Looking at the scene of quantitative finance employment, positions of quantitative traders in huge quant funds generally are considered to be a part of one the most profitable and honoured roles. In a large quant fund, trading jobs act as stepping stones and help individuals to create and run funds by themselves. The large quant fund which is like a “parent” fund for such individuals, supplies the primary investment and also a set of people who can capitalize.
In order to pursue a good employment in quant trading, two important things are highly needed: considerable amount of hard-work and profitable use of time, as there is strong race among individuals to acquire quantitative trading roles. This article covers the small but significant steps to be taken: ways to get in the area, the necessary basic requirements, common career paths and an apt self-study strategy, which will all add to your skills as retail traders as well as professionals to be in the field of quantitative trading.
What to expect from the role?
Primarily, it is important to know what you should expect from the roles, what the position covers up. Much more than the general view of traders of investment bank and the related machismo, quantitative trading research associates to scientific hypothesis testing and academic severity. As the quantitative trading processes conducted are mostly automated in all cases, a unique input is hardly seen in there.
Generally, anyone who wants to get into the quant field should be expected to have training in scientific methodology, this as well as hypothesis testing is widely looked at as reputed in the community of quant finance. And this, almost always, not significantly however, indicates training up to the level of doctoral research, generally through a PhD course or Masters degree (in a quantitative field of course!). Yes! You can move in quantitative trading professionally using other ways, but it is highly uncommon.
There is much diversity in the skills necessary for an expert researcher of quantitative trading. The most important are probability, mathematics as well as statistical testing which work as a primary base. Areas like time series analysis, machine/statistical learning, optimisation and market microstructure need a better advanced knowledge. Also counted are programming, analysing and using academic models, backtesting, forecasting, signal generation, data cleansing, portfolio management and execution methods.
Learning this should be taken seriously. It is observed that generally it takes a minimum of 5 and maximum of 10 years to study the material that can make you lucrative and consistent at a quantitative trading within any firm. Of course, the returns are considerable. The field is highly challenging and intellectual. It does give you a range of career choices with high packages, which also indicates your own capability to create your own new fund if you have a good track record.
The background required
Usually, students look up to careers with quantitative finance as well as research along with studying (graduation or doctorate). All that I advise now is for those who want to move into a career in quant trading from some other job, although with a warning that out there, is a really competitive and challenging field.
Primarily, quantitative trading and research, professionally, needs extensive expertise in mathematics and hypothesis testing of statistical nature, along with the general ones needed like linear algebra, multivariate calculus and probability theory. Keep up and sustain you rank and mark in your graduation and the necessary subjects which will strengthen your basic chances.
Please get a degree in any of these subjects from the best schools if physics or mathematics are not present in your base, as there will be a strong fight, among the contenders, to get to the top, in the firms and it is even more struggling without a proper knowledge in the areas.
Moreover, you should be proficient at model implementation through computer programming. Normally, the usual options include R which is an open-source statistical language; Python which has wide data analysis libraries; or MatLab. Have knowledge of at least one of these if quantitative trading is your true interest.
A crucially important requirement is the ability of analysing fresh research and using it speedily. Now we know why and how PhD candidates are taken up first for trading roles, because they are taught this skill in doctoral training. So if you want a seat in a good quant fund, try having a PhD.
Introductory Quantitative Trading
The acceptance of quantitative trading now has expanded up to a retail level as well as in the quant fund area. This website has its major aspects covering the area. Given below is a link of articles, blogs and programs on the website:
To increase your understanding, try Ernie Chan’s given below works. They are an extensive account of quant trading techniques and methodologies.
- Quantitative Trading: How to Build Your Own Algorithmic Trading Business – Ernie Chan
- Algorithmic Trading: Winning Strategies and Their Rationale – Ernie Chan
Econometrics/Time Series Analysis
In quantitative trading, time series analysis has an important position. This has a major component in the form of asset price series along with some form of derivative series. Hence, time series analysis comes out as a significant and necessary area for a researcher in quantitative trading.
I advise some basic but significant works for learning econometrics and time series analysis:
- Analysis of Financial Time Series by Tsay
- Introductory Econometrics for Finance by Brooks
- Time Series Analysis by Hamilton
A very good resource for books that I found is OTexts.
Given below is a link of a brilliant book on forecasting:
- Forecasting: Principles and Practice by Hyndman and Athana¬sopou¬los
Also available on the website is an article on forecasting with method holtwinters exponential smoothing.
Intermediate Machine/Statistical Learning
Statistical learning strategies are an important reliance for the present quantitative trading. Today highly informative books exist that have brought practice and theory closer. I strongly recommend the below linked two texts to understand machine/statistical learning:
- An Introduction to Statistical Learning: with Applications in R written by James
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction written by Hastie.
The major strategies attracting include Multivariating Linear Regression, Logistic Regression, Tree-Based Techniques (involcing Random Forests), Resampling Techniques, Support Vector Machines (SVM), , Clustering (K-Means, Hierarchical), Principal Component Analysis (PCA) Kernal Techniques and Neural Networks.
Put on the website, is also a blog that talks about machine learning in trading.