Discussing the article: "Statistical Arbitrage Through Cointegrated Stocks (Part 9): Backtesting Portfolio Weights Updates"
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Check out the new article: Statistical Arbitrage Through Cointegrated Stocks (Part 9): Backtesting Portfolio Weights Updates.
This article describes the use of CSV files for backtesting portfolio weights updates in a mean-reversion-based strategy that uses statistical arbitrage through cointegrated stocks. It goes from feeding the database with the results of a Rolling Windows Eigenvector Comparison (RWEC) to comparing the backtest reports. In the meantime, the article details the role of each RWEC parameter and its impact in the overall backtest result, showing how the comparison of the relative drawdown can help us to further improve those parameters.
The market is in a continuous state of change. This is the mantra that is guiding us in this journey towards building a statistical arbitrage framework for the average retail trader. We stick to it by avoiding the usual notions of bear and bull markets, directional trends, or correlated assets. Instead, we use statistical methods to estimate the probabilities of pairs or groups of assets to preserve some kind of relationship over a foreseeable time horizon. For now, we are dealing with the cointegration relationship for its flexibility and almost universal applicability in financial markets. We can look for cointegration between any assets, including assets from different classes, and even between financial assets and non-financial data, like a stock symbol and the evolution of shipping costs. Once a cointegration is found, it can almost certainly be traded.
The drawback is that, from a statistical point of view, there is no guarantee that the cointegration will remain valid for the next hour, day, or week. It will always have some residual probability that it will break starting from the next tick. There is almost a one-hundred percent probability that the tickers’ prices will change.
Author: Jocimar Lopes