Hi there,
I'm about to start a project I have been thinking about for a long
while. I basically wish to analyse stock market data. I already have the
data in a table and I'm now in the process of writing my own indicators and
oscillators. I hope to learn while re-inventing this wheel and perhaps
explore different things unavailable in most stocks analysis software I
found.
I would love to do an sql query that would look like the following ones.
I wonder if they are possible and valid applications for SQL and what would
be the proper implementation for these. I know I can calculate all this
using C, but it would be most useful (to my later projects) to do it in SQL
directly (my last example gives you an idea where I'm going).
select max(opening_price, closing_price, high_price) - min(opening_price,
closing_price, low_price) as day_range...
select closing_price, moving_average(20,closing_price),
exp_mov_avg(20,closing_price)...
select closing_price, moving_average( funky_oscillator( closing_price ) )...
I think creating a module and using virtual tables may be an idea... but
doing the above selects would involve some serious twists I think. What
I'd need are virtual functions or something like that...
A DBMS is a good way to keep your raw data. But I highly doubt that a
majority of your analysis algorithms are going to be expressible in SQL
without going way beyond the intended purpose of the language. You will
either find yourself limiting the analyses to what is convenient to
express in SQL, or you will spend much more time writing queries than
you would spend describing your data processing in a form more suited to
functions. SQL is primarily a language for extracting sets from other
sets, according to defined criteria (which include set relationships).
Your analyses, unless they are like nothing I've ever seen or imagined,
are going to be derived from functions on time series rather than sets,
per se. I expect you would find a signal processing library, such as
can be found in Matlab, Octave, or Scilab, to be a much better start
than what you might write in SQL in reasonable time.
That said, it is not hard to imagine that selection of datasets might be
done with SQL, and perhaps some query criteria might include measures
derived from your own custom functions on time series.
Can you guys confirm with me whether I'm asking too much and I should
concentrate on a C or C++ implementation of my functions, or is there a way
to make it happen in sqlite?
There is no real either/or choice posed here. You can create your own
custom functions to be incorporated into SQLite queries, including
aggregate functions. The aggregate functions might produce some output
other than what is returned to SQLite. What I think will be
unreasonable or unduly limiting is making it happen in bare SQLite. You
will need more.
Thanks,
Simon
Have fun!
--
Larry Brasfield
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