Quantopian's risk model allows you to disentangle alpha (specific returns) from risk (common returns). This is done by defining a set of market effects which are known risk factors, and then seeing how the returns of a model/strategy can be explained by those risk factors. Whatever left over is returns specific to that model/strategy, AKA alpha. We'll be releasing a lecture on this soon. For more info now see our risk model page: https://www.quantopian.com/risk-model
In this example we’ll show an algorithm which was built to have high exposure to a known market risk, short term reversal. Short term reversal is a specific form of mean reversion that bets on short term deviations in price reverting to the mean; and uses price data exclusively. It is considered common risk due to its widespread knowledge and use, so it would be considered to have any real alpha. All the performance, positive or negative, obtained from investing in mean reversion would be considered common risk attributed.
Mean reversion more generally is just the notion of modeling a quantity such that bets can be placed on deviations reverting. You can do mean reversion on alternative data, mean reversion on sentiment, mean reversion on specific phenomena. The main issue here is the very simplistic form of mean reversion that short term reversal represents.
Also, keep in mind that managed and intentional risk exposure can be okay, in so far that there is a clear explanation for it and it's additive to the strategy. If you just have random exposure to a factor, that's not good. If you have consistent exposure to a factor that's also not good. If your exposure turns on and off over time depending on intelligent decisions, the portfolio is well diversified, and the on-off toggling actually nets you positive returns, that may be acceptable depending on context. A case we likely don't want is some timing strategy on a single risk factor, but an algorithm that takes on some risk that changes over time can be okay.
We’ll show a performance attribution breakdown for the strategy to give you a sense of how to use it on your own.
For more info, see our lecture on controlling risk exposure during portfolio optimization.
Note: This post has been edited for clarity.