Would you consider adding a trailing stop to each position?
Also, curious: do you worry about over fitting when you optimize parameters on the entire data set? (As opposed to in sample optimization / out of sample validation)
Would you consider adding a trailing stop to each position?
Also, curious: do you worry about over fitting when you optimize parameters on the entire data set? (As opposed to in sample optimization / out of sample validation)
Hi! Sorry for the delay in answering, too many msgs... :)
Yes, I've tried stops, but they didn't quite work: yes, they reduced the drawdowns, but they severely reduced the returns as well...
Yes, I worry about overfitting. But in this case, not that much. I'm optimizing 2 variables: the universe (150, 500, or 1000 stocks) and the rebalancing frequency (weekly, biweekly, or monthly). As shown in the article, the worst run is 22% annual return, and the best is 26%. So, they are all pretty close. Cheers!
Great read! Thanks for sharing this.
Would you consider adding a trailing stop to each position?
Also, curious: do you worry about over fitting when you optimize parameters on the entire data set? (As opposed to in sample optimization / out of sample validation)
Hi! Sorry for the delay in answering, too many msgs... :)
Yes, I've tried stops, but they didn't quite work: yes, they reduced the drawdowns, but they severely reduced the returns as well...
Yes, I worry about overfitting. But in this case, not that much. I'm optimizing 2 variables: the universe (150, 500, or 1000 stocks) and the rebalancing frequency (weekly, biweekly, or monthly). As shown in the article, the worst run is 22% annual return, and the best is 26%. So, they are all pretty close. Cheers!