Difference Between Algorithmic Trading, Quantitative Trading,Automated Trading And High-Frequency Trading
Algorithmic Trading – Algorithmic trading means turning a trading idea into an algorithmic trading strategy via an algorithm. The algorithmic trading strategy thus created can be backtested with historical data to check whether it will give good returns in real markets. The algorithmic trading strategy can be executed either manually or in an automated way.
Quantitative Trading – Quantitative trading involves using advanced mathematical and statistical models for creating and executing an algorithmic trading strategy.
Automated Trading – Automated trading means completely automating the order generation, submission, and the order execution process.
HFT (High-Frequency) Trading – Trading strategies can be categorized as low-frequency, medium-frequency and high-frequency strategies as per the holding time of the trades. High-frequency strategies are algorithmic strategies which get executed in an automated way in quick time, usually on a sub-second time scale. Such strategies hold their trade positions for a very short time and try to make wafer-thin profits per trade, executing millions of trades every day.
An important point to note here is that automated trading does not mean it is free from human intervention. Automated trading has caused the focus of human intervention to shift from the process of trading to a more behind-the-scenes role, which involves devising newer alpha-seeking strategies on a regular basis.
In the past, entry into algorithmic trading firms used to be restricted to PhDs in Physics, Mathematics or Engineering Sciences, who could build sophisticated quant models for trading. However, in recent years there has been an explosive growth of the online education industry, offering comprehensive algorithmic trading programs to wannabe algorithmic traders. This has made it possible to get into this domain without having to go through the long (8-10 years) academic route.
Steps To Becoming An Algo Trading Professional
In the sections below, we outline the core areas that any aspiring algorithmic trader ought to focus on to learn algorithmic trading. We also present our readers with a comprehensive picture of the different ways and means through which these essential skill sets can be acquired.
Step 1: Core Areas Of Algorithmic Trading
Algorithmic trading is a multi-disciplinary field which requires knowledge in three domains, namely,
- Quantitative Analysis/Modeling
- Programming Skills
- Trading/Financial Markets Knowledge
If you are a trader who is used to trade using fundamental and technical analysis, you would need to shift gears to start thinking quantitatively. Working on statistics, time-series analysis, statistical packages such as Matlab, R should be your favourite activities. Exploring historical data from exchanges and designing new algorithmic trading strategiesshould excite you. Problem-solving skills are highly valued by recruiters across trading firms.
A professional Coder/Developer in a trading firm is expected to have a good fundamental knowledge of financial markets such as types of trading instruments (stocks, options, currencies etc.), types of strategies (Trend Following, Mean Reversal etc.), arbitrage opportunities, options pricing models and risk management. This knowledge will be crucial when you interact with the quants and will help in creating robust programs.
View some popular algo strategies here -> Algorithmic Trading Strategies, Paradigms and Modelling Ideas
The strategies created by the quants are implemented in the live markets by the Programmers. If you want to excel in the technology-driven domain of automated trading, you should be willing to learn new skills and you shouldn’t be disinclined to any field. So if you have never printed “hello world” by compiling your own coding program, it’s time to download the compiler of your interest – C++/Java/Python/Ruby and start doing it! The best way to learn to program is to practice, practice and practice. Sound knowledge of programming languages like Python/C++/Java/R is a pre-requisite for a Quant Developer job in trading firms. You can read a couple of our popular blog posts on Programming below:
- Why Python Algorithmic Trading is the Preferred Choice among Traders
- Popular Python trading platforms for Algorithmic Trading
Step 2: Ways To Become An Algo Trading Professional
Getting started with books
Algorithmic trading books are a great resource to learn algo trading. You will find many good books written on different algorithmic trading topics by some well-known authors. As an example, to hone your knowledge of derivatives, the “Options, Futures, and Derivatives” book authored by John C. Hull is considered a very good read for beginners. For algorithmic trading, one can read the “Algorithmic Trading: Winning Strategies and Their Rationale” book by Dr. Ernest Chan.
Find a list of good reads here → Essential Books on Algorithmic Trading
In addition to the algorithmic trading books, beginners can follow various blogs on algorithmic trading; watch YouTube videos, catch trading podcasts (e.g. Chat with Traders), attend online webinars (list of webinars hosted by QuantInsti), or get registered on platforms like Quantiacs and Quantopian to learn to code. One can also register for the free courses that are available on various online learning portals like Coursera, Udemy, Udacity, edX, & Open Intro.
Although these free resources are a good starting point, one should note that some of these have their own shortcomings. For example, algorithmic trading books do not give you a hands-on experience in trading. Free courseson online portals can be subject specific and may offer very limited knowledge to serious learners. Another important point to note is the lack of interaction with experienced market practitioners when you opt for some of these free courses.
Learn from Professionals/Experts/Market Practitioners
The building blocks in learning Algorithmic trading are Statistics, Derivatives, Matlab/R, and Programming languages like Python. It becomes necessary to learn from the experiences of market practitioners, which you can do only by implementing strategies practically alongside them. You can join any organization as a trainee or intern to get familiarized with their work ethics and market best practices. If it’s not possible for you to join any such organization then you can opt for classroom courses/workshops or paid online courses. Most of the classroom courses/workshops are delivered in the form of 2 days to 2 weeks long workshops or as a part of Financial Engineering degree programs. On the online front, there are online learning portals such as QuantInsti, Coursera, Udemy, Udacity, edX, & Open Intro, they have expert faculty from mathematics and computer science backgrounds who share their experiences and strategy ideas/tactics with you during the course.
Step 3: Get Placed, Learn More And Implement On The Job
Once you get placed in an algorithmic trading firm, you are expected to apply and implement your algorithmic trading knowledge in real markets for your firm. As a new recruit, you are also expected to have knowledge of other processes as well, which are part of your workflow chain.
As an example, firms which trade low latency strategies will usually have their platform built on C++, whereas in trading firms where latency is not a critical parameter, trading platforms can be based on a programming language like Python. Thus, it becomes essential for wannabe and new Quant Developers to have an understanding of both the worlds.
New recruits working on specific projects may be given a brief training to get a good grasp on the subject. Trading firms usually make their new recruits spend time on different desks (e.g. Quant Desk, Programming, Risk Management Desk) which give them a fair understanding of the work process followed in the organization. To put it in subtle words, learning in the algorithmic world never stops!!