@Antony, the minimal Style Risk exposure tilts are incredible! Is that the 'natural' style exposures of your factor?
@Antony, great work and should serve as the model template and analysis framework under the new guidance. Your analysis is very much on point. Reflects on your professorial style, comprehensive yet compact. I like it Thank you, Professor Antony Jackson!
Very nice, Antony. In all these, you don't use Optimize API Risk Model other than TargetWeights with no constraints, right? I like the walk forward validation method.
@Antony, Looks really good. The alpha decay has improved a lot and turnover seem symmetrical! Keep up the good work!
Turnover follows the 63 day reporting cycle, trading peaks as new reports come out and seem to hold their positions till the next reporting cycle. The minor kinks at the bottom could just be a result of stocks falling in and out of the QTU universe.
@Antony, this is as raw and pure an alpha signal as you can get. At this level, meaning as an individual signal that will be later combined with other individual signals in a signal combination schema, there is really no need to try and control style risks as these may eventually cancel out in the combination phase with other hopefully uncorrelated signals that perhaps have different common style risks exposures . However, I will try and make a case for factor smoothing. In your example above, I get that you really don't need to do factor smoothing because you chose factors that are naturally slow moving and this is manifested by the symmetrical movement of your turnover, it peaks at the 63 day reporting cycle. I believe factor smoothing may be necessary had you chosen the fast moving short term alphas that churn out 4-5 IRs but with very high turnover. There is also the right and wrong way to do smoothing. The wrong way will tend to overfit the model. But this we can leave for later discussion.
Lastly, I would like to stress that prospective authors should first change their mindsets with this new approach and guidance given by Q investment team. We are no longer designing an end to end algorithmic trading system that functions as a standalone implementation within the fund. We are now asked to generate individual signals or as Q likes to call it factors that will be later combined with others for signal combination, portfolio construction, risk management and execution, all of which will be handled by the Q investment team at the backend. So let us understand where our tasks begins and where it ends. This is akin to a production line where different processes are compartmentalized . Change is always hard because old habits die hard but it needs to be done.
Makes perfect sense to me. What I don’t quite understand is how one can get all specific Returns, and either no or negative common Returns, even though the strategy clearly has consistent exposure to several of the Risk factors. I sometimes see this in some of my strategies as well. What am I missing?
If you look at Antony's last chart (bottom right) where returns per risk exposures are enumerated, you see total common returns at slightly negative. If you add the individual returns per common style and sector risks exposures, you will arrive at the total common returns. How specific returns are arrived at is by regressing out the common returns as defined by Q. Hope this helps.
P.S. Another way to put it, say, Short Term Reversal is defined by Q as -RSI(15), your factor should not follow the returns pattern of -RSI(15) to lessen the attribution to STR.
In my opinion, this common and specific returns hoolabaloo is more of a marketing ploy invented by the industry to try and distinguish that common returns are cheap and specific returns have a price (2% fee and 20% of profits). To me a return is a return is a return, no matter how you slice it. It all depends on whether your returns survive and outperforms during different market conditions and regimes, consistency in other words. You don't hear Jim Simons and Rentec talk about specific and common returns.
During the Estimates Challenge tearsheet review Thomas mentioned that they ideally want to see portfolio positions fit a normal distribution. However, even when I weight my portfolio by a
zscore of my alpha factor, I'm not seeing the kind of distribution he was describing as ideal. Anybody have any ideas of how to accomplish the ideal distribution of portfolio weights?
Any more clues as to what you mean by dynamic optimization? What is being optimized?
@Antony Jackson I've never really used Quantopian all that much, but I am interested in learning. I saw the tutorial here: https://www.quantopian.com/tutorials/alphalens#lesson1 but was wondering if I could ask you (or others with a better understanding than myself) because I'm not quite sure how this notebook works. Can I plug in any backtest and use this to evaluate it? As in, even if the backtest has a high return, if total returns in this notebook come out as negative, is that an indication that the algorithm would likely not be profitable in real-life (in the future)? Or do I have to modify my algorithm to give correct results in this notebook? There's been a lot of conversation regarding Q's contest specifications (some saying they are too limiting and there are good algorithms that don't adhere to their specifications), but I was wondering if you could shed some light on your own thoughts. I've attached a copy of this notebook with an algorithm that returns a lot in the backtest, but comes out negative in the notebook (I think?). I guess I'm asking, what does it mean? Sorry for the newbie questions. I've seen crazy returns in backtests before (not just on here), but in real-life, they weren't worth much. So wondering if this notebook helps evaluate reality or not. It's also possible I'm totally misunderstanding the purpose of this notebook...hah! Thanks!
@Antony Jackson Ah, interesting. Thanks. The backtest I inputed trades once a month, so I would think that should be fine, but I'll try messing around with the trade time and see how that effects it.