List of todos for revamping financial-machine-learning project

[bin-yang-algotune]

Here are several improvements I have in my mind, feel free to raise PR and contribute.

- [ ] 1. Make READ.md a web page, make links etc easier to track similar to https://pages.github.com/, this might require a new repo name, so it's not worth the effort, may consider using wiki instead

• 2. Create a data store within the project = data file (csv/pickle) would suffice
• 3. Create python/github api to query tracked project status and retire projects like @firmai suggested
• 4. Somehow allow other users to comment and suggest new inclusions easier (not sure how yet)
• 5. Organize or add extra sections:
  • a. Backtester engines
  • b. Other alternative asset types: crypto on top of my mind, but open to ideas
  • c. Academic scans (might need help with these)
  • d. Quant finance/ml blogs, for example, https://quantocracy.com/

[bin-yang-algotune]

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.

[firmai]

1. 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.
2. 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/,
3. 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.
4. If anyone else has some ideas, feel free to throw them on here.

[bin-yang-algotune]

1. 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/

2. 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

3. 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

[bin-yang-algotune]

thinking of process of something like this.