Numer.ai is a crowd sourced hedge fund that uses the individual classifiers of its users to predict future asset prices.
They take propriety real world financial data and encrypt it with homomorphic encryption. Homomorphic encryption is a mapping method that makes it possible, from analysis performed on the encrypted data, to derive conclusions on the original data. They split the data into a training data set and a test data set.
The training data's shape is
(96320, 22) with $21$ normalised features columns, no index and a binary label target. The test data's shape is
(36072, 21) with $21$ normalised features and unsorted index
The idea is to develop a classifier based on the training data and then to submit your prediction for the test set. If your classification performs well on the test set and your algorithm is original (diversifying), they pay you in Bitcoins. Unlike Kaggle you do not upload your code.
Throwing a couple of standard algorithms at the problem I came up with a log loss of $ \approx 0.6930$ which doesn't get you anywhere close to the top ranks.
My question is: How are they making money out of this?
If encrypted data is based on historical stock prices, financial fundamental data or any other real world time series data (which I assume you will need for any kind of quantitative trading), how can you predict future asset prices without having access to my classifier? Can you infer it from my classification of the training data set or the test data set (closest distance)? That seems unlikely given the dimensionality of the data set. You would have to look at the most recent data points and put them into the classifier. I assume this is not a Ponzi scheme because they have sophisticated investors with a quantitative trading background.