The investment team no longer runs stand-alone funded algorithms 'as is'. Instead signals are combined using proprietary ensemble methods, risk management and execution algorithms. There are regulatory requirements that potentially limit position sizes in individual stocks. These all combine to make the fund, in its current form, a mid-frequency fund.
With that in mind, here is my interpretation of the results contained in the second notebook above for my two-factor estimates strategy.
1. Alpha Decay
The first chart exhibits the Information Ratio (IR) as far as fourteen trading days out. This is the annualized ratio of expected returns to standard deviation of returns. The chart shows two pieces of information --- specific and total returns. For a model to score highly in terms of uniqueness, the returns should be dominated by specific returns, i.e., those not explained by the Quantopian risk model. This will be characterized by the blue and green bars being at a similar height throughout.
The first chart seems to be pretty good for my strategy. There is some alpha decay, but it is slow and gradual. The absolute level of the IR seems OK.
The box-plot exposure chart does a great job of comparing the strategy's returns against the risk model. For my model, the sector exposures are centered around zero, and while there are some persistent style exposures, they are smaller than 10%. The two largest ones, momentum and short-term reversal make sense in terms of my economic hypothesis, so I'm not too worried about them.
3. Holdings / Turnover
This is a critical chart. The IR can be decomposed into the forecasting strength of a model and its breadth. More independent bets are good, and my strategy is placing bets on approximately 1800 stocks over the 7-year sample period. The mean turnover for my model is 10%, so I don't appear to be placing very short-term bets.
The five lines in this chart should not overlap. The safest way to achieve this is to use the optimizer with TargetWeights and without constraints.