I'm afraid the query will probably not produce the results you expect. The reason is, that the first .es() query is split on the ExchangeName field while the second is not. I don't see a way to correctly use split.plus() at the same time here.
Instead I would suggest to create an index pattern with a scripted field that calculates the average for each document using (doc['BidPrice'].value + doc['AskPrice'].value) / 2 and visualize that in the Visualize app using the average of the scripted field as a metric aggregation (y-axis) and a date histogram and a terms filter as the bucket aggregations (x-axis).
If you're using the most recent version of Kibana you might also have success with the Time Series Visual Builder, which allows for some ad-hoc processing of the data as well.
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