Nice demo! I am trying it out but can't figure out syntax error I get on # Display main metrics st.metric(label=f"{ticker} Last Price", value=f"{last_close:.2f} USD", delta f"{change:.2f} ({pct_change:.2f}%)") Script execution error File "/mnt/c/Users/data1/utm/markets/yf_dashboard.py", line 92 st.metric(label=f"{ticker} Last Price", value=f"{last_close:.2f} USD", delta f"{change:.2f} ({pct_change:.2f}%)") ^ SyntaxError: positional argument follows keyword argument
Excellent video!! Just a question, I see that the hexagons in which we have recorded measurements are obtained. If I wanted to see all the hexagons of a metropolitan city like NY, is there a way to see the complete hexagonization of the city?
To the point video, keep the good work. Request you to suggest how to read stocks data from a web socket and use in creating live chart from that live fed in Streamlit.
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.
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?
Coordinates: yes, as long as you have measures of air quality for specific coordinates. Population: no. Population density is often correlated with air quality levels, but kriging doesn't use auxiliary data.
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.
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.
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.
