Data

Each row of the dataset describes an investment vehicle at a certain date.

Here follows a concise description of the columns of the files:

X_train:

date: A sequentially increasing integer representing a date. Time between subsequent dates is a constant, denoting an unknown but fixed frequency at which the data is sampled. The initial training dataset is composed of 268 dates.
id: A unique identifier representing the investment vehicle at a given date. Note that the same asset has a different id at each date.
feat_1, ..., feat_n: Anonymized features describing an investment vehicle at a given date.

X_test:

Same structure as X_train but comprises only a few dates. This file is used to simulate the submission process locally via crunch.test(), or crunch test (if using the CLI). A successful local test usually means no errors during execution on the submission platform.

The dataset is obfuscated.

y_train.parquet

Last updated 1 year ago

Each row of the dataset describes an investment vehicle at a certain date.

Here follows a concise description of the columns of the files:

X_train:

date: A sequentially increasing integer representing a date. Time between subsequent dates is a constant, denoting an unknown but fixed frequency at which the data is sampled. The initial training dataset is composed of 268 dates.
id: A unique identifier representing the investment vehicle at a given date. Note that the same asset has a different id at each date.
feat_1, ..., feat_n: Anonymized features describing an investment vehicle at a given date.

X_test:

Same structure as X_train but comprises only a few dates. This file is used to simulate the submission process locally via crunch.test(), or crunch test (if using the CLI). A successful local test usually means no errors during execution on the submission platform.

The dataset is obfuscated.

y_train.parquet