89 points by dfmdata 1 year ago flag hide 20 comments
finance_engineer 1 year ago next
Great work! I've been waiting for something like this in the financial market space. Is it possible to use this library with R?
quant_researcher 1 year ago next
I noticed the library handles time-series analysis pretty well, any plans to implement machine learning algorithms for predictive analysis?
project_creator 1 year ago next
Yes, actually! Machine learning support is coming soon, with regression algorithms and decision tree models as our first priority. Expect to see this in a few months.
dev_james 1 year ago prev next
R support isn't there yet, but it's on the roadmap. It's written in Python and should work with tools like RPython.
bot_investment 1 year ago prev next
Any plans for implementing a disaster recovery module for data analysis in case of major market disturbances or events?
project_creator 1 year ago next
Yes, we want this library to be robust enough for all scenarios, so we're keeping disaster recovery a priority. It will contain fail-safes for when unexpected market events occur.
market_analyst 1 year ago prev next
This looks great, looking forward to using the ML functionalities. When do you think you will have a detailed roadmap available?
project_creator 1 year ago next
Thanks, Analyst! We hope to have a roadmap ready by our next update. If you register for notifications, we'll keep you in the loop.
some_dev 1 year ago prev next
I noticed the library relies on Pandas, does that mean we can use parallel processing directly?
dev_james 1 year ago next
Yes, we designed it to take full advantage of Pandas, and that includes its parallel processing capabilities.
financial_advisor 1 year ago prev next
Interesting project. I think it's a good step towards democratizing financial market data. Keep up the great work!
blockchain_enthusiast 1 year ago prev next
I'm curious to know if you considered blockchain technology to keep records secure?
project_creator 1 year ago next
We have thought about blockchain technology, and we generally agree that it has promising elements for security and transparency. It's a bit early to include in this library, but we'll be watching its developments closely.
code_reviewer 1 year ago prev next
How does this library handle memory usage with large datasets?
dev_james 1 year ago next
Efficient memory management is essential for large datasets, and that's why we chose to use Pandas. It takes care of memory efficiently with its data structures, like Series, DataFrame, and Panel.
datascience_intern 1 year ago prev next
What kind of documentation does this library have? Is it sufficient for someone with moderate programming experience? Thanks!
project_creator 1 year ago prev next
We've designed our documentation to be comprehensive and easy to follow. We aimed to ensure that those with moderate programming experience could navigate it comfortably. Of course, we welcome feedback that could help improve it!
algorithm_expert 1 year ago prev next
What kind of algorithms can I expect in the machine learning module?
project_creator 1 year ago next
Our first version will include core regression algorithms and basic decision tree approaches. As the library evolves, so will our machine learning functionality, likely expanding to clustering and ensemble models.
head_quant 1 year ago prev next
Very intrigued. I hope to see real-life applications of this library soon! Best wishes for the project.