u/jjustineee

currently creating a baccarat prediction system (yes I know it's impossible) but I'm doing it for the heck of it and because it's hard, profiting from it would be a side bonus, only did it to make daddy Nietzsche proud by attempting the great and the impossible.

is there any actual good github repos that has prediction systems I can take a look on? one that applies quant trading (stochastic markov chain and whatnot) incremental training, randomforest, xgboost, monte carlo simulators and so on that y'all think is worth taking a look? .

for the boring part:

what I did!!!

initially I wanted to predict something, coin toss is....actually impossible, dice rolls are impossible so next on the list is cards, but I needed to attach a theme onto it and how it behaves rather than pulling cards from it one by one and I was introduced with Baccarat since there is a specific ruleset and you only have to predict left or right, red or blue.

what I did was that I attached 16 currently existing prediction system each have their own rules

"always bet P B P B"

"always bet P P B B"

"always bet on the recent winner"

"always bet on the...."

theres so many and some aren't as basic as the first two...I gott hem all from youtube and observation (watching them on twitch)

now they are indicators, what's next is that I made a machine learning model that detects when they were right and wrong, detecting their behavior and pattern, when were they correct, and when they were wrong, since basically baccarat is at the mercy of the shuffle of the shoe (8 decks per shoe) and then I made a monte carlo simulator that has those 16 prediction system betting on it so that I can simulate the game rather than watch it on twitch for lengthy amounts of time.

i made three apps, monte carlo simulator, the ml trainer, and the baccarat app that can import the ml model and provide it's predictions

the ml trainer provides two models, the gatekeeper and the primary, gatekeeper says when it is confident to bet, while primary is the one that says P or B

currently the loop is that I create data from a monte carlo simulator, then import it to create a model in the trainer, import it back to monte carlo simulator to play and lose and learn from its mistakes and so on and so forth, then back to trainer.

I use entropy targeting to measure the randomness in the data, feature locking for data that doesn't contribute to anything, and l1 and l2. it also has gradient descent, sigmoid scaling, and markov chain.

so currently the question would be am I doing the stuff correctly or am I executing it correctly which is why I am deep diving into github repos to check actual works since I've only been doing this on my spare time so around two weeks worth with 5 hours a day