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    <p>You might be relieved to know that there is already a python library that is optimized for rolling financial calculations... it is called <a href="http://pandas.pydata.org/" rel="nofollow noreferrer"><code>pandas</code></a>.</p> <p>I don't think <a href="https://stackoverflow.com/questions/10065051/python-pandas-and-databases-like-mysql">pandas will read from SQL yet</a>; however, <a href="http://pandas.pydata.org/" rel="nofollow noreferrer"><code>pandas</code></a> will read from a csv... I took the liberty of using csv data (which it seems you have stored in <code>ma.csv</code>)... once you have done that, getting a rolling 7-day average on your CLOSE is as simple as...</p> <pre><code>&gt;&gt;&gt; import pandas as pn &gt;&gt;&gt; from datetime import date &gt;&gt;&gt; df = pn.read_csv('fut.csv', index_col=2, parse_dates=[2]) &gt;&gt;&gt; pn.rolling_mean(df['CLOSE'], window=7) yyyymmdd 2005-03-29 NaN 2005-03-30 NaN 2005-03-31 NaN 2005-04-01 NaN 2005-04-04 NaN 2005-04-05 NaN 2005-04-06 0.121429 2005-04-07 0.120429 2005-04-08 0.119429 2005-04-11 0.118571 2005-04-12 0.117857 2005-04-13 0.117429 2005-04-14 0.117000 2005-04-15 0.116571 2005-04-18 0.116714 2005-04-19 0.117286 2005-04-20 0.117571 2005-04-21 0.117857 2005-04-22 0.118143 2005-04-25 0.118429 2005-04-26 0.118714 &gt;&gt;&gt; &gt;&gt;&gt; pn.rolling_mean(df['CLOSE'], window=7)[date(2005,4,26)] 0.11871428571428572 &gt;&gt;&gt; </code></pre> <p><code>df</code> above is a <a href="http://pandas.pydata.org/" rel="nofollow noreferrer"><code>pandas</code></a> <a href="http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe" rel="nofollow noreferrer"><code>DataFrame</code></a>, which is a specialized structure for holding tables of time-indexed values associated with an object... in this case, the <code>DataFrame</code> holds your HIGH, LOW, CLOSE, etc...</p> <p>Besides making your job much easier, <a href="http://pandas.pydata.org/" rel="nofollow noreferrer"><code>pandas</code></a> also offloads most of the heaving lifting to Cython, which makes running thousands of these calculations rather fast.</p> <p><hr></p> <h2>fut.csv</h2> <pre><code>SYMBOL,DESCRIPTION,yyyymmdd,OPEN,HIGH,LOW,CLOSE,tmp1,tmp2,tmp3,tmp4 AC-057,Ethanol CBOT (Pit) Liq Cont,20050329,0.121,0.123,0.121,0.123,47,233,32,219 AC-057,Ethanol CBOT (Pit) Liq Cont,20050330,0.124,0.124,0.122,0.122,68,233,0,219 AC-057,Ethanol CBOT (Pit) Liq Cont,20050331,0.123,0.123,0.123,0.123,68,246,57,226 AC-057,Ethanol CBOT (Pit) Liq Cont,20050401,0.122,0.122,0.122,0.122,5,241,5,221 AC-057,Ethanol CBOT (Pit) Liq Cont,20050404,0.12,0.12,0.12,0.12,1,240,0,220 AC-057,Ethanol CBOT (Pit) Liq Cont,20050405,0.12,0.12,0.12,0.12,5,241,0,220 AC-057,Ethanol CBOT (Pit) Liq Cont,20050406,0.12,0.12,0.12,0.12,4,241,2,220 AC-057,Ethanol CBOT (Pit) Liq Cont,20050407,0.119,0.119,0.116,0.116,30,233,23,209 AC-057,Ethanol CBOT (Pit) Liq Cont,20050408,0.115,0.115,0.115,0.115,35,217,34,194 AC-057,Ethanol CBOT (Pit) Liq Cont,20050411,0.117,0.117,0.117,0.117,5,217,0,194 AC-057,Ethanol CBOT (Pit) Liq Cont,20050412,0.117,0.117,0.117,0.117,5,217,2,194 AC-057,Ethanol CBOT (Pit) Liq Cont,20050413,0.117,0.117,0.117,0.117,9,217,0,194 AC-057,Ethanol CBOT (Pit) Liq Cont,20050414,0.117,0.117,0.117,0.117,9,217,0,194 AC-057,Ethanol CBOT (Pit) Liq Cont,20050415,0.117,0.117,0.117,0.117,9,218,4,190 AC-057,Ethanol CBOT (Pit) Liq Cont,20050418,0.117,0.117,0.117,0.117,5,218,0,190 AC-057,Ethanol CBOT (Pit) Liq Cont,20050419,0.119,0.119,0.119,0.119,5,218,5,190 AC-057,Ethanol CBOT (Pit) Liq Cont,20050420,0.119,0.119,0.119,0.119,0,218,0,190 AC-057,Ethanol CBOT (Pit) Liq Cont,20050421,0.119,0.119,0.119,0.119,5,218,0,190 AC-057,Ethanol CBOT (Pit) Liq Cont,20050422,0.119,0.119,0.119,0.119,5,223,0,190 AC-057,Ethanol CBOT (Pit) Liq Cont,20050425,0.119,0.119,0.119,0.119,0,223,0,190 AC-057,Ethanol CBOT (Pit) Liq Cont,20050426,0.119,0.119,0.119,0.119,0,223,0,190 </code></pre>
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    1. POSelect 7, 14, 20, 50, 200 day prices from OHLC data in SQL database.
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    1. USMike Pennington
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    1. COThat's actually really helpful. I didn't realize these other math libraries existed. Maybe you could add some input as to other libraries and perhaps add a few comments about the SQL code? There's some set logic involved here that I'd really like to get a solution to.
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    2. COI guess I'm not sure what else I can say about your SQL other than you might consider using [hdf5](http://www.hdfgroup.org/HDF5/) since it's more optimized for seeks on a range of dates when compared to SQL. As for the set logic, perhaps it's better to ask another [so] question because we try to scope questions to a single topic.
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