With Russell 3000 you may find lots of stocks which would not be shortable. A larger cap universe (Russell 1000 for example) would make it more likely for shorts to be possible. IB provides stock margin and short borrow on their FTP in these links:
With Russell 3000 you may find lots of stocks which would not be shortable. A larger cap universe (Russell 1000 for example) would make it more likely for shorts to be possible. IB provides stock margin and short borrow on their FTP in these links:
Maybe you can try balancing out the longs and shorts over the same industry or sector clusters. Like 2 long/shorts in oil & gas, 2 in tech, etc.. Clusters can be identified with PCA & DBscan or other methods. Etc.... In my rough initial research this increases the Sharpe ratio.
I'm researching a stat arb strategy also and your post gave me some ideas. Thanks for that. All the best!
Very nice strategy and clear analysis.
With Russell 3000 you may find lots of stocks which would not be shortable. A larger cap universe (Russell 1000 for example) would make it more likely for shorts to be possible. IB provides stock margin and short borrow on their FTP in these links:
Python
import pandas as pd
StockMargin=pd.read_csv(‘ftp://shortstock:%20@ftp3.interactivebrokers.com/stockmargin_final_dtls.IBLLC-US.dat’,delimiter='|’,skiprows=1)
ShortBorrow=pd.read_csv(‘ftp://shortstock:%20@ftp3.interactivebrokers.com/usa.txt’,delimiter='|’,skiprows=1)
Maybe you can try balancing out the longs and shorts over the same industry or sector clusters. Like 2 long/shorts in oil & gas, 2 in tech, etc.. Clusters can be identified with PCA & DBscan or other methods. Etc.... In my rough initial research this increases the Sharpe ratio.
I'm researching a stat arb strategy also and your post gave me some ideas. Thanks for that. All the best!
Thanks, my friend! Great points! Looking forward to reading your study! Cheers
I like the idea of finding the clusters at the sub-industry level, this would make it more like a pair-trading strategy.