Optiver Realized Volatility Prediction

Apply your data science skills to make financial markets better

Competition webpage

Aim

The aim of this competition is to predict a patient’s severity of decline in lung function based on a CT scan of their lungs. Lung function is assessed based on output from a spirometer, which measures the forced vital capacity (FVC), i.e. the volume of air exhaled.

Evaluation metric - Root mean square percentage error (RMPSE)

Data provided -

Training data stock_id - ID code for the stock. Not all stock IDs exist in every time bucket. Parquet coerces this column to the categorical data type when loaded; you may wish to convert it to int8. time_id - ID code for the time bucket. Time IDs are not necessarily sequential but are consistent across all stocks. seconds_in_bucket - Number of seconds from the start of the bucket, always starting from 0. bid_price[1/2] - Normalized prices of the most/second most competitive buy level. ask_price[1/2] - Normalized prices of the most/second most competitive sell level. bid_size[1/2] - The number of shares on the most/second most competitive buy level. ask_size[1/2] - The number of shares on the most/second most competitive sell level.

Goal - Predict the final three FVC measurements for each patient, as well as a confidence value in your prediction.

Approach used -

LightGBM, XGboost, TabNet

Feature engineering and selection

Images