This is a summary of links featured on Quantocracy on Monday, 10/03/2016. To see our most recent links, visit the Quant Mashup. Read on readers!
-
Hidden Markov Models for Regime Detection using R [Quant Start]In the previous article in the series Hidden Markov Models were introduced. They were discussed in the context of the broader class of Markov Models. They were motivated by the need for quantitative traders to have the ability to detect market regimes in order to adjust how their quant strategies are managed. In particular it was mentioned that "various regimes lead to adjustments of asset
-
Index Front Running: What Happens When a Stock is Added to an Index? [Signal Plot]This post documents some of my research on index front running. This trading strategy is simply buying stocks before they are added to indexes that passively managed funds are designed to track. I initially came across this idea through a Bloomberg article, The Hugely Profitable, Wholly Legal Way to Game the Stock Market. The article made it seem like this is easy money, so I decided to do some
-
The Perils of Backtesting with Unrealistic Data [Allocate Smartly]As readers hear us repeat often, our results tend to be less optimistic than youll find elsewhere. We do our best to show backtested results that are as realistic as possible (even though showing results that are as good as possible would probably be better for business). Thats partially a result of simple things, like accounting for transaction costs of 0.1% per trade (or roughly $10 on a
-
A shock to the covariance system [Flirting with Models]Mean-variance optimization assumes that you can fully describe the risks and returns of assets in a few simple numbers. Extreme market events often cause volatilities and correlations to spike dramatically, but stress testing on an individual asset basis can allow our own biases and oversights to creep into the process. By decomposing the risk structure into independent sources of risks and
-
Implementing Python in Interactive Brokers C++ API [Quant Insti]In the previous article on IBPy Tutorial to implement Python in Interactive Brokers API, I talked about Interactive Brokers, its API and implementing Python codes using IBPy. In this article, I will be talking about implementing python in IBs C++ API using a wrapper, written by Dr. Hui Liu. About Dr. Hui Liu Dr. Hui Liu is the founder of Running River Investment LLC, which is a private hedge
-
Better To Buy Strength or Weakness? [System Trader Success]Emotionally its a lot easier to buy on strength than to buy on weakness. Buying into a falling market feels unnatural. Your instincts warn that price may continue to fall resulting in lost capital. On the other hand buying when the market makes new highs feels more natural. Price is moving in your direction and the sky is the limit! However, with so many other aspects of trading what feels