After finishing the video, the thing that immediately come to my mind is to apply this method on the parameter selection of a single strategy. Like doctor said in the beginning, walk forward rolling window method doesn't consider the current market infomation into the decision, but only the past performance of the strategy/portfolio itself, which might also be suffered from a lot of noise overfit issue or the plateaus area is hard to identify......etc I wonder if I change to use this kind of machine learning way to dynamically change the parameter while backtesting, maybe those strategies that I already throwed away could revive. Gread content!
So ml model takes in ... market regime features + trading strategy parameter(if any)+ allocation weights ... spits out sharpe ratio i could easily see this overfit and not having predictive power. If this worked, we would optimize parameter for single strategy. Rebalance parameter each month. But this is no different than walk forward optimization... Also... regimes can change before the weights are rebalanced.
man he did leave a lot to the imagination but this idea is so insane with AI actually having a neaural'ish network. I wonder if their approach is bruteforcing different set of conditions across asset classes till they find out the conditions that actually affect the markets and in what ratio, and then improve the current model. Coz thats fckin epic
I'm not very familiar with Quant Finance, but I thought the implicitly defined regimes makes sense. Sorta like HMM but with kinda very large number of hidden states
I initially felt the same way, however I believe the key area to pay attention to is 20:37 where he talks about the features used. If you dig a little deeper into time-series features this explains how the regimes are modeled IMO.
After finishing the video, the thing that immediately come to my mind is to apply this method on the parameter selection of a single strategy. Like doctor said in the beginning, walk forward rolling window method doesn't consider the current market infomation into the decision, but only the past performance of the strategy/portfolio itself, which might also be suffered from a lot of noise overfit issue or the plateaus area is hard to identify......etc
I wonder if I change to use this kind of machine learning way to dynamically change the parameter while backtesting, maybe those strategies that I already throwed away could revive.
Gread content!
So ml model takes in
... market regime features + trading strategy parameter(if any)+ allocation weights
... spits out sharpe ratio
i could easily see this overfit and not having predictive power.
If this worked, we would optimize parameter for single strategy. Rebalance parameter each month. But this is no different than walk forward optimization...
Also... regimes can change before the weights are rebalanced.
w
man he did leave a lot to the imagination but this idea is so insane with AI actually having a neaural'ish network. I wonder if their approach is bruteforcing different set of conditions across asset classes till they find out the conditions that actually affect the markets and in what ratio, and then improve the current model. Coz thats fckin epic
silllllllllly
Hghh
that's a terrible presentation. He did not give any definition of regime, nor did he describe a method how to measure regimes. A lot of talking though
I'm not very familiar with Quant Finance, but I thought the implicitly defined regimes makes sense. Sorta like HMM but with kinda very large number of hidden states
I initially felt the same way, however I believe the key area to pay attention to is 20:37 where he talks about the features used. If you dig a little deeper into time-series features this explains how the regimes are modeled IMO.