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 buy and sell and other trading tasks, like supervising market vulnerability and price movements.
Algorithm trader – Credit: algorithmtrader.net
Technical skills needed
Algorithmic trading requires skills of two kinds, code development, and domain knowledge. While coding skills demand an effective programming languages command, domain knowledge deals with stock trends in various sectors thoroughly. Indeed, coding is a highly demanding skill.
Credit: Algorithmic Tr4ding: Algorithmic Trading Encompasses Number of Skills
To emerge as a successful algorithmic trader, at a minimum, you require three core technical skills, programming, statistics, and risk management. These can be considered minimum needs. Besides, a toolkit with numerical optimization, big data analysis, and machine learning can be invaluable. Though not significant, they remain very powerful to carry out 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 make excellent insights for managing the markets for the truly dedicated pioneers. However, not consisting of a 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.
Programming
Learning to program is essential for algorithmic trading as this skill enables one to carry out useful research. You need to be familiar with C-based language syntax such as Java, which is easy to learn, C++ language. Same time, you need to further focus on the data structure fundamentals and algorithms that provide you a sound foundation. With decent hard work, you can achieve a decent level in the C++ language.
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As mentioned above that to carry out much of the research carefully, 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 that is great 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 know which is most suitable for a task.
Simulation
After learning to program, you can try to undertake a modeling task in your written simulation to test your programming capabilities in the environment of the algorithmic trading implementation. Instead, you can still try simulations developed by others before doing your simulation writing. For a modeling task, speed, accuracy, and flexibility are prime considerations. Simulation helps you with all this as further learning to develop trading strategies and actual research.
Statistics
Having good working and practical statistics knowledge is a must for any algorithmic trader to be successful. Statistics underlies everything we deal with, right from risk management to performance measurement and decision making to the development of strategies. Significantly, statistics, a vital skill 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 the best ideas for trading that particular market. You can manage risk by correlating portfolio components while regression analysis helps to test ideas on different factors influencing a market.
Credit: How To Build an Algorithmic Trading Strategy | Algo Trading
Statistics help 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 can pay you if you learn Mean-Variance Optimization (MVO), and Kelly allocation, the allocation strategies.
Take 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 the ability to do high-quality and objective research. You need also to develop practical applications, widely reading and implementing what you learned and read 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.
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Using different programming languages 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.
