We study revenue optimization learning algorithms for repeated posted-price auctions where a seller interacts with a single strategic buyer that holds a fixed private valuation for a good and seeks to maximize his cumulative discounted surplus. We propose a novel algorithm that never decreases offered prices and has a tight strategic regret bound of $\Theta(\log\log T)$. This result closes the open research question on the existence of a no-regret horizon-independent weakly consistent pricing. We also show that the property of non-decreasing prices is nearly necessary for a weakly consistent algorithm to be a no-regret one.