Trade Overview

General
- syque
- QuantNet
Quality Tools
QuantNet Best-selling Books of 2012
Quant
Prepare
- becoming a quantitative devveloper
- becoming a quantitative analyst
More Resource
SRC
- quantlib
- algoquant

General

syque

http://syque.com/index.htm

QuantNet

https://www.quantnet.com/

Quality Tools

http://syque.com/quality_tools/tools/tools.htm

Tools for collecting data
Tools for analysing numeric data
Tools for understanding qualitative data
Tools for analysing and designing processes
Tools for understanding process performance
Tools for understanding cause and effect
Tools for identifying solutions

QuantNet Best-selling Books of 2012

https://www.quantnet.com/threads/quantnet-best-selling-books-of-2012.12190/

A Primer for the Mathematics of Financial Engineering, 2nd Ed - Dan Stefanica
Solutions, Primer For The Mathematics Of Financial Engineering, 2nd Ed - Dan Stefanica
Quant Job Interview Questions And Answers– Mark Joshi
Fifty Challenging Problems in Probability with Solutions – Frederick Mosteller
The Complete Guide to Capital Markets for Quantitative Professionals
My Life as a Quant: Reflections on Physics and Finance – Emanuel Derman
Heard on The Street: Quantitative Questions from Wall Street Job Interviews - Timothy Crack
Frequently Asked Questions in Quantitative Finance - Paul Wilmott
A Practical Guide To Quantitative Finance Interviews – Xinfeng Zhou
Liar's Poker - Michael Lewis
Cracking the Coding Interview: 150 Programming Questions and Solutions - Gayle Laakmann McDowell
How I Became a Quant: Insights from 25 of Wall Street's Elite - Richard R. Lindsey
Problem Solving with C++, 8th Edition - Walter Savitch
Options, Futures and Other Derivatives (8th Edition) - John Hull
Stochastic Calculus for Finance I: The Binomial Asset Pricing Model - Steven Shreve
Paul Wilmott on Quantitative Finance 3 Volume Set (2nd Edition) - Paul Wilmott
Stochastic Calculus for Finance II: Continuous-Time Models - Steven Shreve
Principles of Financial Engineering, 2nd Edition - Salih Neftci
C++ Design Patterns and Derivatives Pricing - Mark Joshi
C++ Primer Plus - Stephen Prata
The Big Short: Inside the Doomsday Machine - Michael Lewis
When Genius Failed: The Rise and Fall of Long-Term Capital Management - Roger Lowenstein
Starting Your Career as a Wall Street Quant - Brett Jiu
Reminiscences of a Stock Operator - Edwin Lefevre
The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It - Scott Patterson

Quant

http://www.wonabru.com/index_files/Page572.html

Prepare

becoming a quantitative devveloper¹

Programming Skills
If you're targeting bank roles, then you will likely need to be using C++, Java or C# in a Microsoft/Windows environment. If you are targeting hedge funds then you will likely be translating MatLab or R into C++ and/or Python. Funds tend to use Java and C# less, since they're often in a UNIX environment where C++ and Python make more sense.
1. Read through the entirety of Accelerated C++ by Andrew Koenig. This book will get you up to scratch on C++ syntax and, in particular, will go into detail about pointers and memory management.
2. Read through (multiple times!) the entirety of Effective C++ by Scott Meyers. I've repeated this advice consistently across the site, but it still bears saying again. This book will take you from a beginner C++ programmer to an intermediate programmer who is ready for interview.
3. Scott Meyers has also written More Effective C++ and Effective STL. You will then need to consider the Boost library, multithreaded programming and Linux operating system fundamentals to become a true expert.
Software Engineering
it is now easy to contribute to open source software projects via the internet. One of the largest quantitative finance projects is the QuantLib project. Reading through (some of) the source code to this project will give you a good idea at how large-scale C++ software projects are written.
1. Read through both Steve McConnell's Code Complete and Robert Martin's Clean Code.
2. A discussion on good software craftsmanship would not be complete without mentioning Design Patterns, also known as the "Gang Of Four" book.
3. here is a free eBook on learning Git, Pro Git, which I suggest you work your way through. It will save you hours of wasted development time!
4. Some industries are now turning towards continuous integration practices, which encourage continuous testing and deployment of code via a fully automated testing and deployment system. Although you are likely to be able to pick up most of how a CI system works "on the job", you might want to impress your interviewer by demonstrating your knowledge of the subject via working through a book such as Paul Duvall's Continuous Integration.
Database Interaction

Although quant devs are not often database administrators, if you wish to learn more about advanced MySQL optimisation, then the following two books, while certainly not necessary, are highly useful if you are running into database problems: High Performance MySQL and MySQL High Availability.
Finance and Numerical Algorithms

Of more relevance are the algorithms used in quantitative finance to carry out both instrument pricing and algorithmic trading. The investment bank derivatives pricing techniques will almost certainly concentrate on Monte Carlo Methods and Finite Difference Methods, both of which rely on knowledge of probability, statistics, numerical analysis and partial differential equations. These are all topics which a good student will be familiar with in grad school, but for those considering a career change, you will need to gain a good understanding of these methods if you wish to become an options pricing quant developer in a bank.

or hedge funds, you will likely be implementing trading infrastructure - either low or high frequency. This will involve taking an algorithm already coded up in MatLab, R or Python (or even C++) and then optimising it in a faster language, such as C++, as well as hooking up this algorithm to a prime brokerage application programming interface (API) and executing trades.

In order to read about these topics further, please have a look at my C++ Implementation articles, my Python Implementation articles and the Quantitative Finance Reading List.

becoming a quantitative analyst²

Quantitative analysts and financial engineers spend their time determining fair prices for derivative products. This involves some deep mathematical theory including probability, measure theory, stochastic calculus and partial differential equations. Thus to become a quant analyst it is necessary to have a strong mathematical background in mathematics, usually through an undergraduate degree in mathematics, physics or engineering.

The Mathematical Fundamentals
1. For those of you who are unfamiliar with financial markets or the derivative products within them, the best place to start is with John Hull's Options, Futures, and Other Derivatives.
2. Next up is Mark Joshi's The Concepts and Practice of Mathematical Finance, which is pitched at 3rd year undergraduate mathematics level.
3. The next book, by Martin Baxter and Andrew Rennie, Financial Calculus: An Introduction to Derivative Pricing, can be read concurrently.
If you really want to become an expert at the underlying mathematics, say for carrying out a top Masters in Financial Engineering (MFE) program or for beginning a PhD in Mathematical Finance, you will need to gain a deeper level of mathematical sophistication at stochastic calculus. Steven Shreve has written a two-volume set, which covers both the discrete (Stochastic Calculus for Finance I: The Binomial Asset Pricing Model) and continuous (Stochastic Calculus for Finance II: Continuous-Time Models) cases. The books are quite involved and given the limited time with which you may have to study, you may find them too deep and specific for front office quant job interviews.

If you wish to delve into more mathematical finance books take a look at the quantitative finance reading list section on mathematical finance.
Advanced Mathematical Finance
Advanced mathematical finance really comes down to learning more about stochastic calculus and risk neutral pricing. These are both extensive research areas in mathematics.
1. Mark Joshi's recent More Mathematical Finance essentially continues where his other book finishes
2. For deeper explorations of stochastic calculus, it is worth picking up Brownian Motion and Stochastic Calculus by Karatzas and Shreve as well as Stochastic Differential Equations by Oksendal.
Topics (Fixed Income/Credit)
If your research area is geared more towards particular products - specifically in the fixed income and credit spaces - then the following books will be of interest.
1. For the modelling of interest rates and term structure, Brigo's book Interest Rate Models - Theory and Practice will provide the necessary groundwork to begin reading Filipovic's Term-Structure Models.
2. If your research area is geared towards credit risk then two recommended texts are Credit Risk Pricing Models and Modelling, Pricing, and Hedging Counterparty Credit Exposure.
Numerical Methods
1. While you won't need to be absolutely familiar with every NLA algorithm, a reasonable read-through of Lloyd Trefethen's Numerical Linear Algebra will give you a solid grounding on the topic. Another seminal work is Numerical Recipes: The Art of Scientific Computing, which contains many of the algorithms used by quants today, including Monte Carlo techniques, NLA techniques and Fast Fourier Transforms. Implementing these methods (the book uses C++) will help you understand the process of scientific computing and will give you topics to discuss at interview.
2. The best place to get started with basic Monte Carlo is with Mark Joshi's C++ Design Patterns and Derivatives Pricing. Once you're happy with the quant implementations in Joshi's book, you can (optionally) gain a deeper understanding of the state of the art Monte Carlo with Paul Glasserman's Monte Carlo Methods in Financial Engineering.
3. inite Difference Methods (FDM), while popular a few years back, are not quite as important as they once were. Daniel Duffy's Financial Instrument Pricing Using C++ provides a great introduction to pricing financial derivatives with FDM and goes into extensive detail about how to use the STL within financial applications.