Building Hedge Fund Prediction Models for Numerai Datasets Using Machine Learning and Deep Learning - Step-By-Step Example

In February, Numerai announced Numeraire, a cryptographic token to incentivize data scientists around the world to contribute artificial intelligence to our hedge fund (see Forbes, Wired, Smith+Crown). Earlier today, the Numeraire smart contract was deployed to Ethereum, and over 1.2 million tokens were sent to 19,000 data scientists around the world: An AI Hedge Fund Goes Live On Ethereum

Numerai abstracts its financial data, data scientists do not know what the data represents and human biases and overfitting are overcome.

Steps

Irrespective of what dataset you download, the following steps will almost be same with a few modifications:

1 . First initialize a git repo and put the downloaded code there.

$ mkdir numerai_code #create a repo
$ cd numerai_code 
$ git init # initialize git
$ git add numerai_code_downloaded # Add downloaded data to git repo
$ git commit -m 'Numerai code added'
$ git push

2 . Install and Initialize DVC repository

$ pip install dvc
$ dvc init

3 . ‘numerai_training_data.csv’ and ‘numerai_tournament_data.csv’ will be present in the dataset downloaded which can be used to train and predict the results respectively.

4 . Now, the time is to create a prediction model that predicts the data based on the dataset available and then put that file in the same repository (numerai_code) as above. Let’s call it ‘prediction.py’
For the prediction model, I used an LSTM (Long Short Term Memory) Recurrent Neural Networks in Python with Keras.

5 . Run the python code within dvc with the following command:

$ dvc run python prediction.py

6 . The model saves the checkpoints in a CSV file with the name assigned and the saved CSV can be submitted on the Numerai submission page.

Full article: Step by Step guide to Version Control your Machine Learning and Deep Learning tasks — II