  The dynamic portfolio optimization problem in finance frequently requires
learning policies that adhere to various constraints, driven by investor
preferences and risk. We motivate this problem of finding an allocation policy
within a sequential decision making framework and study the effects of: (a)
using data collected under previously employed policies, which may be
sub-optimal and constraint-violating, and (b) imposing desired constraints
while computing near-optimal policies with this data. Our framework relies on
solving a minimax objective, where one player evaluates policies via off-policy
estimators, and the opponent uses an online learning strategy to control
constraint violations. We extensively investigate various choices for
off-policy estimation and their corresponding optimization sub-routines, and
quantify their impact on computing constraint-aware allocation policies. Our
study shows promising results for constructing such policies when back-tested
on historical equities data, under various regimes of operation, dimensionality
and constraints.
