4 ms·
Show HN: Numerai – A global AI tournament to predict the stock market
- myrons 11y agocool
- francispedraza 11y agoThe future.
- dsernst 11y agoHappy to answer any questions
- henryw4k 11y agoAwesome idea!
- yunnnyunnn 11y agoNot bad
- monocle58 11y agowhat is the target group for tournament participants?
- dsernst 11y agoData scientists will probably perform best, but it's open to all
- lukedavis 11y agoCan't wait to compete!
- radiobeat33 11y agoNiiiice, I like it
- klp 11y agoCurious what your thoughts on this are: http://globalslant.com/2015/06/black-box-trading-why-they-all-blow-up/ http://globalslant.com/2015/06/black-box-trading-why-they-al... TLDR: Essentially black box trading inevitably fails because they're all trying to do the same thing. At a certain point everyone tries to liquidate, or buy, or perform same trades at the same time which can lead to a blow up. I wonder if getting around this assumes some sort of diversity in algorithms/ML approach, and if that diversity is a realistic assumption
- dsernst 11y agothanks for the link, looking fwd to reading.
- samarai 11y agoCant wait to see the fierce competition!
- arabelladelucco 11y agoPerfect timing as I'm about to start putting some time and resources into researching some good stocks and investments.
- andytwigg 11y agoWhat is the advantage of an order preserving encryption scheme over choosing an arbitrary mapping for each data column and applying that to the data?
- bobnelson 11y agoCan someone put in layman's terms the passage below? please. Assuming the reader has some machine learning experience but no financial knowledge. Thanks #### numerai_training_data.csv Use this dataset to train your machine learning algorithm. The first fourteen columns (`f1` - `f14`) are integer features. Column `c1` is a categorical feature, column validation indicates a dataset that you can use to validate your model, and target is the binary class you’re trying to predict.
- dsernst 11y agoIf you're looking at the training dataset, you'll see these 17 columns: "f1" - "f14" are feature columns, represented as integers. They are inputs for you model. "c1" is also a feature column, represented as a category. Feel free to ignore it if you're not sure how to best use it. "validation" is a binary 0 or 1 that just says: 'if you want to validate your own model, so you can test your accuracy on your own machine, you may want to use the rows that have 1's here.' "target" is the expected output. For this training data it is provided, for the tournament data you come up with it. Does that help?
- snoitavla 11y agoIt's not exactly in the spirit of data science when the features are unknown and every integer is sort of a black box. Any insights?
- snoitavla 11y agoHere's a baseline XGBooost system that scores 0.51... AUC: https://gist.github.com/alvations/b754bffe1dab515d9af7 https://gist.github.com/alvations/b754bffe1dab515d9af7