As the calculation of the standard deviation is almost complete, the need becomes apparent to create a back test, but at what intervals?

There are 2 that move to the front of the pack right away...

1. Process the data in chunks so the function is fed X+Y draws so the chunks are larger but separated by X+Y or...

2. Process the data by chunking only Y draws back. This would allow the process to be back tested and provide a full output of observed HNC patterns for the entire draw history (to X+Y from the origin draw)

The second option would be a more thorough exploration data set to find common HNC classification patterns because it only deals with data at a time that you would NOT have known beforehand, no a priori knowledge, which is ideal because moving forward you would not have that information.

This would simplify the process of getting a pick by simply counting the most frequent HNC patterns. All that would need to be recorded are the Y classifications, which would match the draw history.

A few challenges to overcome...

1. Counting the remaining chunk sizes to ensure there are X+Y draws remaining to process. A simple count of the remaining data frame rows should handle this.

2. Writing the Y classifications to a CSV file, because this requires buffering the output of each column and writing complete rows after all columns have run. This is mostly solved in the output, but I have to ensure it writes the data appropriately so that it is the same date ascending order of the original data.

3. Figuring out the HNC to play, as the classifications are mostly a one to many relationship... there could, for example, be 3 Hots in a column, so which Hot to play?

4. Refactor the input arguments. I will no longer need to specify the hot & cold threshold percents, so perhaps input the expectancy so the calculated standard deviation can be added and subtracted from it to set the thresholds.

And that is just for the pick N games... followers are a future add...

Busy Hobby!