical work and analysis of large statistical data. Therefore, there are many large, powerful and free libraries for data analysis and statistical work in this language. These tools can be used well in Algorithmic Trading.
Without good knowledge of statistics, you can not succeed in Algorithmic Trading. Statistics are present in all trading operations. From risk calculation to strategy development and decision making, statistics are always present and important. With knowledge of statistics, you will find that this knowledge inspires you with many new ideas in Algorithmic Trading. for example:
- Statistical tests can provide insight into the processes behind market flows. With this insight, you can make decisions about the market and come up with new ideas.
- Knowing the degree of correlation of portfolio components is useful for risk management.
- Data regression can provide insight into how specific ideas and approaches work.
In addition, the most important application of statistics in algorithmic trading is related to the interpretation and testing of models and simulation results.
3- Risk management
To apply Algorithmic Trading, many risks must be considered. For example, infrastructure risks such as a server crash, a power outage, or a temporary internet connection. There are risks associated with the parties to the transaction, such as the risk that your party will not be able to complete the transaction on time or correctly, or the risk of the broker going bankrupt.
To apply Algorithmic Trading, many risks must be considered.
While all of these risks are serious and worth considering, we want to focus on portfolio risk management and trading. In this type of risk management, we try to find the probability of loss and adjust the strategy and portfolio accordingly. Knowledge of calculation and risk management can optimize the design and implementation of the algorithmic trading system.
What are the problems of Algorithmic Trading?
Despite the advantages of using Algorithmic Trading, like any other invention, it has its drawbacks. Some of these problems can be categorized as follows:
- Algorithm trading requires skills that you must either learn yourself or refer to others who have such skills. Obviously programming knowledge is required for algorithmic trading ; But it will be useful for you to be aware of market structure, computer hardware, software and networking.
- Tools like machine learning used today in algorithmic trading are efficient and very powerful, but you need special knowledge to use them. Using Algorithmic Trading is really difficult and requires skills in several areas.
- system, you need infrastructure such as uninterruptible power supply and a permanent internet connection. Today, these infrastructures are easily accessible through cheap servers and cloud services.
- Algorithmic trading runs on computer hardware; What happens if the algotriding server crashes or shuts down for any reason?
- To use algorithmic trading, we run one or more computer programs at the same time. We may not be able to control it constantly due to its complexity, lack of complete knowledge of its details or even fatigue.