Quantocracy’s Daily Wrap for 04/28/2015

Many consider smart beta to be a revolution in the asset management industry. For example, Bloomberg ran an article, Funds Run by Robots Now Accounts for $400 Billion, which caught our attention. According to this article, Smart beta, is one of the fastest growing segments of ETFs, accounting for nearly 20% of all assets in domestic ETFs as of the end of 2014. The secret sauce of

First: the takeaways Expected return for an asset class is a combination of carry and price appreciation If carry is high enough, it can potentially dwarf price depreciation due to rising rates Higher carry assets (e.g. high yield bonds, MLPs, REITs, bank loans, EM debt, et cetera) have historically had low-to-negative estimated duration profiles High carry assets have a variety of associated risk

When developing trading strategies, most systematic traders understand that if you search long enough, you're bound to find something that looks great in back-testing. The question is whether those great results are from a great trading strategy or because the best looking strategy was the one that benefited the most from good luck. A well-known metaphor for this is a roomful of monkeys

A while ago I ran a series of posts on how you would write some python code to systematically trade using the interactive brokers C++ API. Whilst I hope this was helpful it was just a starting point. There are at least two major projects to undertake before one could actually trade. The first is the design of such a system. This is the subject of a book I am writing, which I hope will be published

In this post we will look at the backtest results for dynamic exits of the 66 days-to-expiration (DTE) Iron Condor (IC) options strategy, with 16 delta short strikes, with different profit and loss exits. This is a non-directional options trading strategy that seeks to profit from a market that stays within a range between the two short strikes of the Iron Condor. For some background on how the re