the set $\mathbb{B}$. Our newly austere mode of hindsight optimization yields a rock-bottom option price and correspondingly better guarantees of relative performance at the end of the planning period, whose shortened length is now well within a human life span. Say, for robust betting on the S&P 500 index, the author himself is inclined to use $\mathbb{B}:=\{0,0.5,1,1.5,2\}$, which amounts to the following five (continuously-rebalanced) asset allocations:

1.  $0\%$ stocks, $100\%$ cash

2.  $50\%$ stocks, $50\%$ cash

3.  $100\%$ stocks, $0\%$ cash

4.  $150\%$ stocks, $-50\%$ cash (margin loans)

5.  $200\%$ stocks, $-100\%$ cash (margin loans)

In this example, the author would like to avoid paying the full *Cost of Achieving the Best \[Rebalancing Rule\] in Hindsight* that would correspond (Garivaltis 2018) to $\mathbb{B}:=\mathbb{R}$ or even $\mathbb{B}:=[0,2]$.

The paper is organized as follows. Section \[notation\] explains our basic notation and terminology. Section \[two\] develops our main techniques in the context of hindsight optimization over a pair $b>c$ of rebalancing rules and a single underlying risk asset. We price and replicate both the horizon-$T$ and perpetual versions of the rebalancing option, and give performance simulations that illustrate the general behavior of the replicating strategy. Section \[several\] extends the methodology to general discrete 