RequestHunt is a community-driven platform that collects and curates feature requests from X (Twitter), Reddit, and GitHub. We help product teams discover what features real users are asking for, making it easier to validate product ideas and prioritize feature development based on actual user demand.

How does RequestHunt collect feature requests?

RequestHunt uses AI-powered extraction to automatically collect feature requests from social media platforms including X, Reddit, and GitHub. The platform monitors discussions, issues, and posts to identify genuine user feature requests, extracting key information like the requested feature, product mentioned, and user sentiment.

Who uses RequestHunt?

RequestHunt is used by product managers, founders, developers, and product teams who want to discover what features real users are requesting. It's particularly valuable for startups looking to validate ideas, established companies prioritizing their roadmap, and developers building products that solve real user problems.

Is RequestHunt free to use?

Yes, browsing and searching feature requests on RequestHunt is completely free. We also offer API access for developers who want to integrate feature request data into their own applications. Check our pricing page for API plan details.

How often is RequestHunt data updated?

RequestHunt continuously collects feature requests from Reddit, X (Twitter), and GitHub. You can also trigger on-demand scraping via the API or web interface to get the freshest data for any topic.

[mlfinlab] Implement PBO through the combinatorially symmetric cross-validation (CSCV) method

Implement the probability of backtesting (PBO) estimation method through the combinatorially symmetric cross-validation (CSCV) method, as described in Advances in Financial Machine Learning Chapter 11.

Original Source

@@huaiweicheng | 0 pts | 6/11/2020

**Is your feature request related to a problem? Please describe.** **Describe the solution you'd like** In Advances in Financial Machine Learning Chapter 11 The dangers of backtesting provides us a method to estimate the probability of backtesting. I think it is a very useful method to evaluate the performance of strategies. However, I do not find it implemented in mlfinlab. **Describe alternatives you've considered** **Additional context**

Discussion

Loading comments...