A columnar database or No-SQL solution may be your best choice. 

It depends on which OS you target, what your throughput and latency requirements are and whether you look to persist all data or not and finally how big the size of your data is expected to be. Obviously if you only look to store hourly/daily data then even a database that comprises a year of all options data of the SPX500 underlyings may fit into memory and if that is the case you should definitely look at RedisDB. It can persist/snapshot data but generally loads them back into memory. 

If the size of your data is a constraint to fitting it into memory then another solution such as RavenDB (well written .Net library), or other non-.net solution, depending on requirements, such as Mongo or Couch db may fit a lot better. Please add more requirements and I am happy to edit my post and add information, given I believe I can add value.

**Edit:**

According to your updated information I cannot recommend Redis highly enough: Not only are there libraries for pretty much any programming language and OS imaginable (I use it in my .Net framework with the BookSleeve API). You also get great support in R. You can dump literally time series with millions of elements into it, have it stay in memory (you can also persist it) but you can incredibly fast access the data out of R. I do not know a faster way to access time series data out of R to be honest plus additional indexing packages will give you a great accessor library. If you look for something fast, efficient and look to profile ideas/data, which hints at R usage then RedisDB Is what you want.