The lists cover general quant finance, careers guides, interview prep, quant trading, mathematics, statistical analysis and programming in C++, Python and R.
One area that routinely catches out prospective quants at interview is their lack of basic financial markets knowledge.
It's all well and good being the best mathematician and programmer on the globe, but if you can't tell your stock from your bond, or your bank from your fund, you'll find it a lot harder to pass those HR screenings.
These books also make much better bedtime reading than graduate texts on stochastic calculus...
On top of needing to be aware of capital markets and how they function, the mathematics of derivatives pricing and quantitative trading methods, being able to program in C++ and possibly Python, you also need to ace that quant interview!
The following books are fantastic resources for getting you prepared. Make sure you study not only the content of the brainteasers, but also try deconstructing how they're put together and what you're really being asked.
The career paths for quants have shifted recently towards direct quantitative trading and away from derivatives pricing.
Although Black-Scholes theory is still immensely important for hedging and exotic option pricing purposes, it is now necessary to be intimately familiar with systematic trading and the firms that employ it.
It is difficult to get hold of information from funds about their trading strategies (no surprise there!), but these books provide an in-depth overview into how the "black box" operates.
Time series analysis and financial econometrics are key components of modern algorithmic trading - allowing prediction and forecasting of asset prices.
Time series analysis techniques are widely used in quantitative finance, including asset management and quant hedge funds, for forecasting purposes. Thus if you wish someday to become a skilled quantitative trader it is necessary to have an extensive knowledge of statistical time series analysis and financial econometrics.
The following books will take you from introductory time series and econometrics through to advanced multivariate time series theory at a reasonably comprehensive mathematical level:
Derivatives pricing is still a key part of the financial industry, particularly for fixed income and credit asset classes, and relies on theory developed from stochastic calculus.
Although you don't need to read every book below, they are all good. Each provides a different perspective or emphasis on options pricing theory.
If you have your heart set on becoming a derivatives pricing quant, perhaps working in equities, credit, fixed income or foreign exchange, then you should aim to study as many books from the following list as possible:
The fixed income derivatives market is the largest global derivatives market, driven largely by investor demand for specific views on interest rates or cashflow requirements.
Modelling of interest rate derivatives requires complex mathematics and necessitates a solid understanding of stochastic calculus techniques. The following texts introduce the main models:
C++ is one of the hardest areas for beginning quants to get to grips with. Since it is such a large programming language, and may in fact be a quants first taste of programming, it can be extremely daunting.
The first six books on the list, if understood properly, would make you a competent C++ programmer. By reading the remainder, you will (eventually) become an expert:
These books are designed for learning the basics and how to utilise the language effectively:
These books will cover nearly everything a practising quant will likely ever need to learn about C++ itself:
For those who wish to become the best in their peer group and/or work in high-frequency trading, you will need to know a lot more about the language, including template programming, the ins-and-outs of the STL and Linux programming:
In recent years Python has become a staple in the quantitative finance world. I personally know of many funds that employ it as the end-to-end computational infrastructure for carrying out systematic trading.
It is an easy language to learn but it is harder to master, due to the many libraries a quant will use. Regardless of which type of quant you wish to become, I would suggest learning Python, as it is only going to become more widely adopted as time goes on:
These books are designed for learning the basics and how to utilise Python - and its many scientific libraries - effectively:
These books will cover nearly everything a practising quant will likely ever need to learn about programming in Python and using its libraries - particularly with regard to data science, machine learning and quant finance:
R is an advanced statistical programming environment used widely within systematic quant funts and investment banks.
A great way to learn R is to pair the following books with an online course in statistics (which will often make use of R anyway). This will really help you get to grips with the methods of quantitative trading.
In addition numerous books have been written on various statistical topics, often using R as the implementation language:
These books are designed for learning the basics of statistics with R, as related to quantitative finance:
The following books build on the statistical theory learnt in the aforementioned texts across the fields of time series analysis and machine learning:
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