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Latest Articles

Kalman Filter-Based Pairs Trading Strategy In QSTrader

Previously on QuantStart we have considered the mathematical underpinnings of State Space Models and Kalman Filters, as well as the application of the pykalman library to a pair of ETFs to dynamically adjust a hedge ratio as a basis for a mean reverting trading strategy. Read more...

Quant Finance Career Skills - What Are Employers Looking For?

The world of quantitative finance continues to evolve at a rapid pace. Even in the last four years of the existence of this site the market for quant jobs has shifted significantly. In this article we outline these shifts. The advice on what is likely to be in demand in the next few years will be applicable both to those still in education as well as those thinking ahead to a career change. Read more...

Hidden Markov Models - An Introduction

A consistent challenge for quantitative traders is the frequent behaviour modification of financial markets, often abruptly, due to changing periods of government policy, regulatory environment and other macroeconomic effects. Such periods are known colloquially as "market regimes" and detecting such changes is a common, albeit difficult process undertaken by quantitative market participants. Read more...

How to Learn Advanced Mathematics Without Heading to University - Part 3

In the first and second articles in the series we looked at the courses that are taken in the first half of a four-year undergraduate mathematics degree - and how to learn these modules on your own. Read more...

Dynamic Hedge Ratio Between ETF Pairs Using the Kalman Filter

A common quant trading technique involves taking two assets that form a cointegrating relationship and utilising a mean-reverting approach to construct a trading strategy. This can be carried out by performing a linear regression between the two assets (such as a pair of ETFs) and using this to determine how much of each asset to long and short at particular thresholds. Read more...