Quantocracy’s Daily Wrap for 07/06/2016

This is a quick update post to let readers know that the pre-order release of Advanced Algorithmic Trading has had a new update, adding over 50 pages of material. This brings the current release up to 250 pages. To access the new content, customers simply need to follow the download link received in the original purchase email. If the download email has been misplaced then please email

In this final leg of The Checklist tour we'll be looking at the Dynamic Allocation step and touch briefly on ex-post Performance Analysis. Dynamic Allocation Essentially this involves repeating the previous 9-steps on a periodic basis (e.g. a sequence of monthly allocations) according to a chosen allocation policy. Examples of dynamic allocations include systematic strategies (based on

Algorithmic trading is a field thats generally quite daunting to beginners, forcing them to juggle learning advanced programming techniques and market mechanics. Throughout the process theres usually not a lot of guidance, and even less coding examples. Our goal is to demystify this process and take you from beginner to quant with a hands-on lesson. Well program our own technical

Introduction A blog series to contrast the key distinctions between trend following and countertrend strategies during building, testing and trading. In this post we examine the effects of data integrity and simulated trade sample size on backtested performance. Price Data Integrity One of the major obstacles for traders looking to research trend following models is data. Since trend following

Brexit might have been globally thought of as bad news for the markets, but it was good for trend following. It marked a quick up movement in last months performance, quickly reversing the negative performance of the month to turn it back positive, after the 23rd June vote. The YTD performance is slightly back in positive territory. Below is the full State of Trend Following report as of last