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		<title>Vertical sum spreadsheet version 2 plan</title>
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		<description>hypersoniq's Blog: Vertical sum spreadsheet version 2 plan</description>
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			<title>Original Blog Entry: Vertical sum spreadsheet version 2 plan</title>
			<link>/blogentry/197541</link>
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			<pubDate>Tue, 03 Mar 2026 15:02:05 GMT</pubDate>
			<dc:creator>hypersoniq</dc:creator>
			<description><![CDATA[<p>Since the previous 2 systems are focused on a per column approach (because the positional digits are independent), I will stick with a per column approach here as well. That means eliminating the horizontal sum portion, which will simplify the spreadsheet.<br /><br />The end result will be a cascading sheet, where the groups of columns get shorter as the calculations move across.<br /><br />The first columns will hold the draw history, the next columns will start ten rows down and sum the 10 rows that make up the first k sample. The next set will make up the N sample size, so they will start 30 rows below the k samples and provide both an average and a mode.<br /><br />There should be a way to run a basic back test right in the sheet once the data is populated for the average and mode of 30 k samples. That would be to take the average and mode (separately) and subtract the last 9 in the k sample and see if it is equal to the next digit. In this way, if the initial k and N parameters are inconclusive, they can be changed.<br /><br />It is a great deal of tedium creating such a sheet, but that will serve as the base of coding a function in Python, as the commands to get such data in a pandas data frame are much faster.<br /><br />So we will see if there is any merit to such a system by counting hits on the back test. That will decide whether it is going to include a mode or an average (or both) when it moves to Python.<br /><br />This is still a single approach on 3 independent histories, so there will remain that synchronization issue. Hopefully the specific profile information for each gathered from the other 2 functions might be used together at some point to determine the state of each position.<br /><br />I could make the single script prototype open to settings where k and N are different for each position...<br /><br />At some point I may need to record the output of all of these functions and feed it into a machine learning algo or 2 to find stuff I am currently not seeing.<br /><br />The sheet is built, with one tab for mid day pick 5 and one sheet for evening pick 5... so far the sums(k) are done as is both the rounded average and mode for the sample size (N)<br /><br />From here I need to calculate some summary statistics (the range of the sums, the range of the samples and their distribution), and then come up with a formula to test the effectiveness. This part may take some time...<br /><br />At a quick glance, there is an expected difference in the positions, some run higher (consistently) than others.<br /><br />Would have been easier with the pick 2, but in this test I wanted more columns to compare.... &#x5b;&#xa0;<a href="/blogentry/197541">More</a>&#xa0;&#x5d;</p>]]></description>
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