Comments (4)
Any other suggestions are welcomed.
Btw, here are some suggestions from @firmai
remove projects that is not that good (as time is the judge) add many new ones, create new categories, i.e. synthetic data generation, adversarial machine learning, add paper sections etc.
from financial-machine-learning.
- I like the roadmap @bin-yang-algotune, I have a view ideas for later down the line: a lot of students come to me for advice on which programs to join, we can perhaps also add links to reputable financial engineering rating-and-review websites, and highlight those with core machine learning models.
- It would be good if we could use github actions to pull in the most spoke about (using twitter etc) research papers on arxiv and ssrn, i.e. a subsection acting like an academic version of https://quantocracy.com/,
- Nobody has yet done this, but finance needs benchmark datasets, it would be good to list all the public or near publicly available datasets (below $1k) that could be used in financial machine learning.
- If anyone else has some ideas, feel free to throw them on here.
from financial-machine-learning.
- I like the roadmap @bin-yang-algotune, I have a view ideas for later down the line: a lot of students come to me for advice on which programs to join, we can perhaps also add links to reputable financial engineering rating-and-review websites, and highlight those with core machine learning models.
sounds good, i know quantnet has a annual ranking of programs, need to go through to get which ones are more "machine learning" focus
https://quantnet.com/mfe-programs-rankings/
- It would be good if we could use github actions to pull in the most spoke about (using twitter etc) research papers on arxiv and ssrn, i.e. a subsection acting like an academic version of https://quantocracy.com/,
Yes, that sounds great, issue created here and it will be awesome to review/tag the papers as well. Hopefully we can get some volunteers for this
- Nobody has yet done this, but finance needs benchmark datasets, it would be good to list all the public or near publicly available datasets (below $1k) that could be used in financial machine learning.
I was thinking about the exact same thing, since I have been exploring different data sources for years, we should break it down to different section as well based on asset class and instrument types: i.e. equities/fi/commodities/FX vs single name equities/options/futures/other exotic instruments. Let me think about how to organize our pages and propose something
from financial-machine-learning.
thinking of process of something like this.
from financial-machine-learning.
Related Issues (14)
- Create wiki page for each subsection and add more details
- Deep Learning wiki page, review content etc.
- add last commit date since last update date doesn't accurately reflect the correct status of the project
- Use github api to search for relevant repo
- Other models review content
- Investigate colab notebook server HOT 1
- Make it "watch friendly" HOT 3
- Stock prediction
- ML Finance
- Improve the prediction performance by integrating technical indicators
- Create github actions to pull in research paper from arxiv and ssrn
- Create github action to automatically populate the creation date etc.
- Create a gitter chat for the repo
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