AI Stock Price Prediction Using Large Language Models in Python

[@BitCuration]

@DeepCharts, do you have your code in github or a way to download? Would like to prototype based on this video. Nice works.

[@DeepCharts]

Thanks! Posted now!

[@RodMorrison]

Great video, love the art of the possible!

[@DeepCharts]

Thanks!

[@osmanniazi7888]

Not a bad intro at all. I am an ex Goldman Sachs Quant. I dont know how youtube got me here :-). But I think this is good for someone new to Quant finance and machine learning. Yes someone needs to think deeply about the pricing but this is a good starting point to know how to use these tools.

[@DeepCharts]

Thanks! That was the goal.

[@jonathanmitchell5171]

Not going to repeat my other comment but any other suggestions for a Business major looking to create such a tool. I dont fit in the quant nor the algo community as I just want information relevant to my trading theory, if that makes sense. Less interested in price prediction. I fear the "it works until it doesnt". Dont know what i dont know but it took months to decide what language to start learning.

[@istaruscanada6572]

Do you do one-to-one consultations?

[@NoneOfTheAboveEntParty]

@DeepCharts - I love the idea... I'm still new to python and learning how to do things. When I follow your instructions it is giving me an error "pandas.errors.MergeError: Not allowed to merge between different levels. (1 levels on the left, 2 on the right)" as if the dataframes are not aligned. any suggestions on how to resolve this?

[@DeepCharts]

It could be the yfinance library's latest update. I'd try to install the yfinance version that would be equivalent to the publish date of the video, rather than the most recent yfinance version.

[@jonathanmitchell5171]

Appreciate an AI finance video that focuses on handling/ presenting data as information rather than placing trades. I work a salary job with family and i just dont have the time to proper DD and sometimes my subscriptions go unused for a month or so. I am looking to integrate local AI into my strategy by helping make sense of web articles, reports, and analysts sentiments/ratings. Presenting this data in a manageable format in real time. I am on AMD system so Pytorch makes sense? If use this as template to learn on am I on the right track?

[@DeepCharts]

Pytorch is a great choice. What I've presented here is just an example workflow and methodology. I would test different LLM models for sentiment tasks and different methodologies for turning sentiment into quantitative measures to feed a more comprehensive model. Always make sure to validate your AI measures, as well as your quant finance models, more generally.

[@atrigupta8929]

Will you upload the code in Github.........

[@DeepCharts]

Posted now!

[@brooklyn_domino]

How are you deploying the app on Streamlit? I keep getting this error: "ConnectionError: HTTPConnectionPool(host='0.0.0.0', port=11434): Max retries exceeded with url". I've tried changing the server address from 127.0.0.1 to 0.0.0.0. I've restarted the server, changed the base_url but nothing seems to work. I'm missing some trick here. Do I need to make sure that the ollama server is running on the local system when executing the web app? Because for some reason when I run the program locally from vscode it works.

[@cheongyiksheng1396]

me too

[@Paul-Wonder-Universe]

😮

[@Charles-m7j]

Yeah so that is not how the stock market works.

[@DeepCharts]

Not sure if you watched the video or even read the description. I pretty clearly mention that there are a lot more drivers of stock prices and that this tutorial is just about how to create an LLM workflow that could be used to predict stock prices if more consideration were put into the predictors and the final time series model.

[@Charles-m7j]

@@DeepCharts Right. That is great. It is just profoundly naive to think that it is useful in any way.