Strategies for continuously assessing and improving model performance
I'm glad to see this question because this site gets such few questions on models that are actually in a production state. 

If I was in your position, I would start to think about how I'm going to use this algorithm on a go-forward basis and start to log everything. Every new prediction that your algorithm makes is also a new data point for training. 

So you need to take the time to create a feedback loop and (1) take new datapoints, put them into training, (2) re-train your algorithm, (3) deploy a new version and then (4) use that revised algorithm. You repeat this entire process on a continual basis throughout the life of your project. From the technical side, it's an exercise in re-inforcement learning since your algorithm won't start from a base of zero knowledge. Also, most of the steps I described will most likely require you to write new code, most of which will be related to process and workflow, not necessarily data science or algorithmic. 

Finally, as an FYI, it is said that there are multiple algorithms out there that carry out these types of cycles on a continual basis, literally updating on a minute-by-minute (or faster!) basis, always providing answers on the latest available data points.