Having been influenced by de Prado's Advances in Machine learning book, I've set out to build the dollar bars (in which each bar represents a set dollar amount of transactions in the security) that he endorses as a superior data structure to conventional time-based bars, mostly for its more stationary, iid, and statistically useful properties.
Unfortunately, I just don't have the tick data necessary to really put the idea to use.
I do, however, have an abundance of 1-minute data, which has me wondering the most faithful method I might use to approximate true dollar bars.
My plan is to:
- take the average of the OHLC of each minute bar,
- multiply that by the volume of that bar,
- assign that dollar value to the bar,
- and then begin aggregating the bars to the desired dollar amount from the start of the original time series to its end.
I realize, though, that this might introduce slightly over/undershooting the target dollar amount for each bar, depending on that target dollar amount per bar. Is such an approach problematic or otherwise unworthy, given de Prado's intentions for the dollar bar? Is there a better way to go about it?