Great article, once again. Thanks so much for that. I went through the paper. One thing that is not clear is why the scaling of (threshold signal)_t is done by dividing by 1.5%. The other question is in the formula for threshold signal, what is the frequency of 't'. Is it daily? Monthly? Basically, weight changes as SP500 and 10Y treasury futures produce return everyday. Weight on day zero is 60 and 40. When we compute the new weight daily, we need to know what day is zero so we can compute the new weight on any given day (wrt to that day zero). But this day zero will keep rolling. Do we mark day zero as the beginning of every month for a weight computation during that month?
Thanks, Jatin! Yeah, the 1.5% is an arbitrary hyperparameter chosen to blend the 2 signals. When you play with it, you see how that shifts the weights between the Calendar and the Threshold signals, yielding different equity curves/risk profiles.
The frequency of t is daily. On day zero, w is 60%. From day 1 onwards, use the formula to compute the new w
Thanks. And I assume this day zero resets automatically to a new date whenever the threshold exceeds delta and we rebalance and reset the weights back to 60/40 again, right? We then start re-computing the subsequent weights from that new day zero?
Yeah… it’s in the formula. For Threshold signal, whenever w drifts over a delta, w is reset to 60%. For Calendar signal, reset is only done on the last day of the month
Thanks a lot. I’ll try to model it the way you described. Will let you know if I run into a wall and need to clarify. Hope it’s okay to reach out to you again. Would really appreciate that.
Another way to ask the same question is: In section 5 of the paper, the author says "On average, this investor buys equities and sells bonds after bonds have relatively outperformed and buys bonds while selling equities after equities have outperformed.".
What I am asking is what the lookback period of this outperformance is?
Great post as always! You mentioned an overlay with a mean reversion strategy—is that the “first principle” one using the QPI indicator? I’m curious about your experience with it after a year.
I was able to reproduce the long side fairly well, but are you also trading the short side? And if so, does it perform similarly to your backtest? I haven’t been able to replicate the short side at all, which I find quite puzzling.
Hi Carlos,
Great article, once again. Thanks so much for that. I went through the paper. One thing that is not clear is why the scaling of (threshold signal)_t is done by dividing by 1.5%. The other question is in the formula for threshold signal, what is the frequency of 't'. Is it daily? Monthly? Basically, weight changes as SP500 and 10Y treasury futures produce return everyday. Weight on day zero is 60 and 40. When we compute the new weight daily, we need to know what day is zero so we can compute the new weight on any given day (wrt to that day zero). But this day zero will keep rolling. Do we mark day zero as the beginning of every month for a weight computation during that month?
Thanks, Jatin! Yeah, the 1.5% is an arbitrary hyperparameter chosen to blend the 2 signals. When you play with it, you see how that shifts the weights between the Calendar and the Threshold signals, yielding different equity curves/risk profiles.
The frequency of t is daily. On day zero, w is 60%. From day 1 onwards, use the formula to compute the new w
Thanks. And I assume this day zero resets automatically to a new date whenever the threshold exceeds delta and we rebalance and reset the weights back to 60/40 again, right? We then start re-computing the subsequent weights from that new day zero?
Yeah… it’s in the formula. For Threshold signal, whenever w drifts over a delta, w is reset to 60%. For Calendar signal, reset is only done on the last day of the month
Thanks a lot. I’ll try to model it the way you described. Will let you know if I run into a wall and need to clarify. Hope it’s okay to reach out to you again. Would really appreciate that.
Sure, glad to help :)
Another way to ask the same question is: In section 5 of the paper, the author says "On average, this investor buys equities and sells bonds after bonds have relatively outperformed and buys bonds while selling equities after equities have outperformed.".
What I am asking is what the lookback period of this outperformance is?
As someone who formerly got ideas from WSB, the "improved" strategy is giving up $2 million over just mean reversion! * insert diamond hands emoji *
Hello,
Great post as always! You mentioned an overlay with a mean reversion strategy—is that the “first principle” one using the QPI indicator? I’m curious about your experience with it after a year.
I was able to reproduce the long side fairly well, but are you also trading the short side? And if so, does it perform similarly to your backtest? I haven’t been able to replicate the short side at all, which I find quite puzzling.