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Algorithmic trading can be described as automated buy and sell transactions involving financial instruments, such as stocks, futures, and bonds. It requires an electronically networked connection, buying and selling parties, brokers, and a program for executing buying, selling, and other trading tasks like supervising market vulnerability and price movements.
Algorithm trader – Credit: algorithmtrader.net
Technical Skills Needed
Algorithmic trading requires two kinds of skills, code development and domain knowledge. While coding skills demand an effective command of programming languages, domain knowledge involves a thorough understanding of stock trends in various sectors. Indeed, coding is a highly demanding skill.
Credit: Algorithmic Tr4ding: Algorithmic Trading Encompasses Number of Skills
To become a successful algorithmic trader, you need at least three core technical skills; programming, statistics, and risk management. These can be considered the base skills. Aside from these core skills, numerical optimization, big data analysis, and machine learning can be invaluable additions to your skillset. Though not as significant, they’re still very powerful tools that can make your trading life smoother.
Algorithmic Trading Basics for New Algorithmic Traders: programming baby for algorithmic trading Credit: QuantInsti
Learning market microstructure, behavioral finance, and macroeconomics can help you make excellent insights and better understand the market. Though they are not part of the minimum requirements, economics and financial knowledge help with trading ideas.
Overview of Core Technical Skills
Follow the most suitable strategy for obtaining the listed minimum required skills as promptly as possible. Let us figure out how these skills enable you for algorithmic trading.
Learning to program is essential for algorithmic trading as this skill enables you to carry out useful research. You need to be familiar with C-based language syntaxes such as Java or C++. At the same time, you need to focus on the data structure fundamentals and algorithms that provide you a sound foundation. With hard work, you can achieve a decent level of proficiency in Java or C++.
Credit: skills software component design | Codeido
To carry out much of your research, it pays off if you can become familiar with a higher-level programming language, such as MATLAB, R, or Python. It is quite easy to learn Python, and Python is a great tool to acquire, process, and manage data immediately from diverse sources. You can use Python increasingly as a tool for research as it makes data analysis comparatively painless with some very helpful written libraries available.
Similarly, you can use R for analytics and research, as it is supported by a large repository of functions and useful libraries. R was scripted with capabilities for statistical analysis and fits naturally for the kind of work needed by algorithmic traders.
If you have an engineering background, perhaps MATLAB can be much useful for you. You can choose any of these high-level languages, but not essential to learn all. As there are many crossover capabilities in these languages, and with time you can learn which is most suitable for a task.
After learning to program, you can try to undertake a modeling task in your own written simulation to test your programming capabilities in the environment of algorithmic trading implementation. However, you can always try simulations developed by others before doing your own simulation writing. Speed, accuracy, and flexibility are prime considerations for a modeling task. Simulation actually helps you with all this as further learning to develop trading strategies and actual research.
Having a practical statistics knowledge is a must for any algorithmic trader to be successful. Statistics underlies everything we deal with in algorithmic trading, right from risk management to performance measurement and decision making to the development of strategies. Statistics is a vital skill that will become an inspiration for many of your algorithmic trading ideas. These include statistical tests, correlations, and regression analysis. To illustrate, statistical tests are used to understand an underlying process of a market at any given time that can give best ideas for trading that particular market. You can manage risk with correlating portfolio components while regression analysis helps to test ideas pertaining to different factors influencing a market.
Credit: How To Build an Algorithmic Trading Strategy | Algo Trading
Statistics helps provide insights into whether a particular action outmatches because of taking a higher risk or not. Apart from all these, the most significant statistical application in algorithmic trading is to know the interpretations of simulation results and back-testing.
Management of Risk
Risk management is an important function of an algorithmic trader. Several risk factors come across the algorithmic trading process. For example, infrastructure risk, counterparty risk, and risks at portfolio and trade levels. These risk factors are real time occurrences in the form of servers going down for various reasons, an inability of counter-party for completing a transaction, or a broker going bankrupt and taking away your trading account in the process. Risk management is a complex area where you need to know innumerable possibilities of combinations of risks. As an algorithmic trader, it pays if you learn Mean-Variance Optimization (MVO) and Kelly allocation, the allocation strategies.
Taking it Forward
It pays in the long term if you develop a habit of testing ideas by yourself rather than relying on others’ tested ideas, and if you are able to do high-quality and objective research. You need to also develop practical applications, widely reading and implementing what you learned to maximize your capabilities for success in algorithmic trading. By putting into practice the ideas you learn and read about, you can sharpen your technical skills, and develop a critical thinking mindset and creativity to succeed with algorithmic trading.
On deciding to learn any particular skill, you can assign sub-tasks within it to master systematically each one after the other. For example, in learning Python programming, you can divide this into sub-tasks comprising data types, syntax familiarity, statements/expressions/variables, loops, conditionals, functions, input/output, object oriented programming and debugging. Again, each sub-task can be made into smaller learning tasks. This approach may help you master each piece of task methodically.
Using different programming languages you learned, you could create many algorithms according to your strategy. By doing so, you can understand to deal with different strategies to tackle financial markets. However, algorithms are useless if strategies do not work. Trading strategies depend very much on high-speed programs and sophisticated mathematical formulas.
Enroll in Experfy’s course on Algorithmic Trading Strategies to learn these principles and skills and become a successful algorithmic trader.