Original Blog Entry: Next evolutionary step of the follower program.

hypersoniq — Fri, 29 Dec 2023 19:17:32 GMT

As it is written, the follower refinery script constitutes an estimation function, which is another way to say it is a definitive best guess .

Since this is ultimately an attempt to manually implement a machine learning strategy, the next step will be an error function. I finally figured out how to set up the back test! This process will involve

1. Loading up the first 1,000 games of any of the pick n games into a pandas data frame that will be run through the program, this time with the pick isolated from the rest of the distribution list and written to a csv file. The remainder of the draws will be held in another data frame, and the oldest entry popped off and pushed to the bottom of the original list to be run again. With the result appended to the new csv file.

2. This continues until the entire history has been pushed to the data frame that contained the first 1,000 draws.

3. The manual error function... this new file with all of the picks will be merged with a copy of the draw history file, and the difference recorded between the result and the pick.

The first test will be to see how many times the picks hit, but the most important function will be to find the most common difference by position and use that as a mask to apply to future picks. This new mask can then be tested in place to see if it produced more hits than the original guess.

I believe this to be a fair test because it only uses data for each guess that you would have had access to at that time in history. Plus it will give a definitive answer to the win gaps.

Crazy hectic schedule for the next 3 weeks with class, but I will start coding this during the class break.

The ultimate goal would be the ability to automate the error test and generate a mask, but small steps make progress!

So to recap, the pick is the follower with the highest Markov probability (which is the most frequent) and the mask will be the error correction factor with the highest Markov probability (also the most frequent). Ultimately leading to one pick that hopefully results in a few wins.

Designing this phase as modularly as the first means that I should soon have the ability to back test ANY system that generates one pick.

Happy Coding!... [ More ]