There is no standard way in quant finance to do this. Nevertheless you can use:
- standard ways in statistics to deal with incomplete data sets;
- specific points to take into account that you are dealing with transactions.
The standard statistical way to manage incomplete data is to use a Bayesian method: you model the usual cross-dependences between the variables and replace missing points by the max likely one (like in Inference and missing data
by Donald B. Rubin).
You can add some specific considerations like the fact that short selling is probably not allowed (except if you are working on a market where it is possible). more generally, it means that you have to compute some characteristics of what you see from the strategies, like the PnL, the risk, the time to unwind positions, etc. You should do it a sliding way (i.e. over a sliding window of few days / weeks), so that you would be able to detect some full sequences you have.
Practically, it means that you need:
- to isolate some full sequences you have
- infer the joint distribution of all your variables
- use it to fill gaps that you will have identified in your dataset