In this article I will implement and backtest a strategy based on the paper ‘Statistical Arbitrage in the U.S. Equities Market’ (Avellaneda and Lee, 2008). The paper describes the application of Ornstein-Uhlenbeck process for modelling the cointegration residual (spread) in statistical arbitrage strategies.  Statistical arbitrage is basically a generalized version of pairs trading, where instead of…

In this article I will implement and test a trading strategy inspired by the paper ‘Data mining for algorithmic asset management: an ensemble learning approach’ (Montana, Parella, 2009). The strategy presented in the paper is not really a pairs trading strategy. Instead it uses a synthetic asset (generated from several cross-sectional data streams) to determine if…

I believe that the main drawback of all the pair selection methods we tested so far is the assumption that market conditions don’t change during the whole trading period. We analyzed pricing data and assessed which pairs are suitable for trading only once in the beginning of the period. We then assumed that the relationship between…

In the previous article we discovered that although cointegration method provides us with a lot more potentially tradable pairs, our methods for selecting best pairs for trading do not work as expected. Most of the chosen pairs diverge too much during the trading period. In this post I would like to test several machine learning…

In two previous articles we talked about distance methods of pair selection. When we created a portfolio of two stocks we assumed equal capital allocation to each stock in the pair. This probably limited the amount of potential pairs that were selected for further analysis. Even if two stocks are influenced by the same risk factors,…