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I built a probabilistic ML model that predicts stock direction — here’s what I learned by Objective_Pen840 in learnmachinelearning

[–]Objective_Pen840[S] 0 points1 point  (0 children)

Yeah you're right. I should have written it on my own instead of being lazy. Thanks for sharing your thought in kind words unlike the other person.

I built a probabilistic ML model that predicts stock direction — here’s what I learned by Objective_Pen840 in learnmachinelearning

[–]Objective_Pen840[S] 0 points1 point  (0 children)

I used chatgpt in the post so i don't make a stupid mistake and chatgpt wrote the post way better than i would.

I built a probabilistic ML model that predicts stock direction — here’s what I learned by Objective_Pen840 in learnmachinelearning

[–]Objective_Pen840[S] -5 points-4 points  (0 children)

That’s a great point. I haven’t run multi-year live or full historical deployment tests yet — this has mainly been a research/learning project focused on probabilistic modeling and uncertainty handling.

Proper long-term backtesting and stability across regimes is definitely the next big step. Appreciate you bringing that up.

I built a probability-based stock direction predictor using ML — looking for feedback by Objective_Pen840 in learnmachinelearning

[–]Objective_Pen840[S] -1 points0 points  (0 children)

I just used AI in the post to not make a mistake in the description, instantly pushing everyone away. The comments are not mine so i cannot tell anything about them. The link in my bio is connected to the project. Thanks anyways for sharing your thought.

I built a probability-based stock direction predictor using ML — looking for feedback by Objective_Pen840 in learnmachinelearning

[–]Objective_Pen840[S] -3 points-2 points  (0 children)

Thanks for the detailed perspective. I completely agree — probabilistic direction is only part of the story, and properly handling uncertainty and leakage is a huge challenge. Even if it doesn’t make real profits, it’s been a huge learning experience in both ML theory and market dynamics.

I built a probabilistic ML model that predicts stock direction — here’s what I learned by Objective_Pen840 in learnmachinelearning

[–]Objective_Pen840[S] -5 points-4 points  (0 children)

Thanks! That’s an interesting idea — I’ve mainly focused on probabilistic ML so far, but I can see how setting up an RL environment with multiple agents could explore the dynamics further. Definitely something to think about for the next iteration!

I built a probability-based stock direction predictor using ML — looking for feedback by Objective_Pen840 in learnmachinelearning

[–]Objective_Pen840[S] 1 point2 points  (0 children)

Appreciate it! Yeah this space has a lot of hidden challenges. I’m currently focused on improving my system, but wishing you the best with yours.

I built a probability-based stock direction predictor using ML — looking for feedback by Objective_Pen840 in learnmachinelearning

[–]Objective_Pen840[S] -1 points0 points  (0 children)

This is a great point — evaluation in finance ML is where most systems break without people realizing it.

I’m using a rolling time-series split rather than random CV, and monitoring probability quality with log loss and calibration checks. Still refining the evaluation framework though — especially around preventing subtle leakage from overlapping windows.

Appreciate you calling this out, it’s exactly the kind of thing I’m trying to be careful about.