[Rd] Design for classes with database connection

[Simon Zehnder]

Paul,

thank you very much for this elaborate answer. There are a lot of points which make me rethink what I was planning. Especially I will take a close look on the TSdbi package/source code. Probably this is something I can rely on - that would be very great. 

To conclude very shortly what my idea was: Use R to read the data in and give it a kind of standardised structure, which is then written to SQLite. Use SQLite to store the data and to filter/order it. Use R to get parts of the data and to estimate models on it. The models get an S4 class and have everything they need. 

By huge I think about Tickdata from e.g. Future markets for a year: For DAX Futures this could be around 28.5 Mio. Entries (each entry with timestamp, IDs, price, volume, etc). Another example would be Spot market data for a whole Index (say the Dow30). Usually we get the data from the wrds in a batched form like a csv with all entries for all securities batched. Further order book data with each order send to the exchange for one security for three months - altogether often more than 20 Mio entries (including deleted errors etc.). 

From now on I will move this conversation to R-SIG_DB, as suggested in your last comment.


Best

Simon



On Sep 20, 2013, at 12:27 AM, Paul Gilbert <pgilbert902 at gmail.com> wrote:

> Simon
> 
> Your idea to use SQLite and the nature of some of the sorting and extracting you are suggesting makes me wonder why you are thinking of R data structures as the home for the data storage. I would be inclined to put the data in an SQL database as the prime repository, then extract parts you want with SQL queries and bring them into R for analysis and graphics. If the full data set is large, and the parts you want to analyze in R at any one time are relatively small, then this will be much faster. After all, SQL is primarily for databases, whereas R's strength is more in statistics and graphics.
> 
> In the project http://tsdbi.r-forge.r-project.org/ I have code that does some of the things you probably want. There the focus is on a single identifier for a series, and various observation frequencies are supported. Tick data is supported (as time stamped data) but not extensively tested as I do not work with tick data much. There is a function TSquery, currently in TSdbi on CRAN but very shortly being split with the SQL specific parts of the interface into a package TSsql. It is very much like the queries you seem to have in mind, but I have not used it with tick data. It is used to generate a time series by formulating a query to a database with several possible sorting fields, very much like you describe, and then order the data according to the time index.
> 
> If your data set is large, then you need to think carefully about which fields you index. You certainly do not want to be building the indexes on the fly, as you would need to do if you dump all the data out of R into an SQL db just to do a sort. If the data set is small then indexing does not matter too much. Also, for a small data set there will be much less advantage of keeping the data in an SQL db rather than in R. You do need to be a bit more specific about what "huge" means. (Tick data for 5 days or 20 years? A 100 IDs or 10 million?) Large for an R structure is not necessarily large for an SQL db. With more specifics I might be able to give more suggestions.
> 
> (R-SIG-DB may be a better forum for this discussion.)
> 
> HTH,
> Paul
> 
> On 13-09-18 01:06 PM, Simon Zehnder wrote:
>> Dear R-Devels,
>> 
>> I am designing right now a package intended to simplify the handling
>> of market microstructure data (tick data, order data, etc). As these
>> data is most times pretty huge and needs to be reordered quite often
>> (e.g. if several security data is batched together or if only a
>> certain time range should be considered) - the package needs to
>> handle this.
>> 
>> Before I start, I would like to mention some facts which made me
>> decide to construct an own package instead of using e.g. the packages
>> bigmemory, highfrequency, zoo or xts: AFAIK big memory does not
>> provide the opportunity to handle data with different types
>> (timestamp, string and numerics) and their appropriate sorting, for
>> this task databases offer better tools. Package highfrequency is
>> designed to work specifically with a certain data structure and the
>> data in market microstructure has much greater versatility. Packages
>> zoo and xts offer a lot of versatility but do not offer the data
>> sorting ability needed for such big data.
>> 
>> I would like to get some feedback in regard to my decision and in
>> regard to the short design overview following.
>> 
>> My design idea is now:
>> 
>> 1. Base the package on S4 classes, with one class that handles
>> data-reading from external sources, structuring and reordering.
>> Structuring is done in regard to specific data variables, i.e.
>> security ID, company ID, timestamp, price, volume (not all have to be
>> provided, but some surely exist on market microstructure data). The
>> less important variables are considered as a slot @other and are only
>> ordered in regard to the other variables. Something like this:
>> 
>> .mmstruct <- setClass('mmstruct', representation( name	=
>> "character", index	= "array", N		= "integer", K		= "integer", compiD
>> = "array", secID  	= "array", tradetime	= "POSIXlt", flag		=
>> "array", price	= "array", vol		= "array", other	= "data.frame"))
>> 
>> 2. To enable a lightweight ordering function, the class should
>> basically create an SQLite database on construction and delete it if
>> 'rm()' is called. Throughout its life an object holds the database
>> path and can execute queries on the database tables. By this, I can
>> use the table sorting of SQLite (e.g. by constructing an index for
>> each important variable). I assume this is faster and more efficient
>> than programming something on my own - why reinventing the wheel? For
>> this I would use VIRTUAL classes like:
>> 
>> .mmstructBASE	<- setClass('mmstructBASE', representation( dbName		=
>> "character", dbTable		= "character"))
>> 
>> .mmstructDB		<- setClass('mmstructDB', representation( conn		=
>> "SQLiteConnection"), contains		= c("mmstructBASE"))
>> 
>> .mmstruct <- setClass('mmstruct', representation( name	=
>> "character", index	= "array", N		= "integer", K		= "integer", compiD
>> = "array", secID  	= "array", tradetime	= "POSIXlt", price	=
>> "array", vol		= "array", other	= "data.frame"), contains =
>> c("mmstructDB"))
>> 
>> The slots in the mistrust class hold then a view (e.g. only the
>> head()) of the data or can be used to hold retrieved data from the
>> underlying database.
>> 
>> 3. The workflow would than be something like: 	a) User reads in the
>> data from an external source and gets a data.frame from it. b) This
>> data.frame then can be used to construct an mmstruct object from it
>> by formatting the variables and read them into the SQLite database
>> constructed. c) Given the data structure in the database, the user
>> can sort the data by secID, timestamp etc. and can use several
>> algorithms for cleaning the data (package-specific not in the
>> database) d) Example: The user makes a query to get only price from
>> entries compID = "AA" with tradetime < "2012-03-09" or with trade
>> time only first trading day in a month. This can then be converted
>> e.g. to a 'ts' object in R by coercing e) In addition the user can
>> perform several estimations of market microstructure models by
>> calling package-specific functions.
>> 
>> 
>> Is there a big fault in my design, something I haven't considered? I
>> am very sure on this list are researchers and developers with much
>> more experience. I would like to hear your opinion and ideas. I learn
>> from it and can maybe get to a design which I can then implement for
>> the research on such data and models.
>> 
>> 
>> Best
>> 
>> Simon
>> 
>> 
>> 
>> 
>> ______________________________________________ R-devel at r-project.org
>> mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
>>